Quan Zou

This paper introduces LARK, a framework that learns chemical representations from reactions, showing improved performance in reaction prediction and molecular property prediction.

GeoEPred improves effector protein prediction by integrating sequence and structural information to better model functional motifs and spatial conformations.

Minipoa is a fast and memory-efficient partial order alignment tool that outperforms existing methods in speed, memory usage, and alignment accuracy for large-scale datasets.

Melody predicts DNA methylation from genomic sequences across human tissues and outperforms existing methods, also generalizing to methylation quantitative trait locus effect prediction.

linearPOA introduces a memory-efficient framework for Partial Order Alignment with linear space complexity, significantly reducing memory usage for long-read sequencing.

This paper introduces MoCETSE, a deep learning model that improves prediction of Gram-negative bacterial effector proteins by combining convolutional experts and transformers with relative positional encoding.

scMomer is a pretraining framework for single-cell multi-omics modeling under missing modality conditions, generating biologically meaningful embeddings and outperforming unimodal approaches in downstream tasks.

Chimera constructs a high-accuracy, memory-efficient database for metagenomic classification, outperforming Kraken2 in speed and scalability.

This study identifies metabolic flux disorders in gut microbiota associated with hypertension by analyzing microbial interactions and metabolite changes.

This paper introduces EADTN, an ensemble model for drug-target interactions that improves predictive performance and identifies false-negative samples through adaptive feature weighting and clustering fine-tuning.

DeepBIO is a deep-learning platform that automates biological sequence analysis, offering 42 algorithms and visualization tools for functional prediction and annotation.

This paper introduces PHAT, a deep graph learning framework for peptide secondary structure prediction, achieving better accuracy and interpretability for short peptides.

The paper develops a database of viral post-translational modification sites and a classifier for predicting viral protein phosphorylation sites.

The paper builds a database of nucleic acid binding proteins and predicts their functions using machine learning with 77.43% accuracy.

mAML is an automated ML pipeline for microbial classification in human diseases, achieving high performance on 13 benchmark datasets and providing a repository of 12,429 metagenomic and 38,643 amplicon samples.

This paper introduces STAM, a method using multi-layer networks to identify drug treatment patterns by analyzing tissue-specific PPI networks and drug-target modules.

This paper introduces a structural perturbation method (SPM) to predict miRNA-disease associations, showing improved accuracy over existing methods in cross-validation and case studies.

The paper develops classifiers using miRNA and DNA methylation data to predict tumor origin with high accuracy and provides a webserver for clinical use.