期刊
CELL
卷 144, 期 6, 页码 864-873出版社
CELL PRESS
DOI: 10.1016/j.cell.2011.03.001
关键词
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资金
- NIH [DP2-OD002414-01, DP2 OD002230]
- NIAID [U54 AI057159]
- Burroughs Wellcome Fund
- Packard Fellowship for Science and Engineering
The flood of genome-wide data generated by high-throughput technologies currently provides biologists with an unprecedented opportunity: to manipulate, query, and reconstruct functional molecular networks of cells. Here, we outline three underlying principles and six strategies to infer network models from genomic data. Then, using cancer as an example, we describe experimental and computational approaches to infer differential networks that can identify genes and processes driving disease phenotypes. In conclusion, we discuss how a network-level understanding of cancer can be used to predict drug response and guide therapeutics.
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