Vamsi Nallapareddy

This paper introduces REGEN, a GNN framework for learning gene interaction networks from bulk transcriptomic data and predicting patient outcomes, showing improved performance across cancer types.

The paper introduces Riboclette, a conditional deep learning model that predicts ribosome footprints under amino acid deprivation and identifies key codons and motifs driving translation stalling.

This study introduces RiboGL, a model combining graph and recurrent neural networks to predict ribosome density from mRNA secondary structure, outperforming existing methods.

The paper uses AlphaFold2 to analyze protein structures from 21 organisms, identifying 26 novel superfamilies and expanding structural diversity insights.

The paper introduces CATHe, a method using protein language model embeddings to detect remote homologues for CATH superfamilies, achieving high accuracy and validating predictions with AlphaFold structures.

This paper introduces ABLE, an attention-based bidirectional-LSTM model for enzyme classification using protein FASTA sequences, achieving an F1-score of 0.834.