Haicang Zhang

PocketX uses reinforcement learning to align protein pocket design with biophysical properties, outperforming existing methods on binding energy and evolutionary plausibility.

ProAffinity++ predicts protein-protein binding affinities using sequence and structure representations, outperforming existing methods in accuracy and handling complex cases like antigen-antibody interactions.

The paper introduces ImmuneFold, a transfer learning method for immune protein structure prediction, showing improved accuracy over existing methods for TCR-epitope binding.

CarbonDesign improves protein sequence design by integrating AlphaFold concepts, achieving high accuracy on benchmark datasets and showing robustness in de novo design and variant effect prediction.

ProDESIGN-LE designs protein sequences efficiently by learning residue local environments, achieving high accuracy in structure prediction and experimental validation.

This paper introduces gMVP, a graph attention neural network for predicting the functional effect of missense variants, showing improved performance over existing methods in identifying damaging variants and separating de novo variants in neurodevelopmental disorders.

This study introduces DeepNovo Peptidome and DeepSelf for neoantigen discovery and validation using immunopeptidomics and deep learning, demonstrating their application in cervical cancer tissues.

ThreaderAI improves protein structure prediction by enhancing template-query alignment using deep learning, outperforming existing methods on distant homology proteins.

The paper uses single-cell RNA-seq data to develop a machine learning method called A-risk to prioritize autism risk genes, identifying 71 candidates including new ones like CLCN4, PRKAR1B, and NR2F1.

MVP uses deep learning to predict pathogenicity of missense variants, outperforming previous methods in prioritizing pathogenic variants.

The paper uses bi-clustering to identify gene clusters with consistent expression-protein abundance correlations and builds a prediction model to forecast protein abundance from mRNA data.