Yanjie Wei

VarDCL: A Multimodal PLM-Enhanced Framework for Missense Variant Effect Prediction via Self-distilled Contrastive Learning

VarDCL predicts pathogenic missense variants by integrating multimodal protein language model embeddings and self-distilled contrastive learning, achieving high performance on clinical variants.

RabbitVar: ultra-fast and accurate somatic small-variant calling on multi-core architectures

RabbitVar is a somatic variant caller that achieves high accuracy and efficiency for small variants in cancer genomics, outperforming existing tools on real-world datasets.

Allo-PED: Leveraging protein language models and structure features for allosteric site prediction

AlloPED predicts allosteric sites using protein language models and machine learning, achieving high accuracy on benchmark datasets.

Learning the native-like codons with a 5'UTR and RNA secondary structure aided species-informed transformer model

TransCodon is a Transformer-based model that predicts optimal synonymous codons by integrating species-specific information, 5'UTRs, and RNA structure, improving translational efficiency in heterologous hosts.

Exploring Pathogenic Mutation in Allosteric Proteins: the Prediction and Beyond

The paper predicts pathogenic mutations in allosteric proteins using a machine-learning model and identifies significant pathogenic mutations at allosteric sites.

ThermoLink: Bridging Disulfide Bond and EnzymeThermostability through Database Construction andMachine Learning Prediction

The paper curates a database of disulfide bonds and their effects on protein thermostability, develops machine learning models to predict beneficial disulfide bonds, and demonstrates the utility of AlphaFold2 for this task.

RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints

RevGraphVAMP improves protein simulation analysis by combining graph neural networks and physical constraints, achieving higher accuracy in dimensionality reduction and state transition prediction.

A New Paradigm for Applying Deep Learning to Protein-Ligand Interaction Prediction

This paper introduces IGModel, a deep learning framework that predicts protein-ligand docking RMSD and binding strength, achieving state-of-the-art performance on multiple benchmarks.

Distinct Neurodevelopmental Patterns and Intermediate Integration-Based Predictive Modeling in Autism Spectral Disorder

This study identifies distinct neurodevelopmental patterns in ASD and introduces LMFGCN, a deep-learning model that improves ASD classification by integrating cortical morphology data.

Deep-learning based bioactive therapeutic peptides generation and screening

This paper generates and screens bioactive peptides using deep learning models, demonstrating their potential for therapeutic applications.

Variational graph encoders: a surprisingly effective generalist algorithm for holistic computer-aided drug design

The paper introduces a generalist model for drug design that predicts multiple properties simultaneously, achieving faster virtual screening and drug development with explainability.

RabbitTClust: enabling fast clustering analysis of millions bacteria genomes with MinHash sketches

RabbitTClust clusters large bacterial genome datasets using MinHash sketches, achieving fast processing times for millions of genomes.

piRPheno: a manually curated database to prioritize and analyze human disease related piRNAs

piRPheno is a manually curated database that provides experimentally supported piRNA-disease associations with confidence scores and clinical correlations.

A novel machine learning based approach for iPS progenitor cell identification

This paper develops a machine learning model to identify iPS progenitor cells using microscopic image analysis, achieving over 52% precision in detecting these rare cells during early reprogramming.