Big Data Approaches for Modeling Response and Resistance to Cancer Drugs.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI:10.1146/ANNUREV-BIODATASCI-080917-013350

Peng Jiang, W. Sellers, X. S. Liu

引用次数: 23

Abstract

Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.

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癌症药物反应和耐药性建模的大数据方法。

尽管癌症研究取得了重大进展，但目前的标准治疗药物无法治愈许多类型的癌症。因此，迫切需要确定更好的预测性生物标志物和治疗方案。传统上，来自假设驱动的研究的见解是癌症生物学和治疗发现的主要力量。最近，在高通量技术突破的催化下，大数据资源的快速增长导致了癌症治疗研究的范式转变。计算方法和基因组学数据的结合已经导致了一些成功的临床应用。在这篇综述中，我们重点介绍了数据驱动的抗癌药物疗效建模方法的最新进展，并介绍了数据科学在癌症治疗研究中的挑战和机遇。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.