Leo J. Lee

Orthrus is a Mamba-based RNA foundation model pre-trained with biological augmentations and contrastive learning, achieving better mRNA property prediction than existing models with less fine-tuning data.

Enigma is an efficient regulatory genomics model that achieves competitive performance with state-of-the-art models while significantly reducing computational cost.

FlashRNA improves computational efficiency for genomic sequence-to-function models while maintaining or enhancing predictive performance across multiple tasks.

This paper introduces a graph neural network model for predicting molecule carcinogenicity, showing improved performance over existing methods.

IntroSpect improves HLA binding peptide identification sensitivity by 80% using motif-guided database building, while maintaining high accuracy and FDR control.

EPIP predicts MHC-I epitope presentation by integrating mass spectrometry-derived motifs and tissue-specific RNA-seq data, outperforming existing methods in independent validation.

HiCSR enhances low-resolution Hi-C data to produce high-resolution contact maps with improved accuracy and biological relevance.

The paper introduces cDeepbind, a deep learning model that incorporates RNA secondary structure to improve RBP binding predictions, achieving better performance on both in-vitro and in-vivo data.