Tavpritesh Sethi

Benchmarking Large Language Models for Assay-Aware Drug-Target Binding Affinity Prediction

This paper benchmarks large language models for drug-target binding affinity prediction, showing that assay descriptions provide predictive value beyond molecular and protein data.

Generative augmentation recovers prognostic disease signatures from underpowered transcriptomic datasets

GeneLift recovers prognostic gene signatures from underpowered transcriptomic datasets by augmenting data with Gaussian mixture modeling and validating results with EvidenceClock.

Genomic Surveillance of COVID-19 Variants with Language Models and Machine Learning

Strainflow predicts SARS-CoV-2 variant surges using genomic data, accurately forecasting Delta and Omicron surges and India's surge two months in advance.

Evaluating Sample Augmentation in Microarray Datasets with Generative Models: A Comparative Pipeline and Insights in Tuberculosis.

The paper evaluates generative models for sample augmentation in microarray datasets, comparing VAE, GAN, and GMM in a tuberculosis dataset to improve biomarker discovery.

lncRNA Mediated Hijacking of T-cell Hypoxia Response Pathway by Mycobacterium tuberculosis Predicts Latent to Active Progression in Humans

The paper identifies lncRNA-mediated disruption of hypoxia response pathways in T cells as a predictor of tuberculosis progression from latent to active, using computational analysis of patient data.