Lang Li

Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer

This paper introduces EmitGCL, a deep-learning framework that predicts future metastasis in cancer with high accuracy, identifies biomarkers, and validates therapeutic targets.

ToxiVerse: A Public Platform for Chemical Toxicity Data Sharing and Customizable Predictive Modeling

ToxiVerse provides a public platform for chemical toxicity data sharing and predictive modeling using curated datasets and machine learning tools.

Identifying Novel Therapeutic Targets for Overcoming TNBC Chemo Resistance Through Comprehensive CRISPR-Cas9 Genome Screening

This study uses CRISPR-Cas9 and RNAseq to identify novel synthetic lethal targets in TNBC cells resistant to cisplatin/doxorubicin, highlighting potential drug targets for combination therapies.

General model with biclustering for target selection in severe preeclampsia

The paper develops a biclustering regression model to identify key genes and their relationships with clinical phenotypes in severe preeclampsia, recommending 81 potential druggable targets.

Identifying Representative Cell Line Models for TNBC Chemotherapy Drug Resistance via Systems Biology and Bioinformatics

CRISPR Double Knockout Library Reveals Synthetic Lethal Gene Pairs in Triple-Negative Breast Cancer

DSCN: double-target selection guided by CRISPR screening and network

DSCN selects effective target combinations by integrating CRISPR screening data and protein-protein interaction networks, showing high accuracy and speed compared to existing methods.

DGCyTOF: deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data

DGCyTOF improves cell type identification in single-cell mass cytometry data using deep learning and visualization, achieving high accuracy and intuitive clustering.

Deep Transfer Learning of Drug Responses by Integrating Bulk and Single-cell RNA-seq data

This paper introduces scDEAL, a deep transfer learning framework that predicts cancer drug responses from single-cell RNA-seq data by integrating bulk RNA-seq drug response data and interpretable gene signatures.

Multimodal single-cell/nucleus RNA-sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease