Ayurgenomics visualisation and machine learning group
- To develop machine learning algorithm for visualizing heterogenous multidimentional phenomics and genomics data
Technological advancement in high-throughput experiments (HTE) allow us to decipher many biological insights such as, how transcription factor interact with downstream genes, with the aid of machine learning algorithms. Machine learning algorithm play a very vital and integral part of understand complex biological event where we profile multitude of genes and uncover patterns from it. Most HTE involve experients, where the phenotype of interest (Xpheno) is simple such as (case/control, normal/disease conditions) and accordingly we developed algorithms to infer genes(Yg) as predictors of the phenotypes (eg., cancer). In recent years, we started appreciating the fact that other covariate such as age, sex, environmental conditions along with our phenotype of interest could play a vital role in regulation within cellular. Overview of phenomics and genomics data is illustrated below in Figure-01.
- Rohit Jain
- Ishita Mediratta
- Kartik Bhatia
- Syed Ahsan Abbas
- [Nishchit Soni]
- Anmol Agarwal
- 2017, PLoS One, Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits
- 2011, ACS Chemical Biology, Ayurgenomics: A New Way of Threading Molecular Variability for Stratified Medicine
- 2016, Journal of Genetics, Genomic insights into ayurvedic and western approaches to personalized medicine
- 2019, The Lancet, A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service
- 2019, The lancet, Depicting the spectrum of diseases that occur during the lifespan of an individual based on electronic health records
- 2019, Nature Scientific Report, Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program
- 2019, Nature Medicine, High-performance medicine: the convergence of human and artificial intelligence
- 2018, bioRxiv, REVA: a rank-based multi-dimensional measure of correlation
- 2018, PNAS, QnAs with Donald Geman
- 2018, Nature Biotechnology, Reply to "Precision medicine in the clouds"
- 2018, Nature Biotechnology, Precision medicine in the clouds
- 2017, Nature Biotechnology, A wellness study of 108 individuals using personal, dense, dynamic data clouds
- 2008, Journal of Translational Medicine, Whole genome expression and biochemical correlates of extreme constitutional types defined in Ayurveda