Muhao Chen

JEDI predicts circular RNA from nucleotide sequences using attention mechanisms and cross-attention layers, outperforming existing methods and enabling backsplicing hotspot discovery.

Bio-JOIE learns joint representations of biological knowledge to predict protein interactions and cluster enzymes, outperforming existing methods in PPI type prediction.

This paper introduces MuPIPR, a deep learning framework that estimates mutation effects on protein-protein interactions by using contextualized amino acid representations and a Siamese residual recurrent convolutional encoder.

This paper evaluates neural network models for embedding Gene Ontology terms and introduces new models based on GCN, ELMo, and BERT, finding that BERT-based embeddings perform best for rare labels.

Lasagna is an end-to-end framework for protein-protein interaction prediction using primary sequences, outperforming existing methods in binary prediction and showing promise in interaction type and binding affinity tasks.