Sisira Kadambat Nair

This paper develops a distributed pipeline for immuno-oncology biomarker discovery using federated learning to integrate clinical and molecular data across institutions without sharing sensitive data.

The paper assesses the inter-lab consistency of circular RNA expression in cancer cell lines and identifies factors affecting reproducibility.

PharmacoDB 2.0 enhances pharmacogenomics analysis by integrating new datasets, implementing FAIR pipelines, and improving drug response analysis tools.

The paper introduces a cloud-based platform called ORCESTRA for reproducible processing and sharing of multimodal biomedical data in a FAIR manner.

This paper provides guidelines for developing machine learning models to predict drug sensitivity using cell line-based pharmacogenomics data, emphasizing proper training and validation practices.