Published in ICLR Workshop on AI for Affordable Heathcare, 2020
A Bayesian framework for survival models with more accurate survival predictions and ability to quantify the survival uncertainty. Read more
Published in Nextgen Genomics, Biology, Bioinformatics and Technologies Conference, 2019
Using graph theory based dimnesion reduction and boolean networks we identify key targets for phenotype switch in TNFa-TNRF Signaling Pathway Read more
Published in ESMO MAP, 2019
This paper provides a Bayesian framework for quantifying survival uncertainity using deep esembles. The input to network is PAM50 gene set and clinical variables. Read more
Published in TNF Superfamily, 2019
This paper identifies few components present in TNFa signalling which when perturbed leads to phenotype switch. It uses graph-theory based based dimensionality reduction technique to identify the candidates. Read more