Dong‐Jun Yu

De novo design of functionally diverse and druggable antimicrobial peptides by diffusion and multimodal deep learning

This paper introduces AMP-D3, a framework for designing diverse and druggable antimicrobial peptides using diffusion and multimodal deep learning, achieving high accuracy in prediction and validation.

A Deep Learning-based Method for Drug Molecule Representation and Property Prediction

DrugDL is a deep learning framework that improves drug molecule representation and multi-task prediction, outperforming existing methods in cold-start scenarios and aiding drug discovery.

Self-iterative multiple instance learning enables the prediction of CD4+ T cell immunogenic epitopes

ImmuScope predicts CD4+ T cell immunogenic epitopes using self-iterative multiple-instance learning, improving accuracy with multi-allelic and single-allelic data.

MKFGO: Integrating Multi-Source Knowledge Fusion with Pre-Trained Language Model for High-Accuracy Protein Function Prediction

MKFGO integrates multi-source biological data with deep learning to predict protein functions, outperforming existing methods on 1522 proteins.

GerNA-Bind: Geometric-enhanced RNA-ligand Binding Specificity Prediction with Deep Learning

GerNA-Bind predicts RNA-ligand binding specificity using a geometric deep learning framework, achieving improved precision and identifying compounds with experimentally confirmed activity against MALAT1 RNA.

Supervised contrastive learning enhances MHC-II peptide binding affinity prediction

This paper introduces ConBoTNet, a deep learning model that improves MHC-II peptide binding affinity prediction using supervised contrastive learning and bottleneck transformers, achieving better performance than existing methods.

scRCA: a Siamese network-based pipeline for the annotation of cell types using imperfect single-cell RNA-seq reference data

scRCA improves cell type annotation using noisy reference data by combining a Siamese network with noise-robust loss functions and outperforms existing methods.

ULDNA: Integrating Unsupervised Multi-Source Language Models with LSTM-Attention Network for Protein-DNA Binding Site Prediction

ULDNA improves DNA-binding site prediction using a LSTM-attention network with pre-trained language models, achieving higher accuracy than existing methods on 1287 proteins.

Integrating Self-Attention Transformer with Triplet Neural Networks for Protein Gene Ontology Prediction

The paper introduces ATGO, a deep-learning method that improves Gene Ontology prediction for proteins using self-attention transformers and triplet networks, achieving higher accuracy than existing methods.

TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for High-Accuracy Gene Function Annotations

TripletGO improves gene function annotations by integrating transcript expression, sequence alignment, and probability models across multiple species and proteins.

RFRSN: Improving protein fold recognition by siamese network

This paper improves protein fold recognition by combining discriminative feature extraction with a siamese network to better measure similarity between protein sequences.

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

TripletRes predicts high-accuracy protein contact-maps from primary sequences using deep residual convolutional networks and coevolutionary matrices, outperforming existing methods on benchmark datasets.