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Singh S, Kaur N, Gehlot A. Application of artificial intelligence in drug design: A review. Comput Biol Med 2024; 179:108810. [PMID: 38991316 DOI: 10.1016/j.compbiomed.2024.108810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.
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Affiliation(s)
- Simrandeep Singh
- Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India.
| | - Navjot Kaur
- Department of Pharmacognosy, Amar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, Ropar, India
| | - Anita Gehlot
- Uttaranchal Institute of technology, Uttaranchal University, Dehradun, India
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2
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Karampuri A, Kundur S, Perugu S. Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling. Comput Biol Med 2024; 174:108433. [PMID: 38642491 DOI: 10.1016/j.compbiomed.2024.108433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/22/2024]
Abstract
Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer. To accomplish this, we have developed a multimodal deep neural network (MM-DNN) based QSAR model integrating omics datasets to elucidate genomic, proteomic expression data, and drug responses, validated rigorously. The results showcase an R2 value of 0.917 and an RMSE value of 0.312, affirming the model's commendable predictive capabilities. Structural analogs of drug molecules specific to RTK signalling were sourced from the PubChem database, followed by meticulous screening to eliminate dissimilar compounds. Leveraging the MM-DNN-based QSAR model, we predicted the biological activity of these molecules, subsequently clustering them into three distinct groups. Feature importance analysis was performed. Consequently, we successfully identified prime drug candidates tailored for each potential downstream regulatory protein within the RTK signalling pathway. This method makes the early stages of drug development faster by removing inactive compounds, providing a hopeful path in combating breast cancer.
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Affiliation(s)
- Anush Karampuri
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India
| | - Sunitha Kundur
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India
| | - Shyam Perugu
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India.
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3
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Hong Y, Xu H, Liu Y, Zhu S, Tian C, Chen G, Zhu F, Tao L. DDID: a comprehensive resource for visualization and analysis of diet-drug interactions. Brief Bioinform 2024; 25:bbae212. [PMID: 38711369 DOI: 10.1093/bib/bbae212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/01/2024] [Accepted: 04/21/2024] [Indexed: 05/08/2024] Open
Abstract
Diet-drug interactions (DDIs) are pivotal in drug discovery and pharmacovigilance. DDIs can modify the systemic bioavailability/pharmacokinetics of drugs, posing a threat to public health and patient safety. Therefore, it is crucial to establish a platform to reveal the correlation between diets and drugs. Accordingly, we have established a publicly accessible online platform, known as Diet-Drug Interactions Database (DDID, https://bddg.hznu.edu.cn/ddid/), to systematically detail the correlation and corresponding mechanisms of DDIs. The platform comprises 1338 foods/herbs, encompassing flora and fauna, alongside 1516 widely used drugs and 23 950 interaction records. All interactions are meticulously scrutinized and segmented into five categories, thereby resulting in evaluations (positive, negative, no effect, harmful and possible). Besides, cross-linkages between foods/herbs, drugs and other databases are furnished. In conclusion, DDID is a useful resource for comprehending the correlation between foods, herbs and drugs and holds a promise to enhance drug utilization and research on drug combinations.
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Affiliation(s)
- Yanfeng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Hongquan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuhong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Sisi Zhu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Chao Tian
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Gongxing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Affiliated Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
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Zhou Y, Chen Z, Yang M, Chen F, Yin J, Zhang Y, Zhou X, Sun X, Ni Z, Chen L, Lv Q, Zhu F, Liu S. FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response. Brief Bioinform 2024; 25:bbae223. [PMID: 38742521 PMCID: PMC11091744 DOI: 10.1093/bib/bbae223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/25/2024] [Accepted: 04/21/2024] [Indexed: 05/16/2024] Open
Abstract
Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.
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Affiliation(s)
- Yuan Zhou
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Mengjie Yang
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Fengyun Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Jiayi Yin
- Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xuheng Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Ziheng Ni
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Lu Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Qun Lv
- Department of Respiratory, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 311121, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Shuiping Liu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
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5
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Chen B, Pan Z, Mou M, Zhou Y, Fu W. Is fragment-based graph a better graph-based molecular representation for drug design? A comparison study of graph-based models. Comput Biol Med 2024; 169:107811. [PMID: 38168647 DOI: 10.1016/j.compbiomed.2023.107811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/23/2023] [Accepted: 12/03/2023] [Indexed: 01/05/2024]
Abstract
Graph Neural Networks (GNNs) have gained significant traction in various sectors of AI-driven drug design. Over recent years, the integration of fragmentation concepts into GNNs has emerged as a potent strategy to augment the efficacy of molecular generative models. Nonetheless, challenges such as symmetry breaking and potential misrepresentation of intricate cycles and undefined functional groups raise questions about the superiority of fragment-based graph representation over traditional methods. In our research, we undertook a rigorous evaluation, contrasting the predictive prowess of eight models-developed using deep learning algorithms-across 12 benchmark datasets that span a range of properties. These models encompass established methods like GCN, AttentiveFP, and D-MPNN, as well as innovative fragment-based representation techniques. Our results indicate that fragment-based methodologies, notably PharmHGT, significantly improve model performance and interpretability, particularly in scenarios characterized by limited data availability. However, in situations with extensive training, fragment-based molecular graph representations may not necessarily eclipse traditional methods. In summation, we posit that the integration of fragmentation, as an avant-garde technique in drug design, harbors considerable promise for the future of AI-enhanced drug design.
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Affiliation(s)
- Baiyu Chen
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 202103, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Wei Fu
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 202103, China.
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6
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Yin J, Chen Z, You N, Li F, Zhang H, Xue J, Ma H, Zhao Q, Yu L, Zeng S, Zhu F. VARIDT 3.0: the phenotypic and regulatory variability of drug transporter. Nucleic Acids Res 2024; 52:D1490-D1502. [PMID: 37819041 PMCID: PMC10767864 DOI: 10.1093/nar/gkad818] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/01/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.
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Affiliation(s)
- Jiayi Yin
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Nanxin You
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- The Children's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310052, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Jia Xue
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Hui Ma
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Qingwei Zhao
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Lushan Yu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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7
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Zhang Y, Zhou Y, Zhou Y, Yu X, Shen X, Hong Y, Zhang Y, Wang S, Mou M, Zhang J, Tao L, Gao J, Qiu Y, Chen Y, Zhu F. TheMarker: a comprehensive database of therapeutic biomarkers. Nucleic Acids Res 2024; 52:D1450-D1464. [PMID: 37850638 PMCID: PMC10767989 DOI: 10.1093/nar/gkad862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023] Open
Abstract
Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.
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Affiliation(s)
- Yintao Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yanfeng Hong
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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8
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Shen L, Sun X, Chen Z, Guo Y, Shen Z, Song Y, Xin W, Ding H, Ma X, Xu W, Zhou W, Che J, Tan L, Chen L, Chen S, Dong X, Fang L, Zhu F. ADCdb: the database of antibody-drug conjugates. Nucleic Acids Res 2024; 52:D1097-D1109. [PMID: 37831118 PMCID: PMC10768060 DOI: 10.1093/nar/gkad831] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/07/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.
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Affiliation(s)
- Liteng Shen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yu Guo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zheyuan Shen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yi Song
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wenxiu Xin
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
| | - Haiying Ding
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
| | - Xinyue Ma
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Weiben Xu
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Wanying Zhou
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Jinxin Che
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lili Tan
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Liangsheng Chen
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Siqi Chen
- School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Xiaowu Dong
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Luo Fang
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
- School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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9
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Fu T, Zeng S, Zheng Q, Zhu F. The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity. Drug Metab Dispos 2023; 51:1316-1323. [PMID: 37295948 DOI: 10.1124/dmd.123.001275] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/27/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023] Open
Abstract
The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy, and toxicity as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biologic membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiologic functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms. The data collected contributes toward safer and more effective pharmacological treatments. SIGNIFICANCE STATEMENT: The experimental structure of ATP-binding cassette and solute carrier transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms.
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Affiliation(s)
- Tingting Fu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China (F.Z.); School of Pharmaceutical Sciences, Jilin University, Changchun, China (T.F., Q.Z.); College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (S.Z., F.Z.); and Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China (F.Z.)
| | - Su Zeng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China (F.Z.); School of Pharmaceutical Sciences, Jilin University, Changchun, China (T.F., Q.Z.); College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (S.Z., F.Z.); and Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China (F.Z.)
| | - Qingchuan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China (F.Z.); School of Pharmaceutical Sciences, Jilin University, Changchun, China (T.F., Q.Z.); College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (S.Z., F.Z.); and Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China (F.Z.)
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China (F.Z.); School of Pharmaceutical Sciences, Jilin University, Changchun, China (T.F., Q.Z.); College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (S.Z., F.Z.); and Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China (F.Z.)
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10
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Boussios S, Devo P, Goodall ICA, Sirlantzis K, Ghose A, Shinde SD, Papadopoulos V, Sanchez E, Rassy E, Ovsepian SV. Exosomes in the Diagnosis and Treatment of Renal Cell Cancer. Int J Mol Sci 2023; 24:14356. [PMID: 37762660 PMCID: PMC10531522 DOI: 10.3390/ijms241814356] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/15/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
Renal cell carcinoma (RCC) is the most prevalent type of kidney cancer originating from renal tubular epithelial cells, with clear cell RCC comprising approximately 80% of cases. The primary treatment modalities for RCC are surgery and targeted therapy, albeit with suboptimal efficacies. Despite progress in RCC research, significant challenges persist, including advanced distant metastasis, delayed diagnosis, and drug resistance. Growing evidence suggests that extracellular vesicles (EVs) play a pivotal role in multiple aspects of RCC, including tumorigenesis, metastasis, immune evasion, and drug response. These membrane-bound vesicles are released into the extracellular environment by nearly all cell types and are capable of transferring various bioactive molecules, including RNA, DNA, proteins, and lipids, aiding intercellular communication. The molecular cargo carried by EVs renders them an attractive resource for biomarker identification, while their multifarious role in the RCC offers opportunities for diagnosis and targeted interventions, including EV-based therapies. As the most versatile type of EVs, exosomes have attracted much attention as nanocarriers of biologicals, with multi-range signaling effects. Despite the growing interest in exosomes, there is currently no widely accepted consensus on their subtypes and properties. The emerging heterogeneity of exosomes presents both methodological challenges and exciting opportunities for diagnostic and clinical interventions. This article reviews the characteristics and functions of exosomes, with a particular reference to the recent advances in their application to the diagnosis and treatment of RCC.
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Affiliation(s)
- Stergios Boussios
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.G.); (E.S.)
- Faculty of Life Sciences & Medicine, School of Cancer & Pharmaceutical Sciences, King’s College London, Strand, London WC2R 2LS, UK
- Kent Medway Medical School, University of Kent, Canterbury CT2 7LX, UK
- AELIA Organization, 9th Km Thessaloniki–Thermi, 57001 Thessaloniki, Greece
| | - Perry Devo
- School of Sciences, Faculty of Engineering and Science, University of Greenwich, Chatham Maritime ME4 4TB, UK; (P.D.); (I.C.A.G.); (S.V.O.)
| | - Iain C. A. Goodall
- School of Sciences, Faculty of Engineering and Science, University of Greenwich, Chatham Maritime ME4 4TB, UK; (P.D.); (I.C.A.G.); (S.V.O.)
| | - Konstantinos Sirlantzis
- School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK;
| | - Aruni Ghose
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.G.); (E.S.)
- Barts Cancer Centre, Barts Health NHS Trust, London EC1A 7BE, UK
- Mount Vernon Cancer Centre, East and North Hertfordshire NHS Trust, Northwood HA6 2RN, UK
- Immuno-Oncology Clinical Network, London, UK
| | - Sayali D. Shinde
- Centre for Tumour Biology, Barts Cancer Institute, Cancer Research UK Barts Centre, Queen Mary University of London, London EC1M 6BQ, UK;
| | | | - Elisabet Sanchez
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.G.); (E.S.)
| | - Elie Rassy
- Department of Medical Oncology, Gustave Roussy Institut, 94805 Villejuif, France;
| | - Saak V. Ovsepian
- School of Sciences, Faculty of Engineering and Science, University of Greenwich, Chatham Maritime ME4 4TB, UK; (P.D.); (I.C.A.G.); (S.V.O.)
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11
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Soylu NN, Sefer E. BERT2OME: Prediction of 2'-O-Methylation Modifications From RNA Sequence by Transformer Architecture Based on BERT. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2177-2189. [PMID: 37819796 DOI: 10.1109/tcbb.2023.3237769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Recent work on language models has resulted in state-of-the-art performance on various language tasks. Among these, Bidirectional Encoder Representations from Transformers (BERT) has focused on contextualizing word embeddings to extract context and semantics of the words. On the other hand, post-transcriptional 2'-O-methylation (Nm) RNA modification is important in various cellular tasks and related to a number of diseases. The existing high-throughput experimental techniques take longer time to detect these modifications, and costly in exploring these functional processes. Here, to deeply understand the associated biological processes faster, we come up with an efficient method Bert2Ome to infer 2'-O-methylation RNA modification sites from RNA sequences. Bert2Ome combines BERT-based model with convolutional neural networks (CNN) to infer the relationship between the modification sites and RNA sequence content. Unlike the methods proposed so far, Bert2Ome assumes each given RNA sequence as a text and focuses on improving the modification prediction performance by integrating the pretrained deep learning-based language model BERT. Additionally, our transformer-based approach could infer modification sites across multiple species. According to 5-fold cross-validation, human and mouse accuracies were 99.15% and 94.35% respectively. Similarly, ROC AUC scores were 0.99, 0.94 for the same species. Detailed results show that Bert2Ome reduces the time consumed in biological experiments and outperforms the existing approaches across different datasets and species over multiple metrics. Additionally, deep learning approaches such as 2D CNNs are more promising in learning BERT attributes than more conventional machine learning methods.
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12
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Zhao S, Wang P, Heidari AA, Zhao X, Chen H. Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19. EXPERT SYSTEMS WITH APPLICATIONS 2023; 213:119095. [PMID: 36313263 PMCID: PMC9595503 DOI: 10.1016/j.eswa.2022.119095] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/11/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.
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Affiliation(s)
- Songwei Zhao
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Pengjun Wang
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China
| | - Ali Asghar Heidari
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Xuehua Zhao
- School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China
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13
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He H, Duo H, Hao Y, Zhang X, Zhou X, Zeng Y, Li Y, Li B. Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications. Comput Biol Med 2023; 155:106671. [PMID: 36805225 DOI: 10.1016/j.compbiomed.2023.106671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.
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Affiliation(s)
- Hao He
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yujie Zeng
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yinghong Li
- The Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
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14
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Yan TC, Yue ZX, Xu HQ, Liu YH, Hong YF, Chen GX, Tao L, Xie T. A systematic review of state-of-the-art strategies for machine learning-based protein function prediction. Comput Biol Med 2023; 154:106446. [PMID: 36680931 DOI: 10.1016/j.compbiomed.2022.106446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/07/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.
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Affiliation(s)
- Tian-Ci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zi-Xuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Hong-Quan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yu-Hong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yan-Feng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Gong-Xing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
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15
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Fu J, Yang Q, Luo Y, Zhang S, Tang J, Zhang Y, Zhang H, Xu H, Zhu F. Label-free proteome quantification and evaluation. Brief Bioinform 2023; 24:6833644. [PMID: 36403090 DOI: 10.1093/bib/bbac477] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/24/2022] [Accepted: 10/08/2022] [Indexed: 11/21/2022] Open
Abstract
The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hanxiang Xu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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16
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Han Y, Chen W, Heidari AA, Chen H. Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images. JOURNAL OF BIONIC ENGINEERING 2023; 20:1198-1262. [PMID: 36619872 PMCID: PMC9811903 DOI: 10.1007/s42235-022-00295-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 06/17/2023]
Abstract
Coronavirus Disease 2019 (COVID-19) is the most severe epidemic that is prevalent all over the world. How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic. Moreover, it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images. As we all know, image segmentation is a critical stage in image processing and analysis. To achieve better image segmentation results, this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO. Then utilizes RDMVO to calculate the maximum Kapur's entropy for multilevel threshold image segmentation. This image segmentation scheme is called RDMVO-MIS. We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS. First, RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions. Second, the image segmentation experiment was carried out using RDMVO-MIS, and some meta-heuristic algorithms were selected as comparisons. The test image dataset includes Berkeley images and COVID-19 Chest X-ray images. The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.
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Affiliation(s)
- Yan Han
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Weibin Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
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17
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Cheemanapalli S, Palaniappan C, Mahesh Y, Iyyappan Y, Yarrappagaari S, Kanagaraj S. In vitro and in silico perspectives to explain anticancer activity of a novel syringic acid analog ((4-(1H-1, 3-benzodiazol-2-yl)-2, 6-dimethoxy phenol)) through apoptosis activation and NFkB inhibition in K562 leukemia cells. Comput Biol Med 2023; 152:106349. [PMID: 36470147 DOI: 10.1016/j.compbiomed.2022.106349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 11/13/2022] [Accepted: 11/22/2022] [Indexed: 11/24/2022]
Abstract
Syringic acid (SA) is an active carcinogenesis inhibitor; however, the low bioavailability and unstable functional groups hinder its activity. Here, a chemically synthesized novel SA analog (SA10) is evaluated for its anticancer activity using in-vitro and in-silico studies. K562 cell line study revealed that SA10 had shown a higher rate of inhibition (IC50 = 50.40 μg/mL) than its parental compound, SA (IC50 = 96.92 μg/mL), at 50 μM concentration. The inhibition ratio was also been evaluated by checking the expression level of NFkB and Bcl-2 and showing that SA10 has two-fold increase in the inhibitory mechanism than SA. This result demonstrates that SA10 acts as an NFkB inhibitor and an apoptosis inducer. Further, molecular docking and simulation have been performed to get insights into the possible inhibitory mechanism of SA and SA10 on NFkB at the atomistic level. The molecular docking results exemplify that both SA and SA10 bind to the active site of NFkB, thereby interfering with the association between DNA and NFkB. SA10 exhibits a more robust binding affinity than SA and is firmly docked well into the interior of the NFkB, as confirmed by MM-PBSA calculations. In a nutshell, the Benzimidazole scaffold containing SA10 has shown more NFkB inhibitory activity in K562 cells than SA, which could be helpful as an ideal therapeutic NFkB inhibitor for treating cancers.
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Affiliation(s)
- Srinivasulu Cheemanapalli
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India; Regional Ayurveda Research Institute (CCRAS, Govt. of India), Itanagar, Arunachal Pradesh, India
| | - Chandrasekaran Palaniappan
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India; Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Yeshwanth Mahesh
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
| | - Yuvaraj Iyyappan
- National Institute for Plant Biotechnology, ICAR, New Delhi, India
| | - Suresh Yarrappagaari
- Division of Ethnopharmacology, Department of Biotechnology, School of Herbal Studies and Natural Sciences, Dravidian University, Kuppam, Andhra Pradesh, India
| | - Sekar Kanagaraj
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India.
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18
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Yue ZX, Yan TC, Xu HQ, Liu YH, Hong YF, Chen GX, Xie T, Tao L. A systematic review on the state-of-the-art strategies for protein representation. Comput Biol Med 2023; 152:106440. [PMID: 36543002 DOI: 10.1016/j.compbiomed.2022.106440] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/08/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.
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Affiliation(s)
- Zi-Xuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Tian-Ci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Hong-Quan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yu-Hong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yan-Feng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Gong-Xing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
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19
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Li J, Liu K, Hu Y, Zhang H, Heidari AA, Chen H, Zhang W, Algarni AD, Elmannai H. Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet+. Comput Biol Med 2022; 158:106501. [PMID: 36635120 DOI: 10.1016/j.compbiomed.2022.106501] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 01/11/2023]
Abstract
Computerized tomography (CT) is of great significance for the localization and diagnosis of liver cancer. Many scholars have recently applied deep learning methods to segment CT images of liver and liver tumors. Unlike natural images, medical image segmentation is usually more challenging due to its nature. Aiming at the problem of blurry boundaries and complex gradients of liver tumor images, a deep supervision network based on the combination of high-efficiency channel attention and Res-UNet++ (ECA residual UNet++) is proposed for liver CT image segmentation, enabling fully automated end-to-end segmentation of the network. In this paper, the UNet++ structure is selected as the baseline. The residual block feature encoder based on context awareness enhances the feature extraction ability and solves the problem of deep network degradation. The introduction of an efficient attention module combines the depth of the feature map with spatial information to alleviate the uneven sample distribution impact; Use DiceLoss to replace the cross-entropy loss function to optimize network parameters. The liver and liver tumor segmentation accuracy on the LITS dataset was 95.8% and 89.3%, respectively. The results show that compared with other algorithms, the method proposed in this paper achieves a good segmentation performance, which has specific reference significance for computer-assisted diagnosis and treatment to attain fine segmentation of liver and liver tumors.
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Affiliation(s)
- Jian Li
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Kongyu Liu
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Yating Hu
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Hongchen Zhang
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Ali Asghar Heidari
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Weijiang Zhang
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Abeer D Algarni
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
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20
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Hasanzadeh A, Hamblin MR, Kiani J, Noori H, Hardie JM, Karimi M, Shafiee H. Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? NANO TODAY 2022; 47:101665. [PMID: 37034382 PMCID: PMC10081506 DOI: 10.1016/j.nantod.2022.101665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.
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Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Jafar Kiani
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Joseph M. Hardie
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran 141556559, Iran
- Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran 1584743311, Iran
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
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21
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Li Y, Zhao D, Liu G, Liu Y, Bano Y, Ibrohimov A, Chen H, Wu C, Chen X. Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine. Front Neuroinform 2022; 16:956423. [PMID: 36387587 PMCID: PMC9659657 DOI: 10.3389/fninf.2022.956423] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/28/2022] [Indexed: 09/19/2023] Open
Abstract
Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.
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Affiliation(s)
- Yupeng Li
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Guangjie Liu
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Yi Liu
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yasmeen Bano
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Alisherjon Ibrohimov
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Chengwen Wu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Xumin Chen
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou University, Wenzhou, China
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22
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Sun X, Zhang Y, Li H, Zhou Y, Shi S, Chen Z, He X, Zhang H, Li F, Yin J, Mou M, Wang Y, Qiu Y, Zhu F. DRESIS: the first comprehensive landscape of drug resistance information. Nucleic Acids Res 2022; 51:D1263-D1275. [PMID: 36243960 PMCID: PMC9825618 DOI: 10.1093/nar/gkac812] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/22/2022] [Accepted: 10/11/2022] [Indexed: 01/30/2023] Open
Abstract
Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.
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Affiliation(s)
| | | | | | | | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xin He
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Zhejiang University–University of Edinburgh Institute, Zhejiang University, Haining 314499, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunzhu Wang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- To whom correspondence should be addressed.
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23
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Li F, Yin J, Lu M, Mou M, Li Z, Zeng Z, Tan Y, Wang S, Chu X, Dai H, Hou T, Zeng S, Chen Y, Zhu F. DrugMAP: molecular atlas and pharma-information of all drugs. Nucleic Acids Res 2022; 51:D1288-D1299. [PMID: 36243961 PMCID: PMC9825453 DOI: 10.1093/nar/gkac813] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/30/2022] [Accepted: 10/12/2022] [Indexed: 02/06/2023] Open
Abstract
The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.
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Affiliation(s)
| | | | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Tan
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Xinyi Chu
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- Correspondence may also be addressed to Su Zeng.
| | - Yuzong Chen
- Correspondence may also be addressed to Yuzong Chen.
| | - Feng Zhu
- To whom correspondence should be addressed.
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24
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Amahong K, Zhang W, Zhou Y, Zhang S, Yin J, Li F, Xu H, Yan T, Yue Z, Liu Y, Hou T, Qiu Y, Tao L, Han L, Zhu F. CovInter: interaction data between coronavirus RNAs and host proteins. Nucleic Acids Res 2022; 51:D546-D556. [PMID: 36200814 PMCID: PMC9825556 DOI: 10.1093/nar/gkac834] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/07/2022] [Accepted: 09/16/2022] [Indexed: 01/29/2023] Open
Abstract
Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.
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Affiliation(s)
| | | | | | - Song Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Hongquan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Tianci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Zixuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuhong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Lin Tao
- Correspondence may also be addressed to Lin Tao.
| | - Lianyi Han
- Correspondence may also be addressed to Lianyi Han.
| | - Feng Zhu
- To whom correspondence should be addressed. Tel: +86 189 8946 6518; Fax: +86 571 8820 8444;
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25
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Liu S, Chen L, Zhang Y, Zhou Y, He Y, Chen Z, Qi S, Zhu J, Chen X, Zhang H, Luo Y, Qiu Y, Tao L, Zhu F. M6AREG: m6A-centered regulation of disease development and drug response. Nucleic Acids Res 2022; 51:D1333-D1344. [PMID: 36134713 PMCID: PMC9825441 DOI: 10.1093/nar/gkac801] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/27/2022] [Accepted: 09/06/2022] [Indexed: 01/30/2023] Open
Abstract
As the most prevalent internal modification in eukaryotic RNAs, N6-methyladenosine (m6A) has been discovered to play an essential role in cellular proliferation, metabolic homeostasis, embryonic development, etc. With the rapid accumulation of research interest in m6A, its crucial roles in the regulations of disease development and drug response are gaining more and more attention. Thus, a database offering such valuable data on m6A-centered regulation is greatly needed; however, no such database is as yet available. Herein, a new database named 'M6AREG' is developed to (i) systematically cover, for the first time, data on the effects of m6A-centered regulation on both disease development and drug response, (ii) explicitly describe the molecular mechanism underlying each type of regulation and (iii) fully reference the collected data by cross-linking to existing databases. Since the accumulated data are valuable for researchers in diverse disciplines (such as pathology and pathophysiology, clinical laboratory diagnostics, medicinal biochemistry and drug design), M6AREG is expected to have many implications for the future conduct of m6A-based regulation studies. It is currently accessible by all users at: https://idrblab.org/m6areg/.
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Affiliation(s)
- Shuiping Liu
- Correspondence may also be addressed to Shuiping Liu.
| | | | | | | | - Ying He
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Shasha Qi
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jinyu Zhu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Xudong Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Hao Zhang
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, 310000, China
| | - Lin Tao
- Correspondence may also be addressed to Lin Tao.
| | - Feng Zhu
- To whom correspondence should be addressed. Tel: +86 189 8946 6518; Fax: +86 571 8820 8444;
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26
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Xue J, Zhang H, Zeng S. Integrate thermostabilized fusion protein apocytochrome b562RIL and N-glycosylation mutations: A novel approach to heterologous expression of human UDP-glucuronosyltransferase (UGT) 2B7. Front Pharmacol 2022; 13:965038. [PMID: 36034790 PMCID: PMC9412022 DOI: 10.3389/fphar.2022.965038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 07/19/2022] [Indexed: 11/15/2022] Open
Abstract
Human UDP-glucuronosyltransferase (UGT) 2B7 is a crucial phase II metabolic enzyme that transfers glucuronic acid from UDP-glucuronic acid (UDPGA) to endobiotic and xenobiotic substrates. Biophysical and biochemical investigations of UGT2B7 are hampered by the challenge of the integral membrane protein purification. This study focused on the expression and purification of recombinant UGT2B7 by optimizing the insertion sites for the thermostabilized fusion protein apocytochrome b562RIL (BRIL) and various mutations to improve the protein yields and homogeneity. Preparation of the recombinant proteins with high purity accelerated the measurement of pharmacokinetic parameters of UGT2B7. The dissociation constants (KD) of two classical substrates (zidovudine and androsterone) and two inhibitors (schisanhenol and hesperetin) of UGT2B7 were determined using the surface plasmon resonance spectroscopy (SPR) for the first time. Using negative-staining transmission electron microscopy (TEM), UGT2B7 protein particles were characterized, which could be useful for further exploring its three-dimensional structure. The methods described in this study could be broadly applied to other UGTs and are expected to provide the basis for the exploration of metabolic enzyme kinetics, the mechanisms of drug metabolisms and drug interactions, changes in pharmacokinetics, and pharmacodynamics studies in vitro.
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Affiliation(s)
- Jia Xue
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haitao Zhang
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Hangzhou Institute of Innovative Medicine, Institute of Pharmacology and Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- *Correspondence: Haitao Zhang, ; Su Zeng,
| | - Su Zeng
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- *Correspondence: Haitao Zhang, ; Su Zeng,
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27
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Zhang S, Sun X, Mou M, Amahong K, Sun H, Zhang W, Shi S, Li Z, Gao J, Zhu F. REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research. Comput Biol Med 2022; 148:105825. [PMID: 35872412 DOI: 10.1016/j.compbiomed.2022.105825] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/29/2022] [Accepted: 07/03/2022] [Indexed: 12/24/2022]
Abstract
Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.
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Affiliation(s)
- Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Kuerbannisha Amahong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
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Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization. Comput Biol Med 2022; 146:105618. [PMID: 35690477 PMCID: PMC9113963 DOI: 10.1016/j.compbiomed.2022.105618] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/07/2022] [Accepted: 05/12/2022] [Indexed: 11/28/2022]
Abstract
COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It usually is diagnosed by examining pathological photographs of the patient's lungs. There is a lot of detailed and essential information on chest radiographs, but manual processing is not as efficient or accurate. As a result, how efficiently analyzing and processing chest radiography of COVID-19 patients is an important research direction to promote COVID-19 diagnosis. To improve the processing efficiency of COVID-19 chest films, a multilevel thresholding image segmentation (MTIS) method based on an enhanced multiverse optimizer (CCMVO) is proposed. CCMVO is improved from the original Multi-Verse Optimizer by introducing horizontal and vertical search mechanisms. It has a more assertive global search ability and can jump out of the local optimum in optimization. The CCMVO-based MTIS method can obtain higher quality segmentation results than HHO, SCA, and other forms and is less prone to stagnation during the segmentation process. To verify the performance of the proposed CCMVO algorithm, CCMVO is first compared with DE, MVO, and other algorithms by 30 benchmark functions; then, the proposed CCMVO is applied to image segmentation of COVID-19 chest radiography; finally, this paper verifies that the combination of MTIS and CCMVO is very successful with good segmentation results by using the Feature Similarity Index (FSIM), the Peak Signal to Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). Therefore, this research can provide an effective segmentation method for a medical organization to process COVID-19 chest radiography and then help doctors diagnose coronavirus pneumonia (COVID-19).
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29
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Li F, Yin J, Lu M, Yang Q, Zeng Z, Zhang B, Li Z, Qiu Y, Dai H, Chen Y, Zhu F. ConSIG: consistent discovery of molecular signature from OMIC data. Brief Bioinform 2022; 23:6618243. [PMID: 35758241 DOI: 10.1093/bib/bbac253] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/09/2022] [Accepted: 05/31/2022] [Indexed: 12/12/2022] Open
Abstract
The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.
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Affiliation(s)
- Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.,Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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30
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Xiang C, Chen C, Li X, Wu Y, Xu Q, Wen L, Xiong W, Liu Y, Zhang T, Dou C, Ding X, Hu L, Chen F, Yan Z, Liang L, Wei G. Computational approach to decode the mechanism of curcuminoids against neuropathic pain. Comput Biol Med 2022; 147:105739. [PMID: 35763932 DOI: 10.1016/j.compbiomed.2022.105739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Curcumin (CUR), demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC) are the main components of turmeric that commonly used to treat neuropathic pain (NP). However, the mechanism of the therapy is not sufficiently clarified. Herein, network pharmacology, molecular docking and molecular dynamics (MD) approaches were used to investigate the mechanism of curcuminoids for NP treatment. METHODS Active targets of curcuminoids were obtained from the Swiss Target database, and NP-related targets were retrieved from GeneCards, OMIM, Drugbank and TTD databases. A protein-protein interaction (PPI) network was built to screen the core targets. Furthermore, DAVID was used for GO and KEGG pathway enrichment analyses. Interactions between potential targets and curcuminoids were assessed by molecular docking and the MD simulations were run for 100ns to validate the docking results on the top six complexes. RESULTS CUR, DMC, and BDMC had 100, 99 and 100 targets respectively. After overlapping with NP there were 33, 33 and 31 targets respectively. PPI network analysis of TOP 10 core targets, TNF, GSK3β were common targets of curcuminoids. Molecular docking and MD results indicated that curcuminoids bind strongly with the core targets. The GO and KEGG showed that curcuminoids regulated nitrogen metabolism, the serotonergic synapse and ErbB signaling pathway to alleviate NP. Furthermore, specific targets in these three compounds were also analysed at the same time. CONCLUSIONS This study systematically explored and compared the anti-NP mechanism of curcuminoids, providing a novel perspective for their utilization.
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Affiliation(s)
- Chunxiao Xiang
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Chunlan Chen
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Xi Li
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Yating Wu
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Qing Xu
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Lingmiao Wen
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Wei Xiong
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Yanjun Liu
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Tinglan Zhang
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Chongyang Dou
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Xian Ding
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Lin Hu
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Fangfang Chen
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Zhiyong Yan
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Lingli Liang
- Department of Physiology and Pathophysiology, Institute of Neuroscience, Translational Medicine Institute, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an, Shanxi, China.
| | - Guihua Wei
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
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31
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Boosted Sine Cosine Algorithm with Application to Medical Diagnosis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6215574. [PMID: 35785140 PMCID: PMC9242811 DOI: 10.1155/2022/6215574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 05/19/2022] [Accepted: 05/26/2022] [Indexed: 01/09/2023]
Abstract
The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO. ESCA_PSO is proposed based on hybrid SCA and particle swarm optimization (PSO) by incorporating multiple mutation strategies into the original SCA_PSO. To validate the effect of ESCA_PSO in handling global optimization problems, ESCA_PSO was compared with quality algorithms on various types of benchmark functions. In addition, the proposed ESCA_PSO was employed to tune the best parameters of support vector machines for dealing with medical diagnosis tasks. The results prove the efficiency of the proposed algorithms in solving optimization problems.
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32
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Ma Y, Liu Q. Generalized matrix factorization based on weighted hypergraph learning for microbe-drug association prediction. Comput Biol Med 2022; 145:105503. [DOI: 10.1016/j.compbiomed.2022.105503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/28/2022] [Accepted: 04/04/2022] [Indexed: 11/03/2022]
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33
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Boosted machine learning model for predicting intradialytic hypotension using serum biomarkers of nutrition. Comput Biol Med 2022; 147:105752. [DOI: 10.1016/j.compbiomed.2022.105752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/22/2022]
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34
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Feng G, Yao H, Li C, Liu R, Huang R, Fan X, Ge R, Miao Q. ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides. Comput Biol Med 2022; 145:105459. [DOI: 10.1016/j.compbiomed.2022.105459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 12/26/2022]
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35
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Dwivedi M, Mukhopadhyay S, Yadav S, Dubey KD. A multidrug efflux protein in Mycobacterium tuberculosis; tap as a potential drug target for drug repurposing. Comput Biol Med 2022; 146:105607. [PMID: 35617724 DOI: 10.1016/j.compbiomed.2022.105607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 05/02/2022] [Accepted: 05/09/2022] [Indexed: 11/27/2022]
Abstract
Tuberculosis (TB) is a serious communicative disease caused by Mycobacterium tuberculosis. Although there are vaccines and drugs available to treat the disease, they are not efficient, moreover, multidrug-resistant TB (MDR-TB) become a major hurdle in its therapy. These MDR strains utilize the multidrug efflux pump as a decisive weapon to fight against antitubercular drugs. Tap membrane protein was observed as a crucial multidrug efflux pump in M. tuberculosis and its critical implication in MDR-MTB development makes it an effective drug target. In the present study, we have utilized various in silico approaches to predict the applicability of FDA-approved ion channel inhibitors and blockers as therapeutic leads against Tuberculosis by targeting multidrug efflux protein; Tap in MTB. Tap protein structure is predicted by Phyre2 server followed by model refinement, validation, physio-chemical catheterization and target prediction. Further, the interaction between Tap protein and ligands were analysed by molecular docking and MD simulation run of 100 ns. Based on implication and compatibility, 18 FDA-approved ion channel inhibitors and blockers are selected as a ligand against the Tap protein and eventually observed five ligands; Glimepiride, Flecainide, Flupiritine, Nimodipine and Amlodipine as promising compounds which have exhibited the significant stable interaction with Tap protein and are proposed to modulate or interfere with its activity. These compounds illustrated the substantial docking score and total binding enthalpy more than -7 kcal/mol and -42 kcal/mol respectively which implies that the selected FDA-approved compounds can spontaneously interact with the Tap protein to modulate its function. This study proposed Tap protein as a prominent drug target in MTB and investigated compounds that show considerable interaction with the Tap protein as potential therapeutic molecules. These interactions may lead to modulating or inhibit the activity of drug efflux protein thereby making MTB susceptible to antitubercular drugs.
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Affiliation(s)
- Manish Dwivedi
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, 226028, India.
| | - Sutanu Mukhopadhyay
- Department of Chemistry, Ramakrishna Mission Vivekananda Centenary College, West Bengal, India
| | - Shalini Yadav
- Department of Chemistry, Shiv Nadar University, Greater Noida, 201314, India
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36
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Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094776] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The goal of this research is to offer an effective intelligent model for forecasting college students’ career decisions in order to give a useful reference for career decisions and policy formation by relevant departments. The suggested prediction model is mainly based on a support vector machine (SVM) that has been modified using an enhanced butterfly optimization approach with a communication mechanism and Gaussian bare-bones mechanism (CBBOA). To get a better set of parameters and feature subsets, first, we added a communication mechanism to BOA to improve its global search capability and balance exploration and exploitation trends. Then, Gaussian bare-bones was added to increase the population diversity of BOA and its ability to jump out of the local optimum. The optimal SVM model (CBBOA-SVM) was then developed to predict the career decisions of college students based on the obtained parameters and feature subsets that are already optimized by CBBOA. In order to verify the effectiveness of CBBOA, we compared it with some advanced algorithms on all benchmark functions of CEC2014. Simulation results demonstrated that the performance of CBBOA is indeed more comprehensive. Meanwhile, comparisons between CBBOA-SVM and other machine learning approaches for career decision prediction were carried out, and the findings demonstrate that the provided CBBOA-SVM has better classification and more stable performance. As a result, it is plausible to conclude that the CBBOA-SVM is capable of being an effective tool for predicting college student career decisions.
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37
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Zhang C, Mou M, Zhou Y, Zhang W, Lian X, Shi S, Lu M, Sun H, Li F, Wang Y, Zeng Z, Li Z, Zhang B, Qiu Y, Zhu F, Gao J. Biological activities of drug inactive ingredients. Brief Bioinform 2022; 23:6582006. [PMID: 35524477 DOI: 10.1093/bib/bbac160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/01/2022] [Accepted: 04/09/2022] [Indexed: 02/06/2023] Open
Abstract
In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.
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Affiliation(s)
- Chenyang Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
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38
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An efficient rotational direction heap-based optimization with orthogonal structure for medical diagnosis. Comput Biol Med 2022; 146:105563. [PMID: 35551010 DOI: 10.1016/j.compbiomed.2022.105563] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 04/24/2022] [Accepted: 04/24/2022] [Indexed: 12/17/2022]
Abstract
The heap-based optimizer (HBO) is an optimization method proposed in recent years that may face local stagnation problems and show slow convergence speed due to the lack of detailed analysis of optimal solutions and a comprehensive search. Therefore, to mitigate these drawbacks and strengthen the performance of the algorithm in the field of medical diagnosis, a new MGOHBO method is proposed by introducing the modified Rosenbrock's rotational direction method (MRM), an operator from the grey wolf optimizer (GWM), and an orthogonal learning strategy (OL). The MGOHBO is compared with eleven famous and improved optimizers on IEEE CEC 2017. The results on benchmark functions indicate that the boosted MGOHBO has several significant advantages in terms of convergence accuracy and speed of the process. Additionally, this article analyzed the diversity and balance of MGOHBO in detail. Finally, the proposed MGOHBO algorithm is utilized to optimize the kernel extreme learning machines (KELM), and a new MGOHBO-KELM is proposed. To validate the performance of MGOHBO-KELM, seven disease diagnostic questions were introduced for testing in this work. In contrast to advanced models such as HBO-KELM and BP, it can be concluded that the MGOHBO-KELM model can achieve optimal results, which also proves that it has practical significance in solving medical diagnosis problems.
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39
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Wang Z, Zhang Y, Li Q, Zou Q, Liu Q. A road map for happiness: The psychological factors related cell types in various parts of human body from single cell RNA-seq data analysis. Comput Biol Med 2022; 143:105286. [PMID: 35183972 DOI: 10.1016/j.compbiomed.2022.105286] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 01/16/2022] [Accepted: 01/24/2022] [Indexed: 12/13/2022]
Abstract
Massive evidence from all sources including zoology, neurobiology and immunology has confirmed that psychological factors can raise remarkable physiological effects. Researchers have long been aware of the potential value of these effects and wanted to harness them in the development of new drugs and therapies, for which the mechanism study is a necessary prerequisite. However, most of these studies are restricted to neuroscience, or starts with blood sample and fall into the area of immunity. In this study, we choose to focus on the psychological factor of happiness, mining existing publicly available single cell RNA sequencing (scRNA-seq) data for the expression of happiness-related genes collected from various sources of literature in all types of cells in the samples, finding that the expression of these genes is not restricted within neuro-regulated cells or tissue-resident immune cells, on the opposite, cell types that are unique to tissue and organ without direct regulation from nervous system account for the majority to express the happiness-related genes. Our research is a preliminary exploration of where our body respond to our mind at cell level, and lays the foundation for more detailed mechanism research.
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Affiliation(s)
- Ziwei Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology, China
| | - Ying Zhang
- Department of Anesthesiology, Hospital T.C.M Affiliated to Southwest Medical University, Luzhou, China
| | - Qun Li
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology, China; Yangtze Delta Region Institute Quzhou, University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.
| | - Qing Liu
- Department of Algology, Hospital T.C.M Affiliated to Southwest Medical University, Luzhou, China.
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40
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Yang X, Zhao D, Yu F, Heidari AA, Bano Y, Ibrohimov A, Liu Y, Cai Z, Chen H, Chen X. An optimized machine learning framework for predicting intradialytic hypotension using indexes of chronic kidney disease-mineral and bone disorders. Comput Biol Med 2022; 145:105510. [DOI: 10.1016/j.compbiomed.2022.105510] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 04/07/2022] [Accepted: 04/07/2022] [Indexed: 11/03/2022]
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41
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Chen Y, Wang Y, Ding Y, Su X, Wang C. RGCNCDA: Relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs. Comput Biol Med 2022; 143:105322. [PMID: 35217342 DOI: 10.1016/j.compbiomed.2022.105322] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 02/11/2022] [Accepted: 02/13/2022] [Indexed: 12/21/2022]
Abstract
Recently, a large number of studies have indicated that circRNAs with covalently closed loops play important roles in biological processes and have potential as diagnostic biomarkers. Therefore, research on the circRNA-disease relationship is helpful in disease diagnosis and treatment. However, traditional biological verification methods require considerable labor and time costs. In this paper, we propose a new computational method (RGCNCDA) to predict circRNA-disease associations based on relational graph convolutional networks (R-GCNs). The method first integrates the circRNA similarity network, miRNA similarity network, disease similarity network and association networks among them to construct a global heterogeneous network. Then, it employs the random walk with restart (RWR) and principal component analysis (PCA) models to learn low-dimensional and high-order information from the global heterogeneous network as the topological features. Finally, a prediction model based on an R-GCN encoder and a DistMult decoder is built to predict the potential disease-associated circRNA. The predicted results demonstrate that RGCNCDA performs significantly better than the other six state-of-the-art methods in a 5-fold cross validation. Furthermore, the case study illustrates that RGCNCDA can effectively discover potential circRNA-disease associations.
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Affiliation(s)
- Yaojia Chen
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Yanpeng Wang
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Xi Su
- Foshan Maternity & Child Healthcare Hospital, Southern Medical University, Foshan, China.
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China.
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42
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Random Replacement Crisscross Butterfly Optimization Algorithm for Standard Evaluation of Overseas Chinese Associations. ELECTRONICS 2022. [DOI: 10.3390/electronics11071080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The butterfly optimization algorithm (BOA) is a swarm intelligence optimization algorithm proposed in 2019 that simulates the foraging behavior of butterflies. Similarly, the BOA itself has certain shortcomings, such as a slow convergence speed and low solution accuracy. To cope with these problems, two strategies are introduced to improve the performance of BOA. One is the random replacement strategy, which involves replacing the position of the current solution with that of the optimal solution and is used to increase the convergence speed. The other is the crisscross search strategy, which is utilized to trade off the capability of exploration and exploitation in BOA to remove local dilemmas whenever possible. In this case, we propose a novel optimizer named the random replacement crisscross butterfly optimization algorithm (RCCBOA). In order to evaluate the performance of RCCBOA, comparative experiments are conducted with another nine advanced algorithms on the IEEE CEC2014 function test set. Furthermore, RCCBOA is combined with support vector machine (SVM) and feature selection (FS)—namely, RCCBOA-SVM-FS—to attain a standardized construction model of overseas Chinese associations. It is found that the reasonableness of bylaws; the regularity of general meetings; and the right to elect, be elected, and vote are of importance to the planning and standardization of Chinese associations. Compared with other machine learning methods, the RCCBOA-SVM-FS model has an up to 95% accuracy when dealing with the normative prediction problem of overseas Chinese associations. Therefore, the constructed model is helpful for guiding the orderly and healthy development of overseas Chinese associations.
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Xia W, Zheng L, Fang J, Li F, Zhou Y, Zeng Z, Zhang B, Li Z, Li H, Zhu F. PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods. Comput Biol Med 2022; 145:105465. [PMID: 35366467 DOI: 10.1016/j.compbiomed.2022.105465] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 02/06/2023]
Abstract
Bioinformatic annotation of protein function is essential but extremely sophisticated, which asks for extensive efforts to develop effective prediction method. However, the existing methods tend to amplify the representativeness of the families with large number of proteins by misclassifying the proteins in the families with small number of proteins. That is to say, the ability of the existing methods to annotate proteins in the 'rare classes' remains limited. Herein, a new protein function annotation strategy, PFmulDL, integrating multiple deep learning methods, was thus constructed. First, the recurrent neural network was integrated, for the first time, with the convolutional neural network to facilitate the function annotation. Second, a transfer learning method was introduced to the model construction for further improving the prediction performances. Third, based on the latest data of Gene Ontology, the newly constructed model could annotate the largest number of protein families comparing with the existing methods. Finally, this newly constructed model was found capable of significantly elevating the prediction performance for the 'rare classes' without sacrificing that for the 'major classes'. All in all, due to the emerging requirements on improving the prediction performance for the proteins in 'rare classes', this new strategy would become an essential complement to the existing methods for protein function prediction. All the models and source codes are freely available and open to all users at: https://github.com/idrblab/PFmulDL.
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Affiliation(s)
- Weiqi Xia
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Lingyan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Jiebin Fang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Honglin Li
- School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
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A novel mTOR-associated gene signature for predicting prognosis and evaluating tumor immune microenvironment in lung adenocarcinoma. Comput Biol Med 2022; 145:105394. [PMID: 35325730 DOI: 10.1016/j.compbiomed.2022.105394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND The mechanistic target of rapamycin (mTOR) was proven to have great impact on apoptosis, cell proliferation, autophagy, and many other fundamental cellular processes; moreover, it closely correlates with tumor occurrence and development. However, few studies have constructed signatures based on mTOR-associated genes to assess multiple indicators of prognosis in lung adenocarcinoma (LUAD) patients. METHODS mTOR-associated gene sets, whole mRNA expression matrices, and clinical information of LUAD patients in training and validation cohorts were obtained from multiple public databases. Multiple methods were used to screen candidate genes, construct signatures, validate internally and externally, and conduct further studies: differentially expressed gene analysis, LASSO Cox regression analysis, Cox regression analysis, risk factor analysis, nomogram analysis, functional enrichment analysis, analyses in tumor immune microenvironment, and therapy. RESULTS A prognostic signature containing 8 genes (LDHA, SLA, WNT7A, PLK1, CCT6A, BTG2, TXNRD1, and DDIT4) was constructed. It performed well in both internal and external validation. Subsequent analysis found that the prognostic signature was of great significance in evaluating the tumor immune microenvironment and could guide the treatment of patients with LUAD to a certain extent. CONCLUSION The constructed mTOR-associated gene signature accurately predicted the prognostic pattern of patients with LUAD and is expected to be extremely useful in guiding LUAD therapy.
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HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins. Comput Biol Med 2022; 145:105395. [PMID: 35334314 DOI: 10.1016/j.compbiomed.2022.105395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 12/24/2022]
Abstract
The identification of DNA-binding proteins (DBPs) has always been a hot issue in the field of sequence classification. However, considering that the experimental identification method is very resource-intensive, the construction of a computational prediction model is worthwhile. This study developed and evaluated a hybrid kernel alignment maximization-based multiple kernel model (HKAM-MKM) for predicting DBPs. First, we collected two datasets and performed feature extraction on the sequences to obtain six feature groups, and then constructed the corresponding kernels. To ensure the effective utilisation of the base kernel and avoid ignoring the difference between the sample and its neighbours, we proposed local kernel alignment to calculate the kernel between the sample and its neighbours, with each sample as the centre. We combined the global and local kernel alignments to develop a hybrid kernel alignment model, and balance the relationship between the two through parameters. By maximising the hybrid kernel alignment value, we obtained the weight of each kernel and then linearly combined the kernels in the form of weights. Finally, the fused kernel was input into a support vector machine for training and prediction. Finally, in the independent test sets PDB186 and PDB2272, we obtained the highest Matthew's correlation coefficient (MCC) (0.768 and 0.5962, respectively) and the highest accuracy (87.1% and 78.43%, respectively), which were superior to the other predictors. Therefore, HKAM-MKM is an efficient prediction tool for DBPs.
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Wang Y, Qin D, Jin L, Liang G. Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation. Comput Biol Med 2022; 145:105410. [PMID: 35325732 DOI: 10.1016/j.compbiomed.2022.105410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/11/2022] [Accepted: 03/12/2022] [Indexed: 11/03/2022]
Abstract
Atopic dermatitis (AD) is a common inflammatory skin disease involving multiple signaling pathways. One of the effective treatment strategies of AD is to develop a new drug capable of regulating the key therapeutic targets. Here we report the combination use of network analysis, deep learning, and molecular simulation for the identification of key therapeutic targets for AD and screening of potential multi-target drugs. From the TCM@Taiwan database, we identify a small molecule, namely caffeoyl malic acid (CMA), to inhibit the key therapeutic targets (TNFα and IL-4) for AD. CMA is further identified as a TNFα inhibitor by a deep learning model based on convolutional neural network. Molecular simulations demonstrate that CMA can stably bind to TNFα and IL-4, thereby producing diverse effects on the structural fluctuation, structural flexibility, looseness, and motion strength of each protein. Furthermore, conformation alignments reveal that CMA makes the distance between chain A and C of TNFα become wider and the slit between the two α helices of IL-4 get narrow obviously. CMA leads to the change of protein conformation, which hinders the formation of the protein-receptor complex. Collectively, our findings suggest that CMA is a potential dual TNFα/IL-4 inhibitor for the treatment of AD.
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Affiliation(s)
- Yue Wang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China
| | - Dongya Qin
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China
| | - Liang Jin
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China.
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Liu J, Wei J, Heidari AA, Kuang F, Zhang S, Gui W, Chen H, Pan Z. Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis. Comput Biol Med 2022; 144:105356. [PMID: 35299042 DOI: 10.1016/j.compbiomed.2022.105356] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/25/2022] [Accepted: 02/25/2022] [Indexed: 01/09/2023]
Abstract
Classification models such as Multi-Verse Optimization (MVO) play a vital role in disease diagnosis. To improve the efficiency and accuracy of MVO, in this paper, the defects of MVO are mitigated and the improved MVO is combined with kernel extreme learning machine (KELM) for effective disease diagnosis. Although MVO obtains some relatively good results on some problems of interest, it suffers from slow convergence speed and local optima entrapment for some many-sided basins, especially multi-modal problems with high dimensions. To solve these shortcomings, in this study, a new chaotic simulated annealing overhaul of MVO (CSAMVO) is proposed. Based on MVO, two approaches are adopted to offer a relatively stable and efficient convergence speed. Specifically, a chaotic intensification mechanism (CIP) is applied to the optimal universe evaluation stage to increase the depth of the universe search. After obtaining relatively satisfactory results, the simulated annealing algorithm (SA) is employed to reinforce the capability of MVO to avoid local optima. To evaluate its performance, the proposed CSAMVO approach was compared with a wide range of classical algorithms on thirty-nine benchmark functions. The results show that the improved MVO outperforms the other algorithms in terms of solution quality and convergence speed. Furthermore, based on CSAMVO, a hybrid KELM model termed CSAMVO-KELM is established for disease diagnosis. To evaluate its effectiveness, the new hybrid system was compared with a multitude of competitive classifiers on two disease diagnosis problems. The results demonstrate that the proposed CSAMVO-assisted classifier can find solutions with better learning potential and higher predictive performance.
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Affiliation(s)
- Jiacong Liu
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Jiahui Wei
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Fangjun Kuang
- School of Information Engineering, Wenzhou Business College, Wenzhou, 325035, China.
| | - Siyang Zhang
- School of Information Engineering, Wenzhou Business College, Wenzhou, 325035, China.
| | - Wenyong Gui
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Zhifang Pan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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Chen Y, Yang XH, Wei Z, Heidari AA, Zheng N, Li Z, Chen H, Hu H, Zhou Q, Guan Q. Generative Adversarial Networks in Medical Image augmentation: A review. Comput Biol Med 2022; 144:105382. [PMID: 35276550 DOI: 10.1016/j.compbiomed.2022.105382] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 02/25/2022] [Accepted: 03/02/2022] [Indexed: 12/31/2022]
Abstract
OBJECT With the development of deep learning, the number of training samples for medical image-based diagnosis and treatment models is increasing. Generative Adversarial Networks (GANs) have attracted attention in medical image processing due to their excellent image generation capabilities and have been widely used in data augmentation. In this paper, a comprehensive and systematic review and analysis of medical image augmentation work are carried out, and its research status and development prospects are reviewed. METHOD This paper reviews 105 medical image augmentation related papers, which mainly collected by ELSEVIER, IEEE Xplore, and Springer from 2018 to 2021. We counted these papers according to the parts of the organs corresponding to the images, and sorted out the medical image datasets that appeared in them, the loss function in model training, and the quantitative evaluation metrics of image augmentation. At the same time, we briefly introduce the literature collected in three journals and three conferences that have received attention in medical image processing. RESULT First, we summarize the advantages of various augmentation models, loss functions, and evaluation metrics. Researchers can use this information as a reference when designing augmentation tasks. Second, we explore the relationship between augmented models and the amount of the training set, and tease out the role that augmented models may play when the quality of the training set is limited. Third, the statistical number of papers shows that the development momentum of this research field remains strong. Furthermore, we discuss the existing limitations of this type of model and suggest possible research directions. CONCLUSION We discuss GAN-based medical image augmentation work in detail. This method effectively alleviates the challenge of limited training samples for medical image diagnosis and treatment models. It is hoped that this review will benefit researchers interested in this field.
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Affiliation(s)
- Yizhou Chen
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Xu-Hua Yang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Zihan Wei
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore.
| | - Nenggan Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Zhicheng Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China.
| | - Haigen Hu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Qianwei Zhou
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Qiu Guan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
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Meng C, Ju Y, Shi H. TMPpred: A support vector machine-based thermophilic protein identifier. Anal Biochem 2022; 645:114625. [PMID: 35218736 DOI: 10.1016/j.ab.2022.114625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022]
Abstract
MOTIVATION The thermostability of proteins will cause them to break the temperature binding and play more functions. Using machine learning, we explored the mechanism of and reasons for protein thermostability characteristics. RESULTS Different from other methods that only pursue the performance of models, we aim to find important features so as to provide a powerful reference for in vitro experiments. We transformed this problem into a binary classification problem, that is, the distinction between thermophilic proteins and nonthermophilic proteins. Using support vector machine-based model construction and analysis, we inferred that Gly, Ala, Ser and Thr may be the most important components at the residue level that determine the thermal stability of proteins. It is also noteworthy that our proposed model obtains an Sn of 0.892, an Sp of 0.857, an ACC of 0.87566 and an AUC of 0.874. To facilitate other researchers, we wrapped our model and deployed it as a web server, which is accessible at http://112.124.26.17:7000/TMPpred/index.html.
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Affiliation(s)
- Chaolu Meng
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Hohhot, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China.
| | - Hua Shi
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
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Li F, Zhou Y, Zhang Y, Yin J, Qiu Y, Gao J, Zhu F. POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability. Brief Bioinform 2022; 23:6532538. [PMID: 35183059 DOI: 10.1093/bib/bbac040] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/21/2022] [Accepted: 01/27/2022] [Indexed: 12/17/2022] Open
Abstract
Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.
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Affiliation(s)
- Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Jianqing Gao
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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