Application of network link prediction in drug discovery
文献类型：期刊论文
| 作者 | Abbas,Khushnood3,4; Abbasi,Alireza4; Dong,Shi3; Niu,Ling3; Yu,Laihang3; Chen,Bolun2; Cai,Shi-Min5; Hasan,Qambar1 |
| 刊名 | BMC Bioinformatics
 |
| 出版日期 | 2021-04-12 |
| 卷号 | 22期号:1 |
| 关键词 | Data-driven drug discovery Network link prediction Poly-pharmacy Poly-pharmacy side effects prediction Drug-target prediction |
| DOI | 10.1186/s12859-021-04082-y |
| 通讯作者 | Abbas,Khushnood(abbas@cigit.ac.cn) |
| 英文摘要 | AbstractBackgroundTechnological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches.ResultsWe applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone,?ACT and LRW5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$LRW_5$$\end{document} are the top 3 best performers on all five datasets.ConclusionsThis work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks. |
| 语种 | 英语 |
| WOS记录号 | BMC:10.1186/S12859-021-04082-Y |
| 出版者 | BioMed Central |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/13162]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Abbas,Khushnood |
| 作者单位 | 1.Deakin University; Centre for Cellular and Molecular Biology, School of Life and Environmental Science 2.Huaiyin Institute of Technology; College of Computer and Software Engineering 3.Zhoukou Normal University; School of Computer Science and Technology 4.University of New South Wales; School of Engineering and Information Technology 5.University of Electronic Science and Technology of China; School of Computer Science and Engineering |
| 推荐引用方式 GB/T 7714 | Abbas,Khushnood,Abbasi,Alireza,Dong,Shi,et al. Application of network link prediction in drug discovery[J]. BMC Bioinformatics,2021,22(1). |
| APA | Abbas,Khushnood.,Abbasi,Alireza.,Dong,Shi.,Niu,Ling.,Yu,Laihang.,...&Hasan,Qambar.(2021).Application of network link prediction in drug discovery.BMC Bioinformatics,22(1). |
| MLA | Abbas,Khushnood,et al."Application of network link prediction in drug discovery".BMC Bioinformatics 22.1(2021). |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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