Yijie Wang

This paper introduces mmFEA, a kinetic-aware framework for estimating metabolic flux changes from transcriptomics and metabolomics data, showing improved accuracy over baseline methods.

Fitting a linear ODE with an equilibrium term via its matrix-exponential solution, not finite differences, raises TF-network AUROC over competing methods on simulated and scRNA-seq data.

The paper introduces HIL-DD, a human-in-the-loop drug design framework that uses an Equivariant Rectified Flow Model to enable efficient collaboration between human experts and AI for generating drug-like molecules.

The paper develops a machine learning model to predict SARS-CoV-2 mutation trends using historical data, achieving low prediction error and robustness across mutation patterns.

This paper introduces SLGNN, a sparse learning approach for drug-protein binding prediction that identifies chemically valid key structures with better predictive power than existing methods.

This study uses TCGA transcriptomics data to analyze metabolic shifts in 11 cancer subtypes and their adjacent normal tissues, revealing patterns in glucose uptake, glycolysis, TCA cycle, and glutaminolysis.

AIscEA integrates single-cell gene expression and chromatin accessibility data by aligning cell types based on biological consistency, showing improved robustness and accuracy over existing methods.

DEGAS transfers disease information from patients to cells using deep transfer learning, identifying PHF19high myeloma cells linked to progression.

The paper introduces NetREX-CF, a method combining collaborative filtering and sparse network component analysis to reconstruct gene regulatory networks, outperforming existing methods.

PopCorn identifies and maps sub-populations across single-cell experiments with high precision.

AptaBlocks designs RNA sticky bridges to connect aptamers and therapeutic cargoes, enabling efficient RNA-based drug delivery systems.

NetREX constructs context-specific regulatory networks by integrating expression data and context-agnostic networks, showing improved biological consistency in Drosophila data.