ArticlePloS one2017
Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial Intelligence and Digital Technologies in Prakriti Assessment: Toward Standardized Evidence-Based Ayurvedic Practice.Journal of Ayurveda and integrative medicine · 2026Review
- Using AI Algorithms and Machine Learning in the Analysis of a Bio-Purification Method (Therapeutic Emesis, Known as "Vamana Karma"): Protocol for a Mixed Methods Study.JMIR research protocols · 2026Article
- Predicting susceptibility to inflammaging using a genetic inflammatory index through Prakriti-based phenotypic stratification.Frontiers in immunology · 2026Article
- Artificial intelligence in traditional medicine: policy and governance strategies.Bulletin of the World Health Organization · 2025Article
- Prakriti (constitutional typology) in Ayurveda: a critical review of Prakriti assessment tools and their scientific validity.Frontiers in medicine · 2025Review
- The Brief Prakriti Inventory: Latent structure, reliability, and validity.Wellcome open research · 2025Article
- Molecular study of the KCNJ11 gene and its correlation with Prakriti to preventing and managing type 2 diabetes.Journal of traditional and complementary medicine · 2024Article
- Review
- Ayurgenomics-based frameworks in precision and integrative medicine: Translational opportunities.Cambridge prisms. Precision medicine · 2023Review
- Heart rate variability during head-up tilt shows inter-individual differences among healthy individuals of extreme Prakriti types.Physiological reports · 2022Article
- Whole Exome Sequencing in Healthy Individuals of Extreme Constitution Types Reveals Differential Disease Risk: A Novel Approach towards Predictive Medicine.Journal of personalized medicine · 2022Article
- The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review.Frontiers in pharmacology · 2022Article
- Baseline cell proliferation rates and response to UV differ in lymphoblastoid cell lines derived from healthy individuals of extreme constitution types.Cell cycle (Georgetown, Tex.) · 2021Article
- Ayurgenomics and Modern Medicine.Medicina (Kaunas, Lithuania) · 2020Review
- Western Indian Rural Gut Microbial Diversity in ExtremeFrontiers in microbiology · 2018Article
- Exploring AyuGenomics approach for understanding COVID-19 predisposition and progression.Journal of Ayurveda and integrative medicineReview
- Association of coronary artery disease related single nucleotide-polymorphisms with extreme Prakriti types: Insights from a case control study.Journal of Ayurveda and integrative medicineArticle
- Integrating ayurvedic medicine into cancer research programs part 1: Ayurveda background and applications.Journal of Ayurveda and integrative medicineReview
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Authors and funding
15 authors.
Funding
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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