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不同癌症研究用微阵列平台中的生物信息学工具和网络服务器综述。

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research.

机构信息

Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.

Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar.

出版信息

Adv Protein Chem Struct Biol. 2022;131:85-164. doi: 10.1016/bs.apcsb.2022.05.002. Epub 2022 Jun 17.

DOI:10.1016/bs.apcsb.2022.05.002
PMID:35871897
Abstract

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

摘要

在过去的十年中,传统的实验室工作策略逐渐从局限于实验室环境转变为生物信息学时代,以帮助管理和处理组学技术产生的大量数据。本工作概述了生物信息学在分析微阵列数据及其在癌症中的应用方面的最新贡献。我们剖析了不同的微阵列平台及其在癌症模型中的基因表达中的应用。我们强调了计算方法的进步如何增强了微阵列技术在基因表达分析中的作用。蛋白质-蛋白质相互作用数据库分为主要数据库、衍生数据库、元数据库和预测数据库,描述了在计算机中进行基因调控网络预测的策略。此外,我们总结了神经网络在生物信息学中的应用领域,如蛋白质功能、蛋白质相互作用预测、以及计算机药物发现和开发。我们还讨论了深度学习作为癌症预后、诊断和治疗的潜在工具的作用。在未来十年中,有效地、实际地、合乎道德地整合这些资源可能是医疗保健行业最具挑战性的任务;然而,我们相信从长远来看,这是可以实现的。

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