Yixuan Wang

Generative Modeling of Mouse Embryogenesis for Fate and Disease Prediction

Navigo models mouse embryogenesis to predict cell fates and disease by learning developmental trajectories from scRNA-seq data.

cfDecon: Accurate and Interpretable methylation-based cell type deconvolution for cell-free DNA

cfDecon improves cfDNA deconvolution accuracy and interpretability using deep learning, outperforming existing methods in simulation and clinical datasets.

AcrNET: Predicting Anti-CRISPR with Deep Learning

AcrNET predicts anti-CRISPR proteins using a deep learning model, achieving significant performance improvements and class-level predictions.

Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

TAPE deconvolves bulk RNA-seq data into cell-type fractions and gene expression using a deep autoencoder, showing better performance and robustness compared to existing methods.

Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions

A BERT-style model pretrained on 23.7 million unlabelled ncRNA sequences gives embeddings that improve secondary structure, contact, distance-map and some function predictions over task-specific and MSA-based baselines.

scMinerva: an Unsupervised Graph Learning Framework with Label-efficient Fine-tuning for Single-cell Multi-omics Integrated Analysis

scMinerva is an unsupervised graph learning framework for single-cell multi-omics analysis that outperforms existing methods with few labels and provides biomarkers for COVID-19 cell differentiation.

conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics

conST learns embeddings from gene expression, spatial neighbors, and histology via three-level contrastive learning, reaching adjusted Rand index 0.65 on spatialLIBD slice 151673, with GNNExplainer-based explanations.

Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration

This paper introduces a framework to align and integrate single-cell RNA-seq and ATAC-seq data, achieving higher accuracy than existing methods on both simulated and real data.

CLMB: deep contrastive learning for robust metagenomic binning

CLMB improves metagenomic binning by using deep contrastive learning to handle noise, outperforming existing methods and recovering more high-quality genomes.