Xiaowo Wang

SEEKER screens TCR-pMHC combinations to identify antigen recognition, finding polyreactive T cells recognizing up to 80 autoantigens in autoimmune disease.

This paper introduces AnewOmni, a generative model that designs biomolecules across scales by learning interaction patterns and physical constraints from 5 million complexes.

IMMREP25 evaluates TCR:pMHC binding prediction for unseen peptides, showing improved performance with structural modeling but still below random guessing.

This study introduces a mathematical model for Molecular Competition Networks and shows they outperform non-competitive networks in computational tasks.

This paper introduces a transformer-based framework for neoantigen identification, identifies a bias in immunogenicity prediction, and proposes a debiasing strategy to improve model fairness.

Deconer evaluates reference-based cell type deconvolution methods using simulated and real gene expression data, comparing 14 methods across multiple criteria and providing guidance for users.

The paper constructs a generic chromatin accessibility reference (cPeaks) from bulk ATAC-seq data to improve scATAC-seq analysis and cell type identification.

The paper introduces hECA, a cell-centric human Ensemble Cell Atlas, which integrates scRNA-seq data from multiple studies to enable targeted data retrieval, multi-view presentations, and customizable reference creation for cell type annotations.

PccGEO designs functional genetic elements using a knowledge-constrained deep learning model, achieving high success rates in promoter design for E. coli.

ARIC estimates cell type proportions from bulk gene expression or DNA methylation data, showing accuracy in both data types.

This study quantifies how microRNAs modulate gene expression noise and shows how this affects synthetic gene circuits and cell type classification.

DISMIR detects cancer using cfDNA methylation and sequence data with high accuracy at low sequencing depth.

OpenAnnotate provides a web server for efficiently annotating chromatin accessibility of genomic regions across biosamples, tissues, and systems.

NeuronMotif decodes cis-regulatory motifs from DCNNs by analyzing neuron activations, revealing motif combinations and improving model performance.

This study uses a generative adversarial network to design new functional promoters in E. coli, achieving higher success rates and activity than natural promoters.

This study identifies ribosomal DNA methylation as a stable biomarker for detecting cancer in plasma with high accuracy across multiple cancer types.

This study introduces a competition motif model to analyze molecular competition's role in gene regulatory networks, revealing its impact on dose-response curves, response speed, noise, and target correlations.

The paper shows miRNAs reduce gene expression noise in mouse cells using single-cell RNA-sequencing data analysis.

SIP provides an interchangeable pipeline for scRNA-seq data processing, enabling easy integration and customization of analysis steps.

DEsingle identifies three types of differential expression in single-cell RNA-seq by distinguishing real and dropout zeros using a Zero-Inflated Negative Binomial model.