Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword “bioinformatics”). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research?

The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery.

This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature.

像19世纪的物理学一样，通过结合数学方法，尤其是生物学和分子生物学已经像其他少数科学领域一样得到了丰富和应用。在过去的几十年中，生物信息学成为一个全新的科学领域，每年产出超过30,000篇论文（使用关键字“生物信息学”进行公开搜索）。已经建立了巨大的海量通量数据数据库，仅ArrayExpress仅包含超过270万次检测（2019年10月）。计算方法已成为分子生物学中必不可少的工具，尤其是在癌症研究最具挑战性的领域之一-多药耐药性（MDR）中。但是，面对众多不同的算法，方法和方法，普通研究人员面临以下关键问题：存在哪些方法？可以使用哪些方法来解决给定研究的目标？或者，更笼统地说，我如何使用计算生物学/生物信息学来支持我的研究？

当前的审查旨在为与MDR研究相关的现有方法提供指导。特别是，我们提供了以下方面的概述：a）使用表达数据鉴定潜在的生物标志物；b）通过机器学习方法预测治疗反应；c）采用网络方法来确定基因/蛋白质调控网络和潜在的关键参与者；d）识别药物-靶标相互作用；e）使用两方网络确定多药目标；f）鉴定具有MDR表型的细胞亚群；最后，g）使用分子建模方法来指导和增强药物发现。

该审查应作为对MDR研究中有用的一些基本概念的指导。通过使用计算工具，它应该使读者对MDR研究的可能性有一些想法，最后，它应该提供有关文献的简短概述。