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Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits.

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits.
Author Information (click to view)

Tiwari P, Kutum R, Sethi T, Shrivastava A, Girase B, Aggarwal S, Patil R, Agarwal D, Gautam P, Agrawal A, Dash D, Ghosh S, Juvekar S, Mukerji M, Prasher B,


Tiwari P, Kutum R, Sethi T, Shrivastava A, Girase B, Aggarwal S, Patil R, Agarwal D, Gautam P, Agrawal A, Dash D, Ghosh S, Juvekar S, Mukerji M, Prasher B, (click to view)

Tiwari P, Kutum R, Sethi T, Shrivastava A, Girase B, Aggarwal S, Patil R, Agarwal D, Gautam P, Agrawal A, Dash D, Ghosh S, Juvekar S, Mukerji M, Prasher B,

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PloS one 2017 10 0512(10) e0185380 doi 10.1371/journal.pone.0185380
Abstract

In Ayurveda system of medicine individuals are classified into seven constitution types, "Prakriti", for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.

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