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
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 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 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:
Uses advances in Bayesian machine learning to get aleatoric and epistemic uncertainties for cancer survival prediction. Slides: Uncertainty in cancer survival prediction.pdf Read more
Published:
A gentle introduction to Bayesian neural networks majorly focusing on weight-space variational inference. Slides: Introduction to Bayesian Neural Networks.pdf Read more
Published:
Provides a gentle introduction to causal inference, starts from structural causal models to discussing some recent methods to combine intervention and observational data in genetic settings. Slides: Introduction to Causal Inference.pdf Read more
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.