Ananthan Nambiar

In vivo CRISPR base editing for treatment of Huntington's disease

This study uses CRISPR base editing to create HTT protein isoforms resistant to caspase-6 cleavage, reducing toxicity and neurodegeneration in a rodent HD model.

Unified imputation of missing data modalities and features in multi-omic data via shared representation learning

MIMIR is a deep learning framework that imputes missing modalities and features in multi-omic data using shared representation learning, outperforming existing methods on pan-cancer data.

Protein Language Models Capture Structural and Functional Epistasis in a Zero-Shot Setting

This study shows that protein language models capture both structural and functional dependencies from sequence data alone through epistasis analysis.

SPLICER: A Highly Efficient Base Editing Toolbox That Enables In Vivo Therapeutic Exon Skipping

SPLICER improves exon skipping by editing both splice acceptor and donor sites, reducing aberrant splicing and enabling therapeutic applications in Alzheimer's disease.

DR-BERT: A Protein Language Model to Annotate Disordered Regions

DR-BERT is a protein language model pretrained on unannotated proteins to predict disordered regions, outperforming existing methods without using explicit evolutionary or biophysical features.

FUN-PROSE: A Deep Learning Approach to Predict Condition-Specific Gene Expression in Fungi

FUN-PROSE predicts condition-specific gene expression in fungi using promoter sequences and transcription factor data, achieving high correlation with observed expression.

Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

A RoBERTa-style Transformer, pre-trained on 450K Swiss-Prot sequences in 18 hours on 4 V100 GPUs, rivals DeepFam at family classification and beats PIPR at human protein-protein interaction prediction.