Abhimanyu Banerjee

This paper introduces SpliceAI2, a deep learning model that improves identification and quantification of splice-altering variants and their effects on transcripts and disease.

This study uses single-cell chromatin accessibility analysis to identify developmental trajectories and non-coding mutations associated with congenital heart disease.

The paper uses a deep learning model to analyze ChIP-seq data and identifies binding motifs for CTCF zinc fingers 1-2, revealing GAG and ATT motifs.

The paper develops HT-recruit, a high-throughput assay to identify transcriptional effectors in human proteins, finding repressor function correlates with evolutionary age and discovering new activator domains.

Kipoi provides a framework for standardized, reusable machine learning models in genomics, enabling tasks like variant effect prediction and model benchmarking.