Connectivity Mapping: Methods and Applications

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI:10.1146/ANNUREV-BIODATASCI-072018-021211

A. Keenan, Megan L. Wojciechowicz, Zichen Wang, Kathleen M. Jagodnik, S. L. Jenkins, Alexander Lachmann, Avi Ma’ayan

{"title":"Connectivity Mapping: Methods and Applications","authors":"A. Keenan, Megan L. Wojciechowicz, Zichen Wang, Kathleen M. Jagodnik, S. L. Jenkins, Alexander Lachmann, Avi Ma’ayan","doi":"10.1146/ANNUREV-BIODATASCI-072018-021211","DOIUrl":null,"url":null,"abstract":"Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-072018-021211","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Biomedical Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1146/ANNUREV-BIODATASCI-072018-021211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}

引用次数: 34

Abstract

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

查看原文本刊更多论文

连通性映射：方法与应用

连通性映射资源由表示系统小分子、疾病、基因或其他形式的扰动后细胞状态变化的特征组成。这样的资源使得能够基于相似性对来自新扰动的签名进行表征；提供许多主题扰动的空间的全局视图；并允许预测扰动的细胞、组织和生物体表型的能力。签名搜索引擎通过查找查询签名和签名数据库之间的连接来实现假设生成。该框架已被用于识别小分子与其靶标之间的联系，发现细胞对扰动的特异性反应以及用小分子逆转疾病表达状态的方法，并预测现有药物的小分子拟态物。这篇综述提供了连接映射资源的历史视角和当前状态，重点关注方法论和社区实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Annual Review of Biomedical Data Science

CiteScore

11.10

自引率

1.70%

发文量

期刊介绍： The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.