期刊
DRUG DISCOVERY TODAY
卷 22, 期 7, 页码 1069-1076出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.drudis.2017.01.005
关键词
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There is a neat distinction between general purpose statistical techniques and quantitative models developed for specific problems. Principal Component Analysis (PCA) blurs this distinction: while being a general purpose statistical technique, it implies a peculiar style of reasoning. PCA is a 'hypothesis generating' tool creating a statistical mechanics frame for biological systems modeling without the need for strong a priori theoretical assumptions. This makes PCA of utmost importance for approaching drug discovery by a systemic perspective overcoming too narrow reductionist approaches.
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