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To correlate and predict the potential and new functions of traditional Chinese medicine formulas based on similarity indices
Author(s) -
Xu Lu,
Gao PengFei,
Qin GuoChan,
An YingHe,
Tang BangCheng,
Wang Huan,
She YuanBin
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2924
Subject(s) - similarity (geometry) , consistency (knowledge bases) , mathematics , traditional chinese medicine , data mining , artificial intelligence , computer science , medicine , discrete mathematics , alternative medicine , pathology , image (mathematics)
A typical traditional Chinese medicine (TCM) formula (or a prescription) is composed of 1 or several single herbs. The number of possible TCM formulas is nearly as large as that of chemical structures, so the development of quantitative formula‐activity relationship models is as appealing as to build a quantitative structure‐activity relationship model. In this work, a formula descriptor system based on the TCM holistic medical model is generated to correlate and predict formula functions by using similarity indices. First, 73 general descriptors of 78 formulas from Chinese Pharmacopeia (2010) are computed. Second, 6 different similarity indices are used to evaluate the similarities among the 78 formulas. As the main functions of the 78 formulas are known and annotated, a significant similarity implies that a formula is likely to have some new functions owned by its “analogue.” Finally, different similarity measures are compared with reference to the results of experimental and clinical studies. The consistency between some predictions and the literature results indicates that the proposed method can provide clues for mining and investigating the unknown functions of TCM formulas.