Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data.
Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data.
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DOI：
10.1007/978-1-4939-9089-4_15
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发表时间：
2019
影响因子：
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通讯作者：
Wei Chen;Xiao-feng Zhou
中科院分区：
文献类型：
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作者：
Wei Chen;Xiao-feng Zhou
综合基于网络的细胞标志库(LINCS)项目旨在通过编目基因表达和信号转导的变化来创建对生物学的基于网络的理解。L1000大数据集使用L1000平台提供了超过10,000种化合物、shRNA和激酶抑制剂诱导的基因表达谱。我们开发了一个系统的化合物特征发现流水线csNMF，从原始L1000数据处理到药物筛选和机制生成。发现的乳腺癌的化合物特征与Lincs KINOMEScan的数据一致，并具有临床意义。通过这种方式，我们的计算模型阐明了化合物的潜在功效机制。
The library of integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction. L1000 big datasets provide gene expression profiles induced by over 10,000 compounds, shRNAs, and kinase inhibitors using L1000 platform. We developed a systematic compound signature discovery pipeline named csNMF, which covers from raw L1000 data processing to drug screening and mechanism generation. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. In this way, the potential mechanisms of compounds’ efficacy are elucidated by our computational model.