1
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Zhang S, Zhao Y, Liang Y. AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides. Bioinformatics 2024; 40:btae142. [PMID: 38452348 PMCID: PMC10973939 DOI: 10.1093/bioinformatics/btae142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 03/01/2024] [Accepted: 03/06/2024] [Indexed: 03/09/2024] Open
Abstract
MOTIVATION Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect. RESULTS In this article, we propose AACFlow, an end-to-end model for identification of ACPs based on deep learning. End-to-end models have more room to automatically adjust according to the data, making the overall fit better and reducing error propagation. The combination of attention augmented convolutional neural network (AAConv) and multi-layer convolutional neural network (CNN) forms a deep representation learning module, which is used to obtain global and local information on the sequence. Based on the concept of flow network, multi-head flow-attention mechanism is introduced to mine the deep features of the sequence to improve the efficiency of the model. On the independent test dataset, the ACC, Sn, Sp, and AUC values of AACFlow are 83.9%, 83.0%, 84.8%, and 0.892, respectively, which are 4.9%, 1.5%, 8.0%, and 0.016 higher than those of the baseline model. The MCC value is 67.85%. In addition, we visualize the features extracted by each module to enhance the interpretability of the model. Various experiments show that our model is more competitive in predicting ACPs.
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Affiliation(s)
- Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Ya Zhao
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Yunyun Liang
- School of Science, Xi’an Polytechnic University, Xi'an 710048, China
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2
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Luo Y, Liu XY, Yang K, Huang K, Hong M, Zhang J, Wu Y, Nie Z. Toward Unified AI Drug Discovery with Multimodal Knowledge. HEALTH DATA SCIENCE 2024; 4:0113. [PMID: 38486623 PMCID: PMC10886071 DOI: 10.34133/hds.0113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/25/2024] [Indexed: 03/17/2024]
Abstract
Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.
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Affiliation(s)
- Yizhen Luo
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
- Department of Computer Science and Technology,
Tsinghua University, Beijing, China
| | - Xing Yi Liu
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
| | - Kai Yang
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
| | - Kui Huang
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
- School of Software and Microelectronics,
Peking University, Beijing, China
| | - Massimo Hong
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
- Department of Computer Science and Technology,
Tsinghua University, Beijing, China
| | - Jiahuan Zhang
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
| | - Yushuai Wu
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
| | - Zaiqing Nie
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
- Beijing Academy of Artificial Intelligence (BAAI), Beijing, China
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3
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Ren ZH, Yu CQ, Li LP, You ZH, Li ZW, Zhang SW, Zeng X, Shang YF. SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution. J Chem Inf Model 2024; 64:238-249. [PMID: 38103039 DOI: 10.1021/acs.jcim.3c01665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.
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Affiliation(s)
- Zhong-Hao Ren
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Li-Ping Li
- College of Agriculture and Forestry, Longdong University, Qingyang 745000, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Shan-Wen Zhang
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Yi-Fan Shang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
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4
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Le NQK. Leveraging transformers-based language models in proteome bioinformatics. Proteomics 2023; 23:e2300011. [PMID: 37381841 DOI: 10.1002/pmic.202300011] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023]
Abstract
In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.
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Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
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5
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Zhao BW, Su XR, Yang Y, Li DX, Li GD, Hu PW, Zhao YG, Hu L. Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks. Methods 2023; 220:106-114. [PMID: 37972913 DOI: 10.1016/j.ymeth.2023.10.014] [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: 06/30/2023] [Revised: 10/13/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yue Yang
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Dong-Xu Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Guo-Dong Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Peng-Wei Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yong-Gang Zhao
- Department of Orthopaedic Surgery (hand and foot trauma), People's Hospital of Dongxihu, Wuhan 420100, China.
| | - Lun Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
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6
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Wan H, Liu Q, Ju Y. Utilize a few features to classify presynaptic and postsynaptic neurotoxins. Comput Biol Med 2023; 152:106380. [PMID: 36473343 DOI: 10.1016/j.compbiomed.2022.106380] [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: 09/18/2022] [Revised: 10/21/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022]
Abstract
Neurotoxins are a class of proteins that have a significant damaging effect on nerve tissue. Neurotoxins are classified into presynaptic neurotoxins and postsynaptic neurotoxins, and accurate identification of neurotoxins plays a key role in drug development. In this study, 90 presynaptic neurotoxins and 165 postsynaptic neurotoxins were classified. The features of the presynaptic and postsynaptic neurotoxin sequences were extracted using the AutoProp feature extraction method and feature selection was performed using the maximum relevance maximum distance (MRMD) program, Finally, only two features were retained to achieve 84.7% classification accuracy. Moreover, it was found that the two retained features were present in the conserved sites and motifs of presynaptic neurotoxins and could represent the critical structures of presynaptic neurotoxins. This method demonstrates that using a few key features to classify proteins can effectively identify critical protein structures.
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Affiliation(s)
- Hao Wan
- Institute of Advanced Cross-field Science, College of Life Science, Qingdao University, Qingdao, China
| | - Qing Liu
- Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, China.
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China.
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7
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Zhao BW, Su XR, Hu PW, Ma YP, Zhou X, Hu L. A geometric deep learning framework for drug repositioning over heterogeneous information networks. Brief Bioinform 2022; 23:6692552. [PMID: 36125202 DOI: 10.1093/bib/bbac384] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/01/2022] [Accepted: 08/09/2022] [Indexed: 12/14/2022] Open
Abstract
Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Peng-Wei Hu
- Merck China Innovation Hub, Shanghai 200000, China
| | - Yu-Peng Ma
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Xi Zhou
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
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8
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Dhanda SK, Malviya J, Gupta S. Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes. Brief Bioinform 2022; 23:6692551. [PMID: 36070623 DOI: 10.1093/bib/bbac382] [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: 06/07/2022] [Revised: 08/01/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.
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Affiliation(s)
- Sandeep Kumar Dhanda
- Department of Oncology, St Jude Children's Research Hospital, Memphis, Tennessee, USA-38015.,Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Chennai, India
| | - Jitendra Malviya
- Department of Life Sciences and Biological Science, IES University Bhopal, India
| | - Sudheer Gupta
- NGS & Bioinformatics Division, 3B BlackBio Biotech India Ltd., 7-C, Industrial Area, Govindpura, Bhopal, India
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9
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Liu XH, Cheng T, Liu BY, Chi J, Shu T, Wang T. Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development. Front Pharmacol 2022; 13:955648. [PMID: 36016554 PMCID: PMC9395726 DOI: 10.3389/fphar.2022.955648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/13/2022] [Indexed: 12/14/2022] Open
Abstract
COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.
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10
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Chang CC, Hsu HJ, Wu TY, Liou JW. Computer-aided discovery, design, and investigation of COVID-19 therapeutics. Tzu Chi Med J 2022; 34:276-286. [PMID: 35912059 PMCID: PMC9333103 DOI: 10.4103/tcmj.tcmj_318_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 11/22/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) pandemic is currently the most serious public health threat faced by mankind. Thus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is being intensively investigated. Several vaccines are now available for clinical use. However, owing to the highly mutated nature of RNA viruses, the SARS-CoV-2 is changing at a rapid speed. Breakthrough infections by SARS-CoV-2 variants have been seen in vaccinated individuals. As a result, effective therapeutics for treating COVID-19 patients is urgently required. With the advance of computer technology, computational methods have become increasingly powerful in the biomedical research and pharmaceutical drug discovery. The applications of these techniques have largely reduced the costs and simplified processes of pharmaceutical drug developments. Intensive and extensive studies on SARS-CoV-2 proteins have been carried out and three-dimensional structures of the major SARS-CoV-2 proteins have been resolved and deposited in the Protein Data Bank. These structures provide the foundations for drug discovery and design using the structure-based computations, such as molecular docking and molecular dynamics simulations. In this review, introduction to the applications of computational methods in the discovery and design of novel drugs and repurposing of existing drugs for the treatments of COVID-19 is given. The examples of computer-aided investigations and screening of COVID-19 effective therapeutic compounds, functional peptides, as well as effective molecules from the herb medicines are discussed.
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Affiliation(s)
- Chun-Chun Chang
- Department of Laboratory Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Laboratory Medicine and Biotechnology, Tzu Chi University, Hualien, Taiwan
| | - Hao-Jen Hsu
- Department of Life Sciences, Tzu Chi University, Hualien, Taiwan
| | - Tien-Yuan Wu
- Department of Pharmacology, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Je-Wen Liou
- Department of Laboratory Medicine and Biotechnology, Tzu Chi University, Hualien, Taiwan
- Department of Biochemistry, School of Medicine, Tzu Chi University, Hualien, Taiwan
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11
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Pan L, Ji B, Wang H, Wang L, Liu M, Chongcheawchamnan M, Peng S. MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images. Health Inf Sci Syst 2022; 10:4. [PMID: 35432950 PMCID: PMC9004212 DOI: 10.1007/s13755-022-00174-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 04/04/2022] [Indexed: 11/23/2022] Open
Abstract
The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature extraction and the accuracy of model diagnosis. In the experiment, Compared with traditional deep learning models (VGG19, GoogLeNet, Resnet50, Desnet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Furthermore, through ablation experiments, we proved that a multi-channel convolutional neural network (CNN) is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.
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12
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Xiang J, Zhang J, Zhao Y, Wu FX, Li M. Biomedical data, computational methods and tools for evaluating disease-disease associations. Brief Bioinform 2022; 23:6522999. [PMID: 35136949 DOI: 10.1093/bib/bbac006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, China
| | - Jiashuai Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, China
| | - Fang-Xiang Wu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Li
- Division of Biomedical Engineering and Department of Mechanical Engineering at University of Saskatchewan, Saskatoon, Canada
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13
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Wang H, Zhang J, Lu Z, Dai W, Ma C, Xiang Y, Zhang Y. Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients. Brief Bioinform 2022; 23:bbab373. [PMID: 34505138 PMCID: PMC8499921 DOI: 10.1093/bib/bbab373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/06/2021] [Accepted: 08/23/2021] [Indexed: 12/12/2022] Open
Abstract
After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein-protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.
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Affiliation(s)
- Hong Wang
- Chongqing Key Research Laboratory for Drug Metabolism, College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing, 400016, China
- Chongqing Engineering Research Center for Clinical Big-data and Drug Evaluation, Chongqing Medical University, Chongqing, 401331, China
| | - Jingqing Zhang
- Chongqing Key Research Laboratory for Drug Metabolism, College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Zhigang Lu
- Department of Neurology, The First People's Hospital of Jingmen affiliated to Hubei Minzu University, Jingmen, 448000, China
| | - Weina Dai
- Chongqing Key Research Laboratory for Drug Metabolism, College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Chuanjiang Ma
- Chongqing Key Research Laboratory for Drug Metabolism, College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
| | - Yun Xiang
- Gynaecology and Obstetrics, Guangzhou Women and Children's Medical Center, Guangzhou, 510623, China
| | - Yonghong Zhang
- Chongqing Key Research Laboratory for Drug Metabolism, College of Pharmacy, Chongqing Medical University, Chongqing, 400016, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing, 400016, China
- Chongqing Engineering Research Center for Clinical Big-data and Drug Evaluation, Chongqing Medical University, Chongqing, 401331, China
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Zhao BW, Hu L, You ZH, Wang L, Su XR. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. Brief Bioinform 2021; 23:6456295. [PMID: 34891172 DOI: 10.1093/bib/bbab515] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 12/20/2022] Open
Abstract
Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug-disease, drug-protein and protein-disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug-disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug-disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug-disease associations especially for new diseases.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning 530007, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
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