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Wang G, Zhang H, Shao M, Tian M, Feng H, Li Q, Cao C. Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases. Comput Struct Biotechnol J 2024; 23:2478-2486. [PMID: 38952424 PMCID: PMC11215961 DOI: 10.1016/j.csbj.2024.05.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/31/2024] [Accepted: 05/31/2024] [Indexed: 07/03/2024] Open
Abstract
Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.
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Affiliation(s)
- Guishen Wang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
| | - Hangchen Zhang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
| | - Mengting Shao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Min Tian
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Hui Feng
- College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
| | - Qiaoling Li
- Department of Cardiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Chen Cao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
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2
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Jin YT, Tan Y, Gan ZH, Hao YD, Wang TY, Lin H, Tang B. Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors. Methods 2024; 229:125-132. [PMID: 38964595 DOI: 10.1016/j.ymeth.2024.06.012] [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: 05/02/2024] [Revised: 06/01/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024] Open
Abstract
DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.
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Affiliation(s)
- Yan-Ting Jin
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Yang Tan
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Zhong-Hua Gan
- Department of Pathology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yu-Duo Hao
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Tian-Yu Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Bo Tang
- Department of Pathology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China.
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3
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Saadh MJ, Mustafa MA, Kumar A, Alamir HTA, Kumar A, Khudair SA, Faisal A, Alubiady MHS, Jalal SS, Shafik SS, Ahmad I, Khry FAF, Abosaoda MK. Stealth Nanocarriers in Cancer Therapy: a Comprehensive Review of Design, Functionality, and Clinical Applications. AAPS PharmSciTech 2024; 25:140. [PMID: 38890191 DOI: 10.1208/s12249-024-02843-5] [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: 02/17/2024] [Accepted: 05/13/2024] [Indexed: 06/20/2024] Open
Abstract
Nanotechnology has significantly transformed cancer treatment by introducing innovative methods for delivering drugs effectively. This literature review provided an in-depth analysis of the role of nanocarriers in cancer therapy, with a particular focus on the critical concept of the 'stealth effect.' The stealth effect refers to the ability of nanocarriers to evade the immune system and overcome physiological barriers. The review investigated the design and composition of various nanocarriers, such as liposomes, micelles, and inorganic nanoparticles, highlighting the importance of surface modifications and functionalization. The complex interaction between the immune system, opsonization, phagocytosis, and the protein corona was examined to understand the stealth effect. The review carefully evaluated strategies to enhance the stealth effect, including surface coating with polymers, biomimetic camouflage, and targeting ligands. The in vivo behavior of stealth nanocarriers and their impact on pharmacokinetics, biodistribution, and toxicity were also systematically examined. Additionally, the review presented clinical applications, case studies of approved nanocarrier-based cancer therapies, and emerging formulations in clinical trials. Future directions and obstacles in the field, such as advancements in nanocarrier engineering, personalized nanomedicine, regulatory considerations, and ethical implications, were discussed in detail. The review concluded by summarizing key findings and emphasizing the transformative potential of stealth nanocarriers in revolutionizing cancer therapy. This review enhanced the comprehension of nanocarrier-based cancer therapies and their potential impact by providing insights into advanced studies, clinical applications, and regulatory considerations.
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Affiliation(s)
- Mohamed J Saadh
- Faculty of Pharmacy, Middle East University, Amman, 11831, Jordan.
| | - Mohammed Ahmed Mustafa
- Department of Medical Laboratory Technology, University of Imam Jaafar AL-Sadiq, Baghdad, Iraq
| | - Ashwani Kumar
- Department of Life Sciences, School of Sciences, Jain (Deemed-to-be) University, Bengaluru, Karnataka, India
- Department of Pharmacy, Vivekananda Global University, Jaipur, Rajasthan, India
| | | | - Abhishek Kumar
- School of Pharmacy-Adarsh Vijendra Institute of Pharmaceutical Sciences, Shobhit University, Gangoh, 247341, Uttar Pradesh, India
- Department of Pharmacy, Arka Jain University, Jamshedpur, Jharkhand, 831001, India
| | | | - Ahmed Faisal
- Department of Pharmacy, Al-Noor University College, Nineveh, Iraq
| | | | - Sarah Salah Jalal
- College of Pharmacy, National University of Science and Technology, Nasiriyah, Dhi Qar, Iraq
| | - Shafik Shaker Shafik
- Experimental Nuclear Radiation Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq
| | - Irfan Ahmad
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Faeza A F Khry
- Faculty of pharmacy, department of pharmaceutics, Al-Esraa University, Baghdad, Iraq
| | - Munther Kadhim Abosaoda
- College of Technical Engineering, The Islamic University, Najaf, Iraq
- College of Technical Engineering, The Islamic University of Al Diwaniyah, Qadisiyyah, Iraq
- College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq
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Yan M. Receive wireless sensor data through IoT gateway using web client based on border gateway protocol. Heliyon 2024; 10:e31625. [PMID: 38828325 PMCID: PMC11140701 DOI: 10.1016/j.heliyon.2024.e31625] [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: 01/13/2024] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
One of the significant topics in the field of the Internet of Things (IoT) pertains to the interaction and information sharing among people. The utilization of the Border Gateway Protocol (BGP) stack enhances the integration of web protocols and sensor networks, leading to greater accessibility. However, the BGP protocol stack introduces substantial overhead to messages transmitted at each layer, resulting in increased data overhead and energy consumption in networks by several orders of magnitude. This paper proposes a method to reduce the overhead on small and medium-sized packets. In multi-temporal networks utilizing BGP, scheduling and aggregating BGP packets at sensor nodes help achieve specific objectives. Various research methodologies and measures are employed to facilitate this, including request classification, BGP response prioritization within the network, determination of maximum acceptable delay, and overall network management. Synchronization and temporal integration of received messages at sensor nodes are performed, considering the maximum allowable delay for each message and the availability of the destination to process the accumulated messages. The evaluation results of the proposed method demonstrate a significant reduction in energy consumption and network traffic, particularly in monitoring applications within multi-stage networks. The protocol stack used is derived from the BGP standard.
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Affiliation(s)
- Meng Yan
- School of Electrical Information, Changchun Guanghua University, Changchun, Jilin, 130000, China
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5
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Liao Y, Tang Z, Gao K, Trik M. Optimization of resources in intelligent electronic health systems based on internet of things to predict heart diseases via artificial neural network. Heliyon 2024; 10:e32090. [PMID: 38933933 PMCID: PMC11200294 DOI: 10.1016/j.heliyon.2024.e32090] [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: 01/13/2024] [Revised: 05/21/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.
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Affiliation(s)
- Yuxuan Liao
- School of Information and Management, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Zhong Tang
- School of Humanities and Social Sciences, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Kun Gao
- Affiliated Cancer Hospital, Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Mohammad Trik
- Department of Computer Engineering, Boukan Branch, Islamic Azad University, Boukan, Iran
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6
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Zucker R, Kovalerchik M, Stern A, Kaufman H, Linial M. Revealing the genetic complexity of hypothyroidism: integrating complementary association methods. Front Genet 2024; 15:1409226. [PMID: 38919955 PMCID: PMC11196612 DOI: 10.3389/fgene.2024.1409226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 05/16/2024] [Indexed: 06/27/2024] Open
Abstract
Hypothyroidism is a common endocrine disorder whose prevalence increases with age. The disease manifests itself when the thyroid gland fails to produce sufficient thyroid hormones. The disorder includes cases of congenital hypothyroidism (CH), but most cases exhibit hormonal feedback dysregulation and destruction of the thyroid gland by autoantibodies. In this study, we sought to identify causal genes for hypothyroidism in large populations. The study used the UK-Biobank (UKB) database, reporting on 13,687 cases of European ancestry. We used GWAS compilation from Open Targets (OT) and tuned protocols focusing on genes and coding regions, along with complementary association methods of PWAS (proteome-based) and TWAS (transcriptome-based). Comparing summary statistics from numerous GWAS revealed a limited number of variants associated with thyroid development. The proteome-wide association study method identified 77 statistically significant genes, half of which are located within the Chr6-MHC locus and are enriched with autoimmunity-related genes. While coding GWAS and PWAS highlighted the centrality of immune-related genes, OT and transcriptome-wide association study mostly identified genes involved in thyroid developmental programs. We used independent populations from Finland (FinnGen) and the Taiwan cohort to validate the PWAS results. The higher prevalence in females relative to males is substantiated as the polygenic risk score prediction of hypothyroidism relied mostly from the female group genetics. Comparing results from OT, TWAS, and PWAS revealed the complementary facets of hypothyroidism's etiology. This study underscores the significance of synthesizing gene-phenotype association methods for this common, intricate disease. We propose that the integration of established association methods enhances interpretability and clinical utility.
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Affiliation(s)
- Roei Zucker
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michael Kovalerchik
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Amos Stern
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Hadasa Kaufman
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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7
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Guo X, Zheng Z, Cheong KH, Zou Q, Tiwari P, Ding Y. Sequence homology score-based deep fuzzy network for identifying therapeutic peptides. Neural Netw 2024; 178:106458. [PMID: 38901093 DOI: 10.1016/j.neunet.2024.106458] [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: 12/18/2023] [Revised: 05/29/2024] [Accepted: 06/09/2024] [Indexed: 06/22/2024]
Abstract
The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923).
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Affiliation(s)
- Xiaoyi Guo
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Quzhou People's Hospital, Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000, PR China; Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, S637371, Singapore.
| | - Ziyu Zheng
- Department of Mathematical Sciences, University of Nottingham Ningbo, Ningbo, 315100, PR China.
| | - Kang Hao Cheong
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, S637371, Singapore; College of Computing and Data Science, Nanyang Technological University, S639798, Singapore.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, PR China.
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden.
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, PR China.
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8
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Naser IH, Zaid M, Ali E, Jabar HI, Mustafa AN, Alubiady MHS, Ramadan MF, Muzammil K, Khalaf RM, Jalal SS, Alawadi AH, Alsalamy A. Unveiling innovative therapeutic strategies and future trajectories on stimuli-responsive drug delivery systems for targeted treatment of breast carcinoma. NAUNYN-SCHMIEDEBERG'S ARCHIVES OF PHARMACOLOGY 2024; 397:3747-3770. [PMID: 38095649 DOI: 10.1007/s00210-023-02885-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/02/2023] [Indexed: 05/23/2024]
Abstract
This comprehensive review delineates the latest advancements in stimuli-responsive drug delivery systems engineered for the targeted treatment of breast carcinoma. The manuscript commences by introducing mammary carcinoma and the current therapeutic methodologies, underscoring the urgency for innovative therapeutic strategies. Subsequently, it elucidates the logic behind the employment of stimuli-responsive drug delivery systems, which promise targeted drug administration and the minimization of adverse reactions. The review proffers an in-depth analysis of diverse types of stimuli-responsive systems, including thermoresponsive, pH-responsive, and enzyme-responsive nanocarriers. The paramount importance of material choice, biocompatibility, and drug loading strategies in the design of these systems is accentuated. The review explores characterization methodologies for stimuli-responsive nanocarriers and probes preclinical evaluations of their efficacy, toxicity, pharmacokinetics, and biodistribution in mammary carcinoma models. Clinical applications of stimuli-responsive systems, ongoing clinical trials, the potential of combination therapies, and the utility of multifunctional nanocarriers for the co-delivery of assorted drugs and therapies are also discussed. The manuscript addresses the persistent challenge of drug resistance in mammary carcinoma and the potential of stimuli-responsive systems in surmounting it. Regulatory and safety considerations, including FDA guidelines and biocompatibility assessments, are outlined. The review concludes by spotlighting future trajectories and emergent technologies in stimuli-responsive drug delivery, focusing on pioneering approaches, advancements in nanotechnology, and personalized medicine considerations. This review aims to serve as a valuable compendium for researchers and clinicians interested in the development of efficacious and safe stimuli-responsive drug delivery systems for the treatment of breast carcinoma.
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Affiliation(s)
- Israa Habeeb Naser
- Medical Laboratories Techniques Department, AL-Mustaqbal University, Hillah, Babil, Iraq
| | - Muhaned Zaid
- Department of Pharmacy, Al-Manara College for Medical Sciences, Maysan, Amarah, Iraq
| | - Eyhab Ali
- Al-Zahraa University for Women, Karbala, Iraq
| | - Hayder Imad Jabar
- Department of Pharmaceutics, College of Pharmacy, University of Al-Ameed, Karbala, Iraq
| | | | | | | | - Khursheed Muzammil
- Department of Public Health, College of Applied Medical Sciences, Khamis Mushait Campus, King Khalid University, Abha, Saudi Arabia
| | | | - Sarah Salah Jalal
- College of Pharmacy, National University of Science and Technology, Dhi Qar, Iraq
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, the Islamic University, Najaf, Iraq
- College of Technical Engineering, the Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
- College of Technical Engineering, the Islamic University of Babylon, Babylon, Iraq
| | - Ali Alsalamy
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna, Iraq.
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9
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Zhang Y, Wang M, Li Z, Yang X, Li K, Xie A, Dong F, Wang S, Yan J, Liu J. An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs. SCIENCE CHINA. LIFE SCIENCES 2024; 67:1133-1154. [PMID: 38568343 DOI: 10.1007/s11427-023-2522-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/29/2024] [Indexed: 06/07/2024]
Abstract
Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.
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Affiliation(s)
- Yang Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Mengyao Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhenguo Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xuan Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Keqin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ao Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Fang Dong
- College of Life Sciences, Nankai University, Tianjin, 300071, China
| | - Shihan Wang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianxiao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
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10
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Cui Y, Liu H, Ming Y, Zhang Z, Liu L, Liu R. Prediction of strand-specific and cell-type-specific G-quadruplexes based on high-resolution CUT&Tag data. Brief Funct Genomics 2024; 23:265-275. [PMID: 37357985 DOI: 10.1093/bfgp/elad024] [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: 03/17/2023] [Revised: 05/20/2023] [Accepted: 06/01/2023] [Indexed: 06/27/2023] Open
Abstract
G-quadruplex (G4), a non-classical deoxyribonucleic acid structure, is widely distributed in the genome and involved in various biological processes. In vivo, high-throughput sequencing has indicated that G4s are significantly enriched at functional regions in a cell-type-specific manner. Therefore, the prediction of G4s based on computational methods is necessary instead of the time-consuming and laborious experimental methods. Recently, G4 CUT&Tag has been developed to generate higher-resolution sequencing data than ChIP-seq, which provides more accurate training samples for model construction. In this paper, we present a new dataset construction method based on G4 CUT&Tag sequencing data and an XGBoost prediction model based on the machine learning boost method. The results show that our model performs well within and across cell types. Furthermore, sequence analysis indicates that the formation of G4 structure is greatly affected by the flanking sequences, and the GC content of the G4 flanking sequences is higher than non-G4. Moreover, we also identified G4 motifs in the high-resolution dataset, among which we found several motifs for known transcription factors (TFs), such as SP2 and BPC. These TFs may directly or indirectly affect the formation of the G4 structure.
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Affiliation(s)
- Yizhi Cui
- School of Computer Science and Engineering, Beijing Technology and Business University, Beijing, 100048, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, Zhejiang, China
| | - Hongzhi Liu
- School of Computer Science and Engineering, Beijing Technology and Business University, Beijing, 100048, China
| | - Yutong Ming
- School of Computer Science and Engineering, Beijing Technology and Business University, Beijing, 100048, China
| | - Zheng Zhang
- Department of Computer Science and Software Engineering, Auburn University, Auburn, 36830, Alabama, USA
| | - Li Liu
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, Zhejiang, China
| | - Ruijun Liu
- School of Computer Science and Engineering, Beijing Technology and Business University, Beijing, 100048, China
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11
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Zhou W, Liu H, Zhou R, Li J, Ahmadi S. An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines. Heliyon 2024; 10:e30363. [PMID: 38694116 PMCID: PMC11061734 DOI: 10.1016/j.heliyon.2024.e30363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/03/2024] Open
Abstract
Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.
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Affiliation(s)
- Wei Zhou
- Southwest Medical University, Clinical Medicine School, Luzhou, 646000, Sichuan, China
- People's Hospital of Leshan, Department of Cardiology, Leshan, 614000, Sichuan, China
| | - Hongbo Liu
- People's Hospital of Leshan, Department of Cardiology, Leshan, 614000, Sichuan, China
| | - Rui Zhou
- People's Hospital of Leshan, Department of Cardiology, Leshan, 614000, Sichuan, China
| | - Jiafu Li
- The Affiliated Hospital of Southwest Medical University, Department of Cardiology, Luzhou, 646000, Sichuan, China
| | - Sina Ahmadi
- Master of Science of Information Technology Engineering, Department of Computer Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran
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12
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He T, Gao Z, Lin L, Zhang X, Zou Q. Prognostic signature analysis and survival prediction of esophageal cancer based on N6-methyladenosine associated lncRNAs. Brief Funct Genomics 2024; 23:239-248. [PMID: 37465899 DOI: 10.1093/bfgp/elad028] [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: 04/22/2022] [Revised: 06/27/2023] [Accepted: 07/04/2023] [Indexed: 07/20/2023] Open
Abstract
Esophageal cancer (ESCA) has a bad prognosis. Long non-coding RNA (lncRNA) impacts on cell proliferation. However, the prognosis function of N6-methyladenosine (m6A)-associated lncRNAs (m6A-lncRNAs) in ESCA remains unknown. Univariate Cox analysis was applied to investigate prognosis related m6A-lncRNAs, based on which the samples were clustered. Wilcoxon rank and Chi-square tests were adopted to compare the clinical traits, survival, pathway activity and immune infiltration in different clusters where overall survival, clinical traits (N stage), tumor-invasive immune cells and pathway activity were found significantly different. Through least absolute shrinkage and selection operator and proportional hazard (Lasso-Cox) model, five m6A-lncRNAs were selected to construct the prognostic signature (m6A-lncSig) and risk score. To investigate the link between risk score and clinical traits or immunological microenvironments, Chi-square test and Spearman correlation analysis were utilized. Risk score was found connected with N stage, tumor stage, different clusters, macrophages M2, B cells naive and T cells CD4 memory resting. Risk score and tumor stage were found as independent prognostic variables. And the constructed nomogram model had high accuracy in predicting prognosis. The obtained m6A-lncSig could be taken as potential prognostic biomarker for ESCA patients. This study offers a theoretical foundation for clinical diagnosis and prognosis of ESCA.
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Affiliation(s)
- Ting He
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
| | - Zhipeng Gao
- Beidahuang Industry Group General Hospital, Harbin 150000, China
| | - Ling Lin
- Yucai School Attached to Sichuan Chengdu No. 7 High School, Chengdu 610503, China
| | - Xu Zhang
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611730, China
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
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13
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Wei H, Gao L, Wu S, Jiang Y, Liu B. DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae306. [PMID: 38715444 DOI: 10.1093/bioinformatics/btae306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/19/2024] [Accepted: 05/05/2024] [Indexed: 05/30/2024]
Abstract
MOTIVATION Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. RESULTS In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. AVAILABILITY AND IMPLEMENTATION Datasets and source codes are available at https://github.com/Biohang/DiSMVC.
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Affiliation(s)
- Hang Wei
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
| | - Shuai Wu
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
| | - Yina Jiang
- Department of Basic Medicine, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi 712046, China
| | - Bin Liu
- Faculty of Engineering, Shenzhen MSU-BIT University, Shenzhen, Guangdong 518172, China
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
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14
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Laylani LAASS, Al-dolaimy F, Altharawi A, Sulaman GM, Mustafa MA, Alkhafaji AT, Alkhatami AG. Electrochemical DNA-nano biosensor for the detection of Goserelin as anticancer drug using modified pencil graphite electrode. Front Oncol 2024; 14:1321557. [PMID: 38751811 PMCID: PMC11094254 DOI: 10.3389/fonc.2024.1321557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/22/2024] [Indexed: 05/18/2024] Open
Abstract
Goserelin is an effective anticancer drug, but naturally causes several side effects. Hence the determination of this drug in biological samples, plays a key role in evaluating its effects and side effects. The current studies have concentrated on monitoring Goserelin using an easy and quick DNA biosensor for the first time. In this study, copper(II) oxide nanoparticles were created upon the surface of multiwalled carbon nanotubes (CuO/MWCNTs) as a conducting mediator. The modified pencil graphite electrode (ds-DNA/PA/CuO/MWCNTs/PGE) has been modified with the help of polyaniline (PA), ds-DNA, and CuO/MWCNTs nanocomposite. Additionally, the issue with the bio-electroanalytical guanine oxidation signal in relation to ds-DNA at the surface of PA/CuO/MWCNTs/PGE has been examined to determination Goserelin for the first time. It also, established a strong conductive condition to determination Goserelin in nanomolar concentration. Thus, Goserelin's determining, however, has a 0.21 nM detection limit and a 1.0 nM-110.0 µM linear dynamic range according to differential pulse voltammograms (DPV) of ds-DNA/PA/CuO/MWCNTs/PGE. Furthermore, the molecular docking investigation highlighted that Goserelin is able to bind ds-DNA preferentially and supported the findings of the experiments. The determining of Goserelin in real samples has been effectively accomplished in the last phase using ds-DNA/PA/CuO/MWCNTs/PGE.
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Affiliation(s)
| | - F. Al-dolaimy
- Community Health Department, Al-Zahraa University for Women, Karbala, Iraq
| | - Ali Altharawi
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Ghasen M. Sulaman
- Department of Medical Laboratories, Sawa University, Almuthana, Iraq
| | - Mohammed Ahmed Mustafa
- Department of Medical Laboratory Technology, University of Imam Jaafar AL-Sadiq, Baghdad, Iraq
| | | | - Ali G. Alkhatami
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
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15
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Habeeb Naser I, Ali Naeem Y, Ali E, Yarab Hamed A, Farhan Muften N, Turky Maan F, Hussein Mohammed I, Mohammad Ali Khalil NA, Ahmad I, Abed Jawad M, Elawady A. Revolutionizing Infection Control: Harnessing MXene-Based Nanostructures for Versatile Antimicrobial Strategies and Healthcare Advancements. Chem Biodivers 2024; 21:e202400366. [PMID: 38498805 DOI: 10.1002/cbdv.202400366] [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: 02/12/2024] [Revised: 03/18/2024] [Accepted: 03/18/2024] [Indexed: 03/20/2024]
Abstract
The escalating global health challenge posed by infections prompts the exploration of innovative solutions utilizing MXene-based nanostructures. Societally, the need for effective antimicrobial strategies is crucial for public health, while scientifically, MXenes present promising properties for therapeutic applications, necessitating scalable production and comprehensive characterization techniques. Here we review the versatile physicochemical properties of MXene materials for combatting microbial threats and their various synthesis methods, including etching and top-down or bottom-up techniques. Crucial characterization techniques such as XRD, Raman spectroscopy, SEM/TEM, FTIR, XPS, and BET analysis provide insightful structural and functional attributes. The review highlights MXenes' diverse antimicrobial mechanisms, spanning membrane disruption and oxidative stress induction, demonstrating efficacy against bacterial, viral, and fungal infections. Despite translational hurdles, MXene-based nanostructures offer broad-spectrum antimicrobial potential, with applications in drug delivery and diagnostics, presenting a promising path for advancing infection control in global healthcare.
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Affiliation(s)
- Israa Habeeb Naser
- Medical Laboratories Techniques Department, AL-Mustaqbal University, 51001, Hillah, Babil, Iraq
| | - Youssef Ali Naeem
- Department of Medical Laboratories Technology, Al-Manara College for Medical Sciences, Maysan, Iraq
| | - Eyhab Ali
- Al-Zahraa University for Women, Karbala, Iraq
| | | | - Nafaa Farhan Muften
- Department of Medical Laboratories Technology, Mazaya University College, Iraq
| | - Fadhil Turky Maan
- College of Health and Medical Technologies, Al-Esraa University, Baghdad, Iraq
| | | | | | - Irfan Ahmad
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Mohammed Abed Jawad
- Department of Medical Laboratories Technology, Al-Nisour University College, Baghdad, Iraq
| | - Ahmed Elawady
- College of Technical Engineering, The Islamic University, Najaf, Iraq
- College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
- College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq
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16
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Jetti R, Vaca Cárdenas ML, Al-Saedi HFS, Hussein SA, Abdulridui HA, Al-Abdeen SHZ, Radi UK, Abdulkadhim AH, Hussein SB, Alawadi A, Alsalamy A. Ultrasonic synthesis of green lipid nanocarriers loaded with Scutellaria barbata extract: a sustainable approach for enhanced anticancer and antibacterial therapy. Bioprocess Biosyst Eng 2024:10.1007/s00449-024-03021-4. [PMID: 38647679 DOI: 10.1007/s00449-024-03021-4] [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: 01/20/2024] [Accepted: 04/10/2024] [Indexed: 04/25/2024]
Abstract
Ultrasonic manufacturing has emerged as a promising eco-friendly approach to synthesize lipid-based nanocarriers for targeted drug delivery. This study presents the novel ultrasonic preparation of lipid nanocarriers loaded with Scutellaria barbata extract, repurposed for anticancer and antibacterial use. High-frequency ultrasonic waves enabled the precise self-assembly of DSPE-PEG, Span 40, and cholesterol to form nanocarriers encapsulating the therapeutic extract without the use of toxic solvents, exemplifying green nanotechnology. Leveraging the inherent anticancer and antibacterial properties of Scutellaria barbata, the study demonstrates that lipid encapsulation enhances the bioavailability and controlled release of the extract, which is vital for its therapeutic efficacy. Dynamic light scattering and transmission electron microscopy analyses confirmed the increase in size and successful encapsulation post-loading, along with an augmented negative zeta potential indicating enhanced stability. A high encapsulation efficiency of 91.93% was achieved, and in vitro assays revealed the loaded nanocarriers' optimized release kinetics and improved antimicrobial potency against Pseudomonas aeruginosa, compared to the free extract. The combination of ultrasonic synthesis and Scutellaria barbata in an eco-friendly manufacturing process not only advances green nanotechnology but also contributes to sustainable practices in pharmaceutical manufacturing. The data suggest that this innovative nanocarrier system could provide a robust platform for the development of nanotechnology-based therapeutics, enhancing drug delivery efficacy while aligning with environmental sustainability.
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Affiliation(s)
- Raghu Jetti
- Department of Basic Medical Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Maritza Lucia Vaca Cárdenas
- Facultad de Ciencias Pecuarias, Escuela Superior Politécnica de Chimborazo (ESPOCH), Panamericana Sur Km 1½, Riobamba, 060155, Ecuador
| | | | | | | | | | - Usama Kadem Radi
- College of Pharmacy, National University of Science and Technology, Dhi Qar, Iraq
| | - Adnan Hashim Abdulkadhim
- Department of Computer Engineering, Technical Engineering College, Al-Ayen University, Dhi Qar, Iraq
| | | | - Ahmed Alawadi
- College of Technical Engineering, The Islamic University, Najaf, Iraq.
- College of Technical Engineering, The Islamic University of Al-Diwaniyah, Al-Diwaniyah, Iraq.
- College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq.
| | - Ali Alsalamy
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna, 66002, Iraq
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17
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Kamil Zaidan H, Jasim Al-Khafaji HH, Al-Dolaimy F, Abed Hussein S, Otbah Farqad R, Thabit D, Talib Kareem A, Ramadan MF, Hamood SA, Alawadi AH, Alsaalamy A. Exploring the Therapeutic Potential of Lawsone and Nanoparticles in Cancer and Infectious Disease Management. Chem Biodivers 2024; 21:e202301777. [PMID: 38373183 DOI: 10.1002/cbdv.202301777] [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: 11/09/2023] [Revised: 02/09/2024] [Accepted: 02/17/2024] [Indexed: 02/21/2024]
Abstract
Lawsone, a naturally occurring compound found in henna, has been used in traditional medicine for centuries due to its diverse biological activities. In recent years, its nanoparticle-based structure has gained attention in cancer and infectious disease research. This review explores the therapeutic potential of lawsone and its nanoparticles in the context of cancer and infectious diseases. Lawsone exhibits promising anticancer properties by inducing apoptosis and inhibiting cell proliferation, while its nanoparticle formulations enhance targeted delivery and efficacy. Moreover, lawsone demonstrates significant antimicrobial effects against various pathogens. The unique physicochemical properties of lawsone nanoparticles enable efficient cellular uptake and targeted delivery. Potential applications in combination therapy and personalized medicine open new avenues for cancer and infectious disease treatment. While clinical trials are needed to validate their safety and efficacy, lawsone-based nanoparticles offer hope in addressing unmet medical needs and revolutionizing therapeutic approaches.
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Affiliation(s)
| | | | | | - Shaymaa Abed Hussein
- Department of Medical Engineering, Al-Manara College for Medical Sciences, Maysan, Iraq
| | | | - Daha Thabit
- Medical Technical College, Al-Farahidi University, Baghdad, Iraq
| | - Ashwaq Talib Kareem
- College of Pharmacy, National University of Science and Technology, Dhi Qar, Iraq
| | | | - Sarah A Hamood
- Department of Medical Engineering, Al-Esraa University College, Baghdad, Iraq
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, The Islamic University, Najaf, Iraq
- College of Technical Engineering, The Islamic University of Al Diwaniyah, Qadisiyyah, Iraq
- College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq
| | - Ali Alsaalamy
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna, Iraq
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18
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Al-Dolaimy F, Saraswat SK, Hussein BA, Hussein UAR, Saeed SM, Kareem AT, Abdulwahid AS, Mizal TL, Muzammil K, Alawadi AH, Alsalamy A, Hussin F, Kzarb MH. A review of recent advancement in covalent organic framework (COFs) synthesis and characterization with a focus on their applications in antibacterial activity. Micron 2024; 179:103595. [PMID: 38341939 DOI: 10.1016/j.micron.2024.103595] [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: 11/19/2023] [Revised: 01/13/2024] [Accepted: 01/29/2024] [Indexed: 02/13/2024]
Abstract
The primary objective of this review is to present a comprehensive examination of the synthesis, characterization, and antibacterial applications of covalent organic frameworks (COFs). COFs represent a distinct category of porous materials characterized by a blend of advantageous features, including customizable pore dimensions, substantial surface area, and adaptable chemical properties. These attributes position COFs as promising contenders for various applications, notably in the realm of antibacterial activity. COFs exhibit considerable potential in the domain of antibacterial applications, owing to their amenability to functionalization with antibacterial agents. The scientific community is actively exploring COFs that have been imbued with metal ions, such as copper or silver, given their observed robust antibacterial properties. These investigations strongly suggest that COFs could be harnessed effectively as potent antibacterial agents across a diverse array of applications. Finally, COFs hold immense promise as a novel class of materials for antibacterial applications, shedding light on the synthesis, characterization, and functionalization of COFs tailored for specific purposes. The potential of COFs as effective antibacterial agents beckons further exploration and underscores their potential to revolutionize antibacterial strategies in various domains.
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Affiliation(s)
| | | | - Baydaa Abed Hussein
- Department of Medical Engineering, Al-Manara College for Medical Sciences, Maysan, Amarah, Iraq.
| | | | | | - Ashwaq Talib Kareem
- College of Pharmacy, National University of Science and Technology, Dhi Qar, Iraq.
| | | | - Thair L Mizal
- Department of Medical Engineering, Al-Esraa University College, Baghdad, Iraq.
| | - Khursheed Muzammil
- Department of Public Health, College of Applied Medical Sciences, Khamis Mushait Campus, King Khalid University, Abha, KSA.
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, the Islamic University, Najaf, Iraq; College of Technical Engineering, the Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq; College of technical engineering, the Islamic University of Babylon, Najaf, Iraq.
| | - Ali Alsalamy
- College of technical engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq.
| | - Farah Hussin
- Medical Technical College, Al-Farahidi University, Baghdad, Iraq.
| | - Mazin Hadi Kzarb
- College of Physical Education and Sport Sciences, Al-Mustaqbal University, 51001 Hillah, Babil, Iraq.
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19
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Chen M, Sun M, Su X, Tiwari P, Ding Y. Fuzzy kernel evidence Random Forest for identifying pseudouridine sites. Brief Bioinform 2024; 25:bbae169. [PMID: 38622357 PMCID: PMC11018548 DOI: 10.1093/bib/bbae169] [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: 01/18/2024] [Revised: 03/27/2024] [Accepted: 03/31/2024] [Indexed: 04/17/2024] Open
Abstract
Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.
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Affiliation(s)
- Mingshuai Chen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China
| | - Mingai Sun
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Xi Su
- Foshan Women and Children Hospital, Foshan 528000, China
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China
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20
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Zhang ZY, Sun ZJ, Gao D, Hao YD, Lin H, Liu F. Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression. IET Syst Biol 2024. [PMID: 38530028 DOI: 10.1049/syb2.12090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/06/2024] [Accepted: 03/10/2024] [Indexed: 03/27/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.
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Affiliation(s)
- Zhao-Yue Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Zi-Jie Sun
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dong Gao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu-Duo Hao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fen Liu
- Department of Radiation Oncology, Peking University Cancer Hospital (Inner Mongolia Campus), Affiliated Cancer Hospital of Inner Mongolia Medical University, Inner Mongolia Cancer Hospital, Hohhot, China
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21
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Zou Y, Sun X, Wang Y, Wang Y, Ye X, Tu J, Yu R, Huang P. Integrating single-cell RNA sequencing data to genome-wide association analysis data identifies significant cell types in influenza A virus infection and COVID-19. Brief Funct Genomics 2024; 23:110-117. [PMID: 37340787 DOI: 10.1093/bfgp/elad025] [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: 07/05/2022] [Revised: 02/23/2023] [Accepted: 06/01/2023] [Indexed: 06/22/2023] Open
Abstract
With the global pandemic of COVID-19, the research on influenza virus has entered a new stage, but it is difficult to elucidate the pathogenesis of influenza disease. Genome-wide association studies (GWASs) have greatly shed light on the role of host genetic background in influenza pathogenesis and prognosis, whereas single-cell RNA sequencing (scRNA-seq) has enabled unprecedented resolution of cellular diversity and in vivo following influenza disease. Here, we performed a comprehensive analysis of influenza GWAS and scRNA-seq data to reveal cell types associated with influenza disease and provide clues to understanding pathogenesis. We downloaded two GWAS summary data, two scRNA-seq data on influenza disease. After defining cell types for each scRNA-seq data, we used RolyPoly and LDSC-cts to integrate GWAS and scRNA-seq. Furthermore, we analyzed scRNA-seq data from the peripheral blood mononuclear cells (PBMCs) of a healthy population to validate and compare our results. After processing the scRNA-seq data, we obtained approximately 70 000 cells and identified up to 13 cell types. For the European population analysis, we determined an association between neutrophils and influenza disease. For the East Asian population analysis, we identified an association between monocytes and influenza disease. In addition, we also identified monocytes as a significantly related cell type in a dataset of healthy human PBMCs. In this comprehensive analysis, we identified neutrophils and monocytes as influenza disease-associated cell types. More attention and validation should be given in future studies.
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Affiliation(s)
- Yixin Zou
- Department of Epidemiology, National Vaccine Innovation Platform, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xifang Sun
- Department of Mathematics, School of Science, Xi'an Shiyou University, Xi'an, China
| | - Yifan Wang
- Department of Infectious Disease, Jurong Hospital Affiliated to Jiangsu University, Jurong, China
| | - Yidi Wang
- Department of Epidemiology, National Vaccine Innovation Platform, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xiangyu Ye
- Department of Epidemiology, National Vaccine Innovation Platform, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Junlan Tu
- Department of Epidemiology, National Vaccine Innovation Platform, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Rongbin Yu
- Department of Epidemiology, National Vaccine Innovation Platform, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Peng Huang
- Department of Epidemiology, National Vaccine Innovation Platform, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
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22
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Saadh MJ, Shallan MA, Hussein UAR, Mohammed AQ, Al-Shuwaili SJ, Shikara M, Ami AA, Khalil NAMA, Ahmad I, Abbas HH, Elawady A. Advances in microscopy characterization techniques for lipid nanocarriers in drug delivery: a comprehensive review. NAUNYN-SCHMIEDEBERG'S ARCHIVES OF PHARMACOLOGY 2024:10.1007/s00210-024-03033-7. [PMID: 38459989 DOI: 10.1007/s00210-024-03033-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/28/2024] [Indexed: 03/11/2024]
Abstract
This review paper provides an in-depth analysis of the significance of lipid nanocarriers in drug delivery and the crucial role of characterization techniques. It explores various types of lipid nanocarriers and their applications, emphasizing the importance of microscopy-based characterization methods such as light microscopy, confocal microscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy (AFM). The paper also delves into sample preparation, quantitative analysis, challenges, and future directions in the field. The review concludes by underlining the pivotal role of microscopy-based characterization in advancing lipid nanocarrier research and drug delivery technologies.
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Affiliation(s)
- Mohamed J Saadh
- Faculty of Pharmacy, Middle East University, Amman, 11831, Jordan
| | | | | | | | | | | | - Ahmed Ali Ami
- Department of Medical Laboratories Technology, Al-Nisour University College, Baghdad, Iraq
| | | | - Irfan Ahmad
- Department of Clinical Laboratory Sciences, College of Applied Medical Science, King Khalid University, Abha, Saudi Arabia
| | - Huda Hayder Abbas
- College of Pharmacy, National University of Science and Technology, Dhi Qar, Iraq
| | - Ahmed Elawady
- College of Technical Engineering, The Islamic University, Najaf, Iraq.
- College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq.
- College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq.
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23
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Ju H, Cui Y, Su Q, Juan L, Manavalan B. CODENET: A deep learning model for COVID-19 detection. Comput Biol Med 2024; 171:108229. [PMID: 38447500 DOI: 10.1016/j.compbiomed.2024.108229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/20/2024] [Accepted: 02/25/2024] [Indexed: 03/08/2024]
Abstract
Conventional COVID-19 testing methods have some flaws: they are expensive and time-consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some extent. However, there is no accurate and practical automatic diagnostic framework with good interpretability. The application of artificial intelligence (AI) technology to medical radiography can help to accurately detect the disease, reduce the burden on healthcare organizations, and provide good interpretability. Therefore, this study proposes a new deep neural network (CNN) based on CXR for COVID-19 diagnosis - CodeNet. This method uses contrastive learning to make full use of latent image data to enhance the model's ability to extract features and generalize across different data domains. On the evaluation dataset, the proposed method achieves an accuracy as high as 94.20%, outperforming several other existing methods used for comparison. Ablation studies validate the efficacy of the proposed method, while interpretability analysis shows that the method can effectively guide clinical professionals. This work demonstrates the superior detection performance of a CNN using contrastive learning techniques on CXR images, paving the way for computer vision and artificial intelligence technologies to leverage massive medical data for disease diagnosis.
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Affiliation(s)
- Hong Ju
- Heilongjiang Agricultural Engineering Vocational College, China
| | - Yanyan Cui
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Qiaosen Su
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
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24
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Wang T, Yan Z, Zhang Y, Lou Z, Zheng X, Mai D, Wang Y, Shang X, Xiao B, Peng J, Chen J. postGWAS: A web server for deciphering the causality post the genome-wide association studies. Comput Biol Med 2024; 171:108108. [PMID: 38359659 DOI: 10.1016/j.compbiomed.2024.108108] [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: 12/01/2023] [Revised: 01/23/2024] [Accepted: 02/04/2024] [Indexed: 02/17/2024]
Abstract
While genome-wide association studies (GWAS) have unequivocally identified vast disease susceptibility variants, a majority of them are situated in non-coding regions and are in high linkage disequilibrium (LD). To pave the way of translating GWAS signals to clinical drug targets, it is essential to identify the underlying causal variants and further causal genes. To this end, a myriad of post-GWAS methods have been devised, each grounded in distinct principles including fine-mapping, co-localization, and transcriptome-wide association study (TWAS) techniques. Yet, no platform currently exists that seamlessly integrates these diverse post-GWAS methodologies. In this work, we present a user-friendly web server for post-GWAS analysis, that seamlessly integrates 9 distinct methods with 12 models, categorized by fine-mapping, colocalization, and TWAS. The server mainly helps users decipher the causality hindered by complex GWAS signals, including casual variants and casual genes, without the burden of computational skills and complex environment configuration, and provides a convenient platform for post-GWAS analysis, result visualization, facilitating the understanding and interpretation of the genome-wide association studies. The postGWAS server is available at http://g2g.biographml.com/.
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Affiliation(s)
- Tao Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Zhihao Yan
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Yiming Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Zhuofei Lou
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Xiaozhu Zheng
- Department of Anesthesiology, The People's Hospital of Yubei District, Chongqing, 401120, China
| | - DuoDuo Mai
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Yongtian Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Bing Xiao
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Jing Chen
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, China.
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25
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Ahmad I, Al-Dolaimy F, Kzar MH, Kareem AT, Mizal TL, Omran AA, Alazbjee AAA, Obaidur Rab S, Eskandar M, Alawadi AH, Alsalamy A. Microfluidic-based nanoemulsion of Ocimum basilicum extract: Constituents, stability, characterization, and potential biomedical applications for improved antimicrobial and anticancer properties. Microsc Res Tech 2024; 87:411-423. [PMID: 37877737 DOI: 10.1002/jemt.24444] [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: 07/27/2023] [Revised: 09/29/2023] [Accepted: 10/12/2023] [Indexed: 10/26/2023]
Abstract
This paper reports on the findings from a study that aimed to identify and characterize the constituents of Ocimum basilicum extract using gas chromatography-mass spectrometry (GC-MS) analysis, as well as assess the physicochemical properties and stability of nanoemulsions formulated with O. basilicum extract. The GC-MS analysis revealed that the O. basilicum extract contained 22 components, with Caryophyllene and Naringenin identified as the primary active constituents. The nanoemulsion formulation demonstrated excellent potential for use in the biomedical field, with a small and uniform particle size distribution, a negative zeta potential, and high encapsulation efficiency for the O. basilicum extract. The nanoemulsions exhibited spherical morphology and remained physically stable for up to 6 months. In vitro release studies indicated sustained release of the extract from the nanoemulsion formulation compared to the free extract solution. Furthermore, the developed nanoformulation exhibited enhanced anticancer properties against K562 cells while demonstrating low toxicity in normal cells (HEK293). The O. basilicum extract demonstrated antimicrobial activity against Pseudomonas aeruginosa, Candida albicans, and Staphylococcus epidermidis, with a potential synergistic effect observed when combined with the nanoemulsion. These findings contribute to the understanding of the constituents and potential applications of O. basilicum extract and its nanoemulsion formulation in various fields, including healthcare and pharmaceutical industries. Further optimization and research are necessary to maximize the efficacy and antimicrobial activity of the extract and its nanoformulation. RESEARCH HIGHLIGHTS: This study characterized the constituents of O. basilicum extract and assessed the physicochemical properties and stability of its nanoemulsion formulation. The O. basilicum extract contained 22 components, with Caryophyllene and Naringenin identified as the primary active constituents. The nanoemulsion formulation demonstrated excellent potential for biomedical applications, with sustained release of the extract, low toxicity, and enhanced anticancer and antimicrobial properties. The findings contribute to the understanding of the potential applications of O. basilicum extract and its nanoemulsion formulation in healthcare and pharmaceutical industries, highlighting the need for further optimization and research.
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Affiliation(s)
- Irfan Ahmad
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | | | - Mazin Hadi Kzar
- College of Physical Education and Sport Sciences, Al-Mustaqbal University, Hillah, Babil, Iraq
| | - Ashwaq Talib Kareem
- College of Pharmacy, National University of Science and Technology, Dhi Qar, Iraq
| | - Thair L Mizal
- Department of Medical Engineering, Al-Esraa University College, Baghdad, Iraq
| | - Aisha A Omran
- Department of Medical Engineering, AL-Nisour University College, Baghdad, Iraq
| | | | - Safia Obaidur Rab
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Mamdoh Eskandar
- Department of Obstetrics and Gynecology, College of Medicine, King Khalid University, Abha, Saudi Arabia
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, The Islamic University, Najaf, Iraq
- College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
- College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq
| | - Ali Alsalamy
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna, Iraq
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26
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Paknia F, Roostaee M, Isaei E, Mashhoori MS, Sargazi G, Barani M, Amirbeigi A. Role of Metal-Organic Frameworks (MOFs) in treating and diagnosing microbial infections. Int J Biol Macromol 2024; 262:130021. [PMID: 38331063 DOI: 10.1016/j.ijbiomac.2024.130021] [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: 07/31/2023] [Revised: 01/22/2024] [Accepted: 02/05/2024] [Indexed: 02/10/2024]
Abstract
This review article highlights the innovative role of metal-organic frameworks (MOFs) in addressing global healthcare challenges related to microbial infections. MOFs, comprised of metal nodes and organic ligands, offer unique properties that can be applied in the treatment and diagnosis of these infections. Traditional methods, such as antibiotics and conventional diagnostics, face issues such as antibiotic resistance and diagnostic limitations. MOFs, with their highly porous and customizable structure, can encapsulate and deliver therapeutic or diagnostic molecules precisely. Their large surface area and customizable pore structures allow for sensitive detection and selective recognition of microbial pathogens. They also show potential in delivering therapeutic agents to infection sites, enabling controlled release and possible synergistic effects. However, challenges like optimizing synthesis techniques, enhancing stability, and developing targeted delivery systems remain. Regulatory and safety considerations for clinical translation also need to be addressed. This review not only explores the potential of MOFs in treating and diagnosing microbial infections but also emphasizes their unique approach and discusses existing challenges and future directions.
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Affiliation(s)
- Fatemeh Paknia
- Department of Nanobiotechnology, Faculty of Biological Sciences, Tarbiat Modares University, Tehran 14115-154, Iran
| | - Maryam Roostaee
- Department of Chemistry, Faculty of Sciences, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
| | - Elham Isaei
- Noncommunicable Diseases Research Center, Bam University of Medical Sciences, Bam, Iran.
| | - Mahboobeh-Sadat Mashhoori
- Department of Chemistry, Faculty of Science, University of Birjand, P.O.Box 97175-615, Birjand, Iran
| | - Ghasem Sargazi
- Noncommunicable Diseases Research Center, Bam University of Medical Sciences, Bam, Iran
| | - Mahmood Barani
- Student Research Committee, Kerman University of Medical Sciences, Kerman 7616913555, Iran; Medical Mycology and Bacteriology Research Center, Kerman University of Medical Sciences, Kerman 7616913555, Iran.
| | - Alireza Amirbeigi
- Department of General Surgery, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran.
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27
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Zhang HQ, Liu SH, Li R, Yu JW, Ye DX, Yuan SS, Lin H, Huang CB, Tang H. MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier. ACS OMEGA 2024; 9:8439-8447. [PMID: 38405489 PMCID: PMC10882704 DOI: 10.1021/acsomega.3c09587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/27/2024]
Abstract
In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.
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Affiliation(s)
- Hong-Qi Zhang
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shang-Hua Liu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Rui Li
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Jun-Wen Yu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Dong-Xin Ye
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shi-Shi Yuan
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Hao Lin
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School
of Computer Science and Technology, Aba Teachers University, Aba 623002, China
| | - Hua Tang
- School
of Basic Medical Sciences, Southwest Medical
University, Luzhou 646000, China
- Central
Nervous System Drug Key Laboratory of Sichuan Province, Luzhou 646000, China
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28
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Duan H, Zhang Y, Qiu H, Fu X, Liu C, Zang X, Xu A, Wu Z, Li X, Zhang Q, Zhang Z, Cui F. Machine learning-based prediction model for distant metastasis of breast cancer. Comput Biol Med 2024; 169:107943. [PMID: 38211382 DOI: 10.1016/j.compbiomed.2024.107943] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/10/2023] [Accepted: 01/01/2024] [Indexed: 01/13/2024]
Abstract
BACKGROUND Breast cancer is the most prevalent malignancy in women. Advanced breast cancer can develop distant metastases, posing a severe threat to the life of patients. Because the clinical warning signs of distant metastasis are manifested in the late stage of the disease, there is a need for better methods of predicting metastasis. METHODS First, we screened breast cancer distant metastasis target genes by performing difference analysis and weighted gene co-expression network analysis (WGCNA) on the selected datasets, and performed analyses such as GO enrichment analysis on these target genes. Secondly, we screened breast cancer distant metastasis target genes by LASSO regression analysis and performed correlation analysis and other analyses on these biomarkers. Finally, we constructed several breast cancer distant metastasis prediction models based on Logistic Regression (LR) model, Random Forest (RF) model, Support Vector Machine (SVM) model, Gradient Boosting Decision Tree (GBDT) model and eXtreme Gradient Boosting (XGBoost) model, and selected the optimal model from them. RESULTS Several 21-gene breast cancer distant metastasis prediction models were constructed, with the best performance of the model constructed based on the random forest model. This model accurately predicted the emergence of distant metastases from breast cancer, with an accuracy of 93.6 %, an F1-score of 88.9 % and an AUC value of 91.3 % on the validation set. CONCLUSION Our findings have the potential to be translated into a point-of-care prognostic analysis to reduce breast cancer mortality.
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Affiliation(s)
- Hao Duan
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Yu Zhang
- Beidahuang Industry Group General Hospital, Harbin, 150001, China
| | - Haoye Qiu
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Xiuhao Fu
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Chunling Liu
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Xiaofeng Zang
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Anqi Xu
- The First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China
| | - Ziyue Wu
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Xingfeng Li
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Qingchen Zhang
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China.
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China.
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29
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Fu X, Yuan Y, Qiu H, Suo H, Song Y, Li A, Zhang Y, Xiao C, Li Y, Dou L, Zhang Z, Cui F. AGF-PPIS: A protein-protein interaction site predictor based on an attention mechanism and graph convolutional networks. Methods 2024; 222:142-151. [PMID: 38242383 DOI: 10.1016/j.ymeth.2024.01.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/04/2024] [Accepted: 01/13/2024] [Indexed: 01/21/2024] Open
Abstract
Protein-protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein-protein interactions sites is crucial. In this paper, we propose a novel predictor for protein-protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https://github.com/fxh1001/AGF-PPIS.
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Affiliation(s)
- Xiuhao Fu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Ye Yuan
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Haoye Qiu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Haodong Suo
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yingying Song
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Anqi Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yupeng Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Cuilin Xiao
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yazi Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Lijun Dou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland, OH 44106, USA
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China.
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China.
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30
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Jiang J, Pei H, Li J, Li M, Zou Q, Lv Z. FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization. Brief Bioinform 2024; 25:bbae037. [PMID: 38366802 PMCID: PMC10939380 DOI: 10.1093/bib/bbae037] [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: 08/08/2023] [Revised: 12/27/2023] [Accepted: 01/17/2024] [Indexed: 02/18/2024] Open
Abstract
Anti-coronavirus peptides (ACVPs) represent a relatively novel approach of inhibiting the adsorption and fusion of the virus with human cells. Several peptide-based inhibitors showed promise as potential therapeutic drug candidates. However, identifying such peptides in laboratory experiments is both costly and time consuming. Therefore, there is growing interest in using computational methods to predict ACVPs. Here, we describe a model for the prediction of ACVPs that is based on the combination of feature engineering (FE) optimization and deep representation learning. FEOpti-ACVP was pre-trained using two feature extraction frameworks. At the next step, several machine learning approaches were tested in to construct the final algorithm. The final version of FEOpti-ACVP outperformed existing methods used for ACVPs prediction and it has the potential to become a valuable tool in ACVP drug design. A user-friendly webserver of FEOpti-ACVP can be accessed at http://servers.aibiochem.net/soft/FEOpti-ACVP/.
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Affiliation(s)
- Jici Jiang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Hongdi Pei
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Jiayu Li
- College of Life Science, Sichuan University, Chengdu 610065, China
| | - Mingxin Li
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Zhibin Lv
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
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31
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Jiang X, Mostafa L. Modeling Cu removal from aqueous solution using sawdust based on response surface methodology. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:157. [PMID: 38228806 DOI: 10.1007/s10661-024-12343-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/09/2024] [Indexed: 01/18/2024]
Abstract
Copper (Cu), as one of the heavy metals widely used in industrial and agricultural activities, has a fundamental role in the pollution of water resources. Therefore, removing Cu from the aqueous solutions is considered an important challenge in the purification of water resources. Thus, in this study, sawdust with a diameter of 260-600 μm was used to remove Cu from the aqueous solutions. At first, sawdust was washed using distilled water and dried at laboratory temperature. Cu absorption experiments in closed conditions were performed based on the central composite design (CCD) model and with a range of initial Cu concentrations equal to 1-25 mgl-1. The amount of changes for other variables, including pH, time, and amount of sawdust, was equal to 2-10, 5-185 (min), and 5-25 (gl-1), respectively. After the completion of each test, the remaining Cu concentration in the solution was measured using atomic absorption, and the percentage of Cu removed was determined from the difference between the initial and final concentrations. The results showed that the CCD model has a favorable ability to predict Cu removal from the aqueous solutions (R2=0.90 and RSME=3.34%). Based on the Pareto analysis, contact time, the amount of sawdust, pH, and the Cu concentration had the most significant effect on removing Cu from the solution. Contact time, amount of sawdust, and pH were directly related, and the amount of dissolved Cu was proportional to the removal of Cu from the solution. Therefore, sawdust is desirable as a natural adsorbent, and the removal efficiency of Cu from solutions with low Cu concentration is very high (94%). In this regard, it is advised to use sawdust in the process of targeting Cu and heavy metals due to its low cost and availability.
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Affiliation(s)
- Xiaoxue Jiang
- School of Political Science and Law, Tibet University, Lhasa, 850000, China.
| | - Loghman Mostafa
- Department of Medical Biochemical Analysis, College of Health Technology, Cihan University-Erbil, Erbil, Iraq
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32
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Lin L, Liu Y, Gao M, Rezaeipanah A. Improving hepatocellular carcinoma diagnosis using an ensemble classification approach based on Harris Hawks Optimization. Heliyon 2024; 10:e23497. [PMID: 38169861 PMCID: PMC10758797 DOI: 10.1016/j.heliyon.2023.e23497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 09/20/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Hepato-Cellular Carcinoma (HCC) is the most common type of liver cancer that often occurs in people with chronic liver diseases such as cirrhosis. Although HCC is known as a fatal disease, early detection can lead to successful treatment and improve survival chances. In recent years, the development of computer recognition systems using machine learning approaches has been emphasized by researchers. The effective performance of these approaches for the diagnosis of HCC has been proven in a wide range of applications. With this motivation, this paper proposes a hybrid machine learning approach including effective feature selection and ensemble classification for HCC detection, which is developed based on the Harris Hawks Optimization (HHO) algorithm. The proposed ensemble classifier is based on the bagging technique and is configured based on the decision tree method. Meanwhile, HHO as an emerging meta-heuristic algorithm can select a subset of the most suitable features related to HCC for classification. In addition, the proposed method is equipped with several strategies for handling missing values and data normalization. The simulations are based on the HCC dataset collected by the Coimbra Hospital and University Center (CHUC). The results of the experiments prove the acceptable performance of the proposed method. Specifically, the proposed method with an accuracy of 97.13 % is superior in comparison with the equivalent methods such as LASSO and DTPSO.
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Affiliation(s)
- LiuRen Lin
- Department of Pharmacy and Machinery, Qujing Second People's Hospital, Yunnan, Qujing, 655000, China
| | - YunKuan Liu
- Yunnan University of Chinese Medicine, Yunnan Key Laboratory of External Drug Delivery System and Preparation Technology in Universities, Yunnan, Kunming, 650500, China
| | - Min Gao
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Yunnan, Kunming, 650500, China
| | - Amin Rezaeipanah
- Department of Computer Engineering, Persian Gulf University, Bushehr, Iran
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Tan X, Zhao D, Wang M, Wang X, Wang X, Liu W, Ghobaei-Arani M. A decision-making mechanism for task offloading using learning automata and deep learning in mobile edge networks. Heliyon 2024; 10:e23651. [PMID: 38192752 PMCID: PMC10772128 DOI: 10.1016/j.heliyon.2023.e23651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 11/21/2023] [Accepted: 12/08/2023] [Indexed: 01/10/2024] Open
Abstract
The development of mobile networks has led to the emergence of challenges such as high delays in storage, computing and traffic management. To deal with these challenges, fifth-generation networks emphasize the use of technologies such as mobile cloud computing and mobile edge computing. Mobile Edge Cloud Computing (MECC) is an emerging distributed computing model that provides access to cloud computing services at the edge of the network and near mobile users. With offloading tasks at the edge of the network instead of transferring them to a remote cloud, MECC can realize flexibility and real-time processing. During computation offloading, the requirements of Internet of Things (IoT) applications may change at different stages, which is ignored in existing works. With this motivation, we propose a task offloading method under dynamic resource requirements during the use of IoT applications, which focuses on the problem of workload fluctuations. The proposed method uses a learning automata-based offload decision-maker to offload requests to the edge layer. An auto-scaling strategy is then developed using a long short-term memory network which can estimate the expected number of future requests. Finally, an Asynchronous Advantage Actor-Critic algorithm as a deep reinforcement learning-based approach decides to scale down or scale up. The effectiveness of the proposed method has been confirmed through extensive experiments using the iFogSim simulator. The numerical results show that the proposed method has better scalability and performance in terms of delay and energy consumption than the existing state-of-the-art methods.
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Affiliation(s)
- Xin Tan
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
- Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
| | - DongYan Zhao
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
- Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
| | - MingWei Wang
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
- Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
| | - Xin Wang
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
- Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
| | - XiangHui Wang
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
- Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
| | - WenYuan Liu
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
- Shaanxi Artificial Intelligence Joint Laboratory, Shaanxi University of Science and Technology, Xian 710021, Shaanxi, China
| | - Mostafa Ghobaei-Arani
- Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
- Production and Recycling of Materials and Energy Research Center, Qom Branch, Islamic Azad University, Qom, Iran
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Zhang M, Khosravi Aqdam M, Abbas Fadel H, Wang L, Waheeb K, Kadhim A, Hekmati J. Evaluation of soil fertility using combination of Landsat 8 and Sentinel‑2 data in agricultural lands. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:131. [PMID: 38198078 DOI: 10.1007/s10661-024-12301-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/02/2024] [Indexed: 01/11/2024]
Abstract
Today, remote sensing is widely used to estimate soil properties. Because it is an easy and accessible way to estimate soil properties that are difficult to estimate in the field. Based on this, to evaluate the soil fertility (SF), soil sampling was performed irregularly from the surface depth of 0-30 cm in 216 points, 11 soil properties were measured, and the soil fertility index (SFI) was calculated by soil properties. Simultaneously, we combined satellite images of Landsat 8 and Sentinel-2 using the Gram-Schmidt algorithm. Finally, multiple linear regression SFI was calculated using satellite data, as well as the spatial distribution of SFI was obtained in very low, low, moderate, high, and very high classes. Our findings showed that the combination of Landsat 8 and Sentinel-2 data using the Gram-Schmidt algorithm has a higher correlation with SFI than when these data are individually. Therefore, combined Landsat 8 and Sentinel 2 data were used for SFI modeling. Using model selection procedure indices (including Cp, AIC, and ρc criteria), the visible range bands, notably blue (r = 0.65), green (r = 0.63), and red (r = 0.61), provide the best model for estimating SFI (R2 = 0.43, Cp = 3.34, AIC = -277.4, and ρc = 0.44). Therefore, these bands were used to estimate the SFI index. Also, the spatial distribution of the SIF index showed that the most significant area was related to the low class, and the lowest area belonged to the high and very high fertility classes. According to these results, it can be concluded that using the combination of Landsat 8 and Sentinel 2 bands to estimate soil fertility index in agricultural lands can increase the accuracy of soil fertility estimation.
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Affiliation(s)
- Ming Zhang
- Department of Resources and Environment, Anhui Vocational and Technological College of Forestry, Hefei, Anhui, 230031, China.
| | | | | | - Lei Wang
- Ministry of Ecology and Environment, Nanjing Institute of Environmental Sciences, Nanjing, 210042, China
| | - Khlood Waheeb
- Medical Technical College, Al-Farahidi University, Baghdad, Iraq
| | - Angham Kadhim
- Department of Optical Techniques, Al-Zahrawi University College, Karbala, Iraq
| | - Jamal Hekmati
- Department of Horticultural Sciencess, University Campus 2, University of Guilan, Rasht, Iran
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Cao C, Shao M, Zuo C, Kwok D, Liu L, Ge Y, Zhang Z, Cui F, Chen M, Fan R, Ding Y, Jiang H, Wang G, Zou Q. RAVAR: a curated repository for rare variant-trait associations. Nucleic Acids Res 2024; 52:D990-D997. [PMID: 37831073 PMCID: PMC10767942 DOI: 10.1093/nar/gkad876] [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: 08/15/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.
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Affiliation(s)
- Chen Cao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Mengting Shao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Chunman Zuo
- Institute of Artificial Intelligence, Donghua University, Shanghai, China
| | - Devin Kwok
- School of Computer Science, McGill University, Montreal, Canada
| | - Lin Liu
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Yuli Ge
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Zilong Zhang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Feifei Cui
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Mingshuai Chen
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Rui Fan
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Hangjin Jiang
- Center for Data Science, Zhejiang University, Hangzhou, China
| | - Guishen Wang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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Pan S, Kang H, Liu X, Li S, Yang P, Wu M, Yuan N, Lin S, Zheng Q, Jia P. COLOCdb: a comprehensive resource for multi-model colocalization of complex traits. Nucleic Acids Res 2024; 52:D871-D881. [PMID: 37941154 PMCID: PMC10767919 DOI: 10.1093/nar/gkad939] [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: 08/15/2023] [Revised: 10/01/2023] [Accepted: 10/12/2023] [Indexed: 11/10/2023] Open
Abstract
Large-scale genome-wide association studies (GWAS) have provided profound insights into complex traits and diseases. Yet, deciphering the fine-scale molecular mechanisms of how genetic variants manifest to cause the phenotypes remains a daunting task. Here, we present COLOCdb (https://ngdc.cncb.ac.cn/colocdb), a comprehensive genetic colocalization database by integrating more than 3000 GWAS summary statistics and 13 types of xQTL to date. By employing two representative approaches for the colocalization analysis, COLOCdb deposits results from three key components: (i) GWAS-xQTL, pair-wise colocalization between GWAS loci and different types of xQTL, (ii) GWAS-GWAS, pair-wise colocalization between the trait-associated genetic loci from GWASs and (iii) xQTL-xQTL, pair-wise colocalization between the genetic loci associated with molecular phenotypes in xQTLs. These results together represent the most comprehensive colocalization analysis, which also greatly expands the list of shared variants with genetic pleiotropy. We expect that COLOCdb can serve as a unique and useful resource in advancing the discovery of new biological mechanisms and benefit future functional studies.
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Affiliation(s)
- Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Hongen Kang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xinxuan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Shuhua Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Peng Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Mingqiu Wu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Na Yuan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Shiqi Lin
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Qiwen Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100101, China
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Pang Y, Liu B. DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model. BMC Biol 2024; 22:3. [PMID: 38166858 PMCID: PMC10762911 DOI: 10.1186/s12915-023-01803-y] [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: 06/25/2023] [Accepted: 12/15/2023] [Indexed: 01/05/2024] Open
Abstract
Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.
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Affiliation(s)
- Yihe Pang
- School of Computer Science and Technology, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Beijing, Haidian District, 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Beijing, Haidian District, 100081, China.
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Beijing, Haidian District, 100081, China.
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Shi X, Yue C, Quan M, Li Y, Nashwan Sam H. A semi-supervised ensemble clustering algorithm for discovering relationships between different diseases by extracting cell-to-cell biological communications. J Cancer Res Clin Oncol 2024; 150:3. [PMID: 38168012 DOI: 10.1007/s00432-023-05559-4] [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: 07/14/2023] [Accepted: 11/01/2023] [Indexed: 01/05/2024]
Abstract
INTRODUCTION In recent decades, many theories have been proposed about the cause of hereditary diseases such as cancer. However, most studies state genetic and environmental factors as the most important parameters. It has been shown that gene expression data are valuable information about hereditary diseases and their analysis can identify the relationships between these diseases. OBJECTIVE Identification of damaged genes from various diseases can be done through the discovery of cell-to-cell biological communications. Also, extraction of intercellular communications can identify relationships between different diseases. For example, gene disorders that cause damage to the same cells in both breast and blood cancers. Hence, the purpose is to discover cell-to-cell biological communications in gene expression data. METHODOLOGY The identification of cell-to-cell biological communications for various cancer diseases has been widely performed by clustering algorithms. However, this field remains open due to the abundance of unprocessed gene expression data. Accordingly, this paper focuses on the development of a semi-supervised ensemble clustering algorithm that can discover relationships between different diseases through the extraction of cell-to-cell biological communications. The proposed clustering framework includes a stratified feature sampling mechanism and a novel similarity metric to deal with high-dimensional data and improve the diversity of primary partitions. RESULTS The performance of the proposed clustering algorithm is verified with several datasets from the UCI machine learning repository and then applied to the FANTOM5 dataset to extract cell-to-cell biological communications. The used version of this dataset contains 108 cells and 86,427 promoters from 702 samples. The strength of communication between two similar cells from different diseases indicates the relationship of those diseases. Here, the strength of communication is determined by promoter, so we found the highest cell-to-cell biological communication between "basophils" and "ciliary.epithelial.cells" with 62,809 promoters. CONCLUSION The maximum cell-to-cell biological similarity in each cluster can be used to detect the relationship between different diseases such as cancer.
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Affiliation(s)
- Xiuchao Shi
- College of Environment and Life Sciences, Weinan Normal University, Weinan, 714099, Shaanxi, China.
| | - Chunxiao Yue
- Weinan Junior Middle School, Weinan, 714000, Shaanxi, China
| | - Meiping Quan
- College of Environment and Life Sciences, Weinan Normal University, Weinan, 714099, Shaanxi, China
| | - Yalin Li
- College of Environment and Life Sciences, Weinan Normal University, Weinan, 714099, Shaanxi, China
| | - Hiba Nashwan Sam
- Department of Radiology and Sonar Techniques, Al-Noor University College, Nineveh, Iraq
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Wang D. Toward improving the performance of learning by joining feature selection and ensemble classification techniques: an application for cancer diagnosis. J Cancer Res Clin Oncol 2023; 149:16993-17006. [PMID: 37740767 DOI: 10.1007/s00432-023-05422-6] [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: 07/06/2023] [Accepted: 09/12/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION Breast cancer is known as the most common type of cancer in women, and this has raised the importance of its diagnosis in medical science as one of the most important issues. In addition to reducing costs, the diagnosis of benign or malignant breast cancer is very important in determining the treatment method. OBJECTIVE The purpose of this paper is to present a model based on data mining techniques including feature selection and ensemble classification that can accurately predict breast cancer patients in the early stages. METHODOLOGY The proposed breast cancer detection model is developed by joining Adaptive Differential Evolution (ADE) algorithm for feature selection and Learning Vector Quantization (LVQ) neural network for classification. Our proposed model as ADE-LVQ has the ability to automatically and quickly diagnose breast cancer patients into two classes, benign and malignant. As a new evolutionary approach, ADE performs optimal configuration for LVQ neural network in addition to selecting effective features from breast cancer data. Meanwhile, we configure an ensemble classification technique based on LVQ, which significantly improves the prediction performance. RESULTS ADE-LVQ has been analyzed from different perspectives on different datasets from Wisconsin breast cancer database. We apply different approaches to handle missing values and improve data quality on this database. The results of the simulations showed that the ADE-LVQ model is more successful than the equivalent and state-of-the-art models in diagnosing breast cancer patients. Also, ADE-LVQ provides better performance with less complexity, considering feature selection and ensemble learning. In particular, ADE-LVQ improves accuracy (up to 3.4%) and runtime (up to 2.3%) on average compared to the existing best method. CONCLUSION Combined methods based on data mining techniques for breast cancer diagnosis can help doctors in making better decisions for disease treatment.
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Affiliation(s)
- Dan Wang
- Zaozhuang Hospital of Traditional Chinese Medicine, Zaozhuang, 277000, Shandong, China.
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Gomes BC, Peixinho N, Pisco R, Gromicho M, Pronto-Laborinho AC, Rueff J, de Carvalho M, Rodrigues AS. Differential Expression of miRNAs in Amyotrophic Lateral Sclerosis Patients. Mol Neurobiol 2023; 60:7104-7117. [PMID: 37531027 PMCID: PMC10657797 DOI: 10.1007/s12035-023-03520-7] [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: 04/28/2023] [Accepted: 07/14/2023] [Indexed: 08/03/2023]
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that affects nerve cells in the brain and spinal cord, causing loss of muscle control, muscle atrophy and in later stages, death. Diagnosis has an average delay of 1 year after symptoms onset, which impairs early management. The identification of a specific disease biomarker could help decrease the diagnostic delay. MicroRNA (miRNA) expression levels have been proposed as ALS biomarkers, and altered function has been reported in ALS pathogenesis. The aim of this study was to assess the differential expression of plasma miRNAs in ALS patients and two control populations (healthy controls and ALS-mimic disorders). For that, 16 samples from each group were pooled, and then 1008 miRNAs were assessed through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). From these, ten candidate miRNAs were selected and validated in 35 ALS patients, 16 ALS-mimic disorders controls and 15 healthy controls. We also assessed the same miRNAs in two different time points of disease progression. Although we were unable to determine a miRNA signature to use as disease or condition marker, we found that miR-7-2-3p, miR-26a-1-3p, miR-224-5p and miR-206 are good study candidates to understand the pathophysiology of ALS.
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Affiliation(s)
- Bruno Costa Gomes
- Instituto de Fisiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.
- ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisboa, Portugal.
| | - Nuno Peixinho
- ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisboa, Portugal
| | - Rita Pisco
- ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisboa, Portugal
| | - Marta Gromicho
- Instituto de Fisiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Catarina Pronto-Laborinho
- Instituto de Fisiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
| | - José Rueff
- ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisboa, Portugal
| | - Mamede de Carvalho
- Instituto de Fisiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Department of Neurosciences and Mental Health, Hospital de Santa Maria CHULN, Lisboa, Portugal
| | - António Sebastião Rodrigues
- ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisboa, Portugal
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Meng C, Yuan Y, Zhao H, Pei Y, Li Z. IIFS: An improved incremental feature selection method for protein sequence processing. Comput Biol Med 2023; 167:107654. [PMID: 37944304 DOI: 10.1016/j.compbiomed.2023.107654] [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: 09/01/2023] [Revised: 10/09/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
MOTIVATION Discrete features can be obtained from protein sequences using a feature extraction method. These features are the basis of downstream processing of protein data, but it is necessary to screen and select some important features from them as they generally have data redundancy. RESULT Here, we report IIFS, an improved incremental feature selection method that exploits a new subset search strategy to find the optimal feature set. IIFS combines nonadjacent sorting features to prevent the drawbacks of data explosion and excessive reliance on feature sorting results. The comparative experimental results on 27 feature sorting data show that IIFS can find more accurate and important features compared to existing methods.The IIFS approach also handles data redundancy more efficiently and finds more representative and discriminatory features while ensuring minimal feature dimensionality and good evaluation metrics. Moreover, we wrap this method and deploy it on a web server for access at http://112.124.26.17:8005/.
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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 of Agriculture and Animal Husbandry, China
| | - Ye Yuan
- Beidahuang Industry Group General Hospital, Harbin, 150001, China
| | - Haiyan Zhao
- College of Integration of Traditional Chinese and Western Medicine to Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Yue Pei
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China
| | - Zhi Li
- Department of Spleen and Stomach Diseases, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China.
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Bai T, Liu B. ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning. Brief Funct Genomics 2023; 22:442-452. [PMID: 37122147 DOI: 10.1093/bfgp/elad007] [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: 09/27/2022] [Revised: 12/31/2022] [Accepted: 01/31/2023] [Indexed: 05/02/2023] Open
Abstract
Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance of the existing computational methods is low and needs to be further studied. First, most prediction models are trained with outdated databases. Second, only a few predictors can identify multiple subcellular localizations simultaneously. In this work, we establish three human ncRNA subcellular datasets based on the latest RNALocate, including lncRNA, miRNA and snoRNA, and then we propose a novel multi-label classification model based on ensemble learning called ncRNALocate-EL to identify multi-label subcellular localizations of three ncRNAs. The results show that the ncRNALocate-EL outperforms previous methods. Our method achieved an average precision of 0.709,0.977 and 0.730 on three human ncRNA datasets. The web server of ncRNALocate-EL has been established, which can be accessed at https://bliulab.net/ncRNALocate-EL.
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Yang J, Hussein Kadir D. Data mining techniques in breast cancer diagnosis at the cellular-molecular level. J Cancer Res Clin Oncol 2023; 149:12605-12620. [PMID: 37442866 DOI: 10.1007/s00432-023-05090-6] [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: 05/20/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023]
Abstract
INTRODUCTION Studies in the field of better diagnosis of breast cancer using machine learning and data mining techniques have always been promising. A new diagnostic method can detect the characteristics of breast cancer in the early stages and help in better treatment. The aim of this study is to provide a method for early detection of breast cancer by reducing human errors based on data mining techniques in medicine using accurate and rapid screening. METHODOLOGY The proposed method includes data pre-processing and image quality improvement in the first step. The second step consists of separating cancer cells from healthy breast tissue and removing outliers using image segmentation. Finally, a classification model is configured by combining deep neural networks in the third phase. The proposed ensemble classification model uses several effective features extracted from images and is based on majority vote. This model can be used as a screening system to diagnose the grade of invasive ductal carcinoma of the breast. RESULTS Evaluations have been done using two histopathological microscopic datasets including patients with invasive ductal carcinoma of the breast. With extracting high-level features with average accuracies of 92.65% and 93.34% in these two datasets, the proposed method has succeeded in quickly diagnosing and classifying breast cancer with high performance. CONCLUSION By combining deep neural networks and extracting features affecting breast cancer, the ability to diagnose with the highest accuracy is provided, and this is a step toward helping specialists and increasing the chances of patients' survival.
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Affiliation(s)
- Jian Yang
- General Office of China Science and Technology Development Center for Chinese Medicine, Chaoyang District, Beijing, 100020, China.
| | - Dler Hussein Kadir
- Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University, Erbil, Iraq
- Department of Business Administration, Cihan University-Erbil, Erbil, Iraq
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Yang L, Peng S, Yahya RO, Qian L. Cancer detection in breast cells using a hybrid method based on deep complex neural network and data mining. J Cancer Res Clin Oncol 2023; 149:13331-13344. [PMID: 37486394 DOI: 10.1007/s00432-023-05191-2] [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: 07/01/2023] [Accepted: 07/16/2023] [Indexed: 07/25/2023]
Abstract
INTRODUCTION Diagnosis of cancer in breast cells is an important and vital issue in the field of medicine. In this context, the use of advanced methods such as deep complex neural networks and data mining can significantly improve the accuracy and speed of diagnosis. A hybrid approach that can be effective in breast cancer diagnosis is the use of deep complex neural networks and data mining. Due to their powerful nonlinear capabilities in extracting complex features from data, deep neural networks have a very good ability to detect patterns related to cancer. By analyzing millions of data related to breast cells and recognizing common and unusual patterns in them, these networks are able to diagnose cancer with high accuracy. Also, the use of data mining method plays an important role in this process. METHODOLOGY Using data mining algorithms and techniques, useful information can be extracted from the available data and the characteristics of healthy and cancerous cells can be separated. This information can be given as input to the deep neural network to achieve more accurate diagnosis. Another method to diagnose breast cancer is the use of thermography, which we use in this research along with data mining and deep learning. RESULTS Thermography uses an infrared camera to record the temperature of the target area. This method of breast cancer imaging is less expensive and completely safe compared to other methods. A total of 187 volunteers including 152 healthy people and 35 cancer patients were evaluated. Each person had ten thermographic images, resulting in a total of 1870 thermographic images. Four alternative deep complex neural network models, namely ResNet18, ResNet50, VGG19, and Xception, were used to identify thermal images, including benign and malignant images. CONCULSION The evaluation results showed that the use of a combined method based on deep complex neural network and data mining in the diagnosis of cancer in breast cells can bring a significant improvement in the accuracy and speed of diagnosis of this important disease.
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Affiliation(s)
- Ling Yang
- School of Informatics, Harbin Guangsha College, Harbin, 150025, Heilongjiang, China
| | - Shengguang Peng
- School of Engineering and Management, Pingxiang University, Pingxiang, 337055, Jiangxi, China.
| | - Rebaz Othman Yahya
- Department of Computer Science, College of Science, Cihan University-Erbil, Erbil, Iraq
| | - Leren Qian
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, 85281, USA
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Fakhri PS, Asghari O, Sarspy S, Marand MB, Moshaver P, Trik M. A fuzzy decision-making system for video tracking with multiple objects in non-stationary conditions. Heliyon 2023; 9:e22156. [PMID: 38034808 PMCID: PMC10685270 DOI: 10.1016/j.heliyon.2023.e22156] [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: 03/14/2023] [Revised: 10/26/2023] [Accepted: 11/06/2023] [Indexed: 12/02/2023] Open
Abstract
Computer vision remains challenged by tracking multiple objects in motion frames, despite efforts to improve surveillance, healthcare, and human-machine interaction. This paper presents a method for monitoring several moving objects in non-stationary settings for autonomous navigation. Additionally, at each phase, movement information between successive frames, including the new frame and the previous frame, is employed to determine the location of moving objects inside the camera's field of view, and the background in the new frame is determined. With the help of a matching algorithm, the Kanade-Lucas-Tomasi (KLT) feature tracker for each frame is determined. To get the new frame, we access the matching feature points between two subsequent frames, calculate the movement size of the feature points and the camera movement, and subtract the previous frame of moving objects from the current frame. Every moving object within the camera's field of view is captured at every moment and location. The moving items are categorized and segregated using fuzzy logic based on their mass center and length-to-width ratio. Our algorithm was implemented to investigate autonomous navigation surveillance of three types of moving objects, such as a vehicle, a pedestrian, a bicycle, or a motorcycle. The results indicate high accuracy and an acceptable time requirement for monitoring moving objects. It has a tracking and classification accuracy of around 75 % and processes 43 frames per second, making it superior to existing approaches in terms of speed and accuracy.
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Affiliation(s)
- Payam Safaei Fakhri
- Department of Artificial Intelligence, Software Engineering, Islamic Azad University, Central Tehran Branch, Iran
| | - Omid Asghari
- Department of Mechanics, Power and Computer Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Sliva Sarspy
- Department of Computer Science, College of Science, Cihan University-Erbil, Erbil, Iraq
| | - Mehran Borhani Marand
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Paria Moshaver
- Department of Mechanical Engineering, University of Kentucky, Kentucky, United States
| | - Mohammad Trik
- Department of Computer Engineering, Boukan Branch, Islamic Azad University, Boukan, Iran
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46
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Rahimi MR, Makarem D, Sarspy S, Mahdavi SA, Albaghdadi MF, Armaghan SM. Classification of cancer cells and gene selection based on microarray data using MOPSO algorithm. J Cancer Res Clin Oncol 2023; 149:15171-15184. [PMID: 37634207 DOI: 10.1007/s00432-023-05308-7] [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: 08/04/2023] [Accepted: 08/16/2023] [Indexed: 08/29/2023]
Abstract
PURPOSE Microarray information is crucial for the identification and categorisation of malignant tissues. The very limited sample size in the microarray has always been a challenge for classification design in cancer research. As a result, by pre-processing gene selection approaches and genes lacking their information, the microarray data are deleted prior to categorisation. In essence, an appropriate gene selection technique can significantly increase the accuracy of illness (cancer) classification. METHODS For the classification of high-dimensional microarray data, a novel approach based on the hybrid model of multi-objective particle swarm optimisation (MOPSO) is proposed in this research. First, a binary vector representing each particle's position is presented at random. A gene is represented by each bit. Bit 0 denotes the absence of selection of the characteristic (gene) corresponding to it, while bit 1 denotes the selection of the gene. Therefore, the position of each particle represents a set of genes, and the linear Bayesian discriminant analysis classification algorithm calculates each particle's degree of fitness to assess the quality of the gene set that particle has chosen. The suggested methodology is applied to four different cancer database sets, and the results are contrasted with those of other approaches currently in use. RESULTS The proposed algorithm has been applied on four sets of cancer database and its results have been compared with other existing methods. The results of the implementation show that the improvement of classification accuracy in the proposed algorithm compared to other methods for four sets of databases is 25.84% on average. So that it has improved by 18.63% in the blood cancer database, 24.25% in the lung cancer database, 27.73% in the breast cancer database, and 32.80% in the prostate cancer database. Therefore, the proposed algorithm is able to identify a small set of genes containing information in a way choose to increase the classification accuracy. CONCLUSION Our proposed solution is used for data classification, which also improves classification accuracy. This is possible because the MOPSO model removes redundancy and reduces the number of redundant and redundant genes by considering how genes are correlated with each other.
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Affiliation(s)
| | - Dorna Makarem
- Escuela Tecnica Superior de Ingenieros de Telecomunicacion Politecnica de Madrid, Madrid, Spain
| | - Sliva Sarspy
- Department of Computer Science, College of Science, Cihan University-Erbil, Erbil, Iraq
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Ding Y, Zhou H, Zou Q, Yuan L. Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel. Methods 2023; 219:73-81. [PMID: 37783242 DOI: 10.1016/j.ymeth.2023.09.008] [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: 07/26/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023] Open
Abstract
Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions. Its purpose is to timely detect and control drug safety risks. Traditional methods are time-consuming. To accelerate the discovery of side effects, we propose a machine learning based method, called correntropy-loss based matrix factorization with neural tangent kernel (CLMF-NTK), to solve the prediction of drug side effects. Our method and other computational methods are tested on three benchmark datasets, and the results show that our method achieves the best predictive performance.
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Affiliation(s)
- Yijie Ding
- Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou 571158, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Hongmei Zhou
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.
| | - Lei Yuan
- Department of Hepatobiliary Surgery, Quzhou People's Hospital, 100# Minjiang Main Road, Quzhou 324000, China.
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Zheng D, Tang P, Lu D, Han L, Saberi S. A structured combination of ensemble classifier and filter-based feature selection to improve breast cancer diagnosis. J Cancer Res Clin Oncol 2023; 149:14519-14534. [PMID: 37567985 DOI: 10.1007/s00432-023-05238-4] [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: 07/21/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023]
Abstract
INTRODUCTION Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. Machine learning approaches cannot replace professional humans, but they can change the treatment of diseases such as cancer and be used as medical assistants. BACKGROUND Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. Feature selection and classification are common data mining techniques in machine learning that can provide breast cancer diagnosis with high speed, low cost and high precision. METHODOLOGY This paper proposes a new intelligent approach using an integrated filter-evolutionary search-based feature selection and an optimized ensemble classifier for breast cancer diagnosis. The selected features mainly relate to the viable solution as the selected features are successfully used in the breast cancer disease classification process. The proposed feature selection method selects the most informative features from the original feature set by integrating adaptive thresholder information gain-based feature selection and evolutionary gravity-search-based feature selection. Meanwhile, classification model is done by proposing a new intelligent multi-layer perceptron neural network-based ensemble classifier. RESULTS The simulation results show that the proposed method provides better performance compared to the state-of-the-art algorithms in terms of various criteria such as accuracy, sensitivity and specificity. Specifically, the proposed method achieves an average accuracy of 99.42% on WBCD, WDBC and WPBC datasets from Wisconsin database with only 56.7% of features. CONCLUSION Systems based on intelligent medical assistants configured with machine learning approaches are an important step toward helping doctors to detect breast cancer early.
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Affiliation(s)
- Dengru Zheng
- Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China.
| | - Ping Tang
- Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China
| | - Danping Lu
- Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China
| | - Liangfu Han
- Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China
| | - Sajjad Saberi
- Department of Computer Science, Khayyam University, Mashhad, Iran.
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49
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Kim MS, Song M, Kim B, Shim I, Kim DS, Natarajan P, Do R, Won HH. Prioritization of therapeutic targets for dyslipidemia using integrative multi-omics and multi-trait analysis. Cell Rep Med 2023; 4:101112. [PMID: 37582372 PMCID: PMC10518515 DOI: 10.1016/j.xcrm.2023.101112] [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: 11/17/2022] [Revised: 12/22/2022] [Accepted: 06/19/2023] [Indexed: 08/17/2023]
Abstract
Drug targets with genetic support are several-fold more likely to succeed in clinical trials. We introduce a genetic-driven approach based on causal inferences that can inform drug target prioritization, repurposing, and adverse effects of using lipid-lowering agents. Given that a multi-trait approach increases the power to detect meaningful variants/genes, we conduct multi-omics and multi-trait analyses, followed by network connectivity investigations, and prioritize 30 potential therapeutic targets for dyslipidemia, including SORT1, PSRC1, CELSR2, PCSK9, HMGCR, APOB, GRN, HFE2, FJX1, C1QTNF1, and SLC5A8. 20% (6/30) of prioritized targets from our hypothesis-free drug target search are either approved or under investigation for dyslipidemia. The prioritized targets are 22-fold higher in likelihood of being approved or under investigation in clinical trials than genome-wide association study (GWAS)-curated targets. Our results demonstrate that the genetic-driven approach used in this study is a promising strategy for prioritizing targets while informing about the potential adverse effects and repurposing opportunities.
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Affiliation(s)
- Min Seo Kim
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Minku Song
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Beomsu Kim
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Injeong Shim
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Dan Say Kim
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Pradeep Natarajan
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea; Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea.
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50
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Mai J, Lu M, Gao Q, Zeng J, Xiao J. Transcriptome-wide association studies: recent advances in methods, applications and available databases. Commun Biol 2023; 6:899. [PMID: 37658226 PMCID: PMC10474133 DOI: 10.1038/s42003-023-05279-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
Genome-wide association study has identified fruitful variants impacting heritable traits. Nevertheless, identifying critical genes underlying those significant variants has been a great task. Transcriptome-wide association study (TWAS) is an instrumental post-analysis to detect significant gene-trait associations focusing on modeling transcription-level regulations, which has made numerous progresses in recent years. Leveraging from expression quantitative loci (eQTL) regulation information, TWAS has advantages in detecting functioning genes regulated by disease-associated variants, thus providing insight into mechanisms of diseases and other phenotypes. Considering its vast potential, this review article comprehensively summarizes TWAS, including the methodology, applications and available resources.
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Affiliation(s)
- Jialin Mai
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mingming Lu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qianwen Gao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyao Zeng
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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