Rui Yin

GIN-CRC-Pareto: A graph-based Pareto-optimal multi-task learning framework to identify miRNA-target interactions in colorectal cancer

GIN-CRC-Pareto identifies miRNA-target interactions in colorectal cancer using a graph-based multi-task learning framework, outperforming existing methods in accuracy and AUC.

Socioeconomic and Clinical Determinants Driving Access to BRCA Genetic Testing in Cancer : A Case-Control Study Using Observational Electronic Health Records Across Multiple Sites

This study identifies socioeconomic and clinical factors influencing BRCA genetic testing uptake among cancer patients using electronic health records and develops a predictive model to identify those at risk for underuse.

DamageFormer: a damage-aware multimodal deep learning framework for DNA lesion identification from nanopore sequencing

DamageFormer detects and localizes DNA lesions from nanopore sequencing with high accuracy and generalization to unseen damage types.

SEHI-PPI: An End-to-End Sampling-Enhanced Human-Influenza Protein-Protein Interaction Prediction Framework with Double-View Learning

SEHI-PPI predicts human-influenza protein-protein interactions using a double-view deep learning framework with adaptive negative sampling, outperforming existing methods and demonstrating strong generalizability.

High-throughput glioblastoma-neurovascular assembloids model blood-tumor barrier disruption, tumor reprogramming and therapeutic efficacy

This paper develops a high-throughput BTB assembloid model to study glioblastoma-neurovascular interactions and therapeutic responses, finding that BTB disruption correlates with increased tumor invasiveness and drug resistance.

GenoM7GNet: An Efficient N7-methylguanosine Site Prediction Approach Based on a Nucleotide Language Model

GenoM7GNet is a deep learning model for predicting N7-methylguanosine sites using a nucleotide language model, achieving high accuracy and outperforming existing methods.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

Gra-CRC-miRTar is a pre-trained graph neural network framework for identifying miRNA targets in colorectal cancer, achieving better performance than existing methods and revealing functional interactions.

VarPPUD: Variant post prioritization developed for undiagnosed genetic disorders

VarPPUD improves the prioritization of pathogenic variants in undiagnosed genetic disorders by distinguishing likely from unlikely disease-causing variants using machine learning.

ViPal: A Framework for Virulence Prediction of Influenza Viruses with Prior Viral Knowledge Using Genomic Sequences

ViPal improves influenza virulence prediction by integrating prior viral knowledge into machine learning models using genomic sequences.

Deciphering Spatial Domains by Integrating Histopathological Image and Transcriptomics via Contrastive Learning

This paper introduces ConGI, a method that integrates gene expression and histopathological images via contrastive learning to improve spatial domain identification in spatial transcriptomics.

Spatial Transcriptomics Prediction from Histology jointly through Transformer and Graph Neural Networks

Hist2ST predicts RNA-seq expression from histology images using a deep learning model combining Transformer and graph neural networks, outperforming existing methods in gene expression prediction and spatial region identification.

IAV-CNN: a 2D convolutional neural network model to predict antigenic variants of influenza A virus

IAV-CNN predicts influenza A virus antigenic variants using a 2D convolutional neural network with a new amino acid representation, achieving state-of-the-art performance on three datasets.

VirPreNet: A weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all 8 segments

VirPreNet predicts influenza A virus virulence using all 8 viral segments with a weighted ensemble CNN, achieving state-of-the-art performance and highlighting the importance of PB2 and HA segments.

Alignment-free machine learning approaches for the lethality prediction of potential novel human-adapted coronavirus using genomic nucleotide

This study develops alignment-free machine learning models to predict the lethality of novel human coronaviruses using genomic nucleotide sequences, achieving 96.7% accuracy on existing strains.