Qiang Yang

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.

Neural Mechanisms of Willed Attention Control

This study investigates the neural mechanisms of willed attention control using fMRI and EEG data, finding distinct activation patterns and decoding capabilities in the frontoparietal decision network.

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.

From Attention Control to Stimulus Selection: Neural Mechanisms Revealed by Multivariate Pattern and Functional Connectivity Analyses

This study uses fMRI and multivariate analysis to show how spatial attention templates guide stimulus selection through neural connectivity and decoding patterns.

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.

Toxoplasma gondii transcription factor AP2XII-8: a key regulator of G1 phase progression and parasite division

This study identifies TgAP2XII-8 as a critical regulator of G1 phase progression and parasite division in Toxoplasma gondii tachyzoites through functional characterization and genomic analysis.

Decoding Visual Spatial Attention Control

The paper analyzes attention-related baseline activity using fMRI and two analytic approaches, finding that multivariate pattern analysis reveals top-down attentional biases across the visual hierarchy, which better predict behavioral performance than univariate activation.