Liewei Wang

This study identifies ZNF251 deficiency as a driver of PARP inhibitor resistance in BRCA1-mutant breast cancer, showing that targeting pseudo-senescence with senomorphic or senolytic therapies can overcome resistance.

Pisces enhances drug combination synergy prediction by generating 64 augmented views per drug pair, achieving state-of-the-art results across multiple prediction tasks.

The paper identifies FNDC4 as a gene associated with alcohol use disorder through GWAS and shows its role in regulating neurogenesis via alternative splicing and CRISPR/Cas9 experiments.

This paper evaluates the use of GPT for drug sensitivity prediction using pharmacogenomics data, showing improved performance through prompt engineering and fine-tuning.

This paper develops chemotherapy-specific predictors for triple-negative breast cancer using patient-derived xenografts and multi-omic data, achieving high AUROC scores for response prediction.

OmicsFootPrint integrates and interprets multi-omics data using circular images and deep neural networks, achieving high AUC scores in cancer subtype classification and drug response prediction.

NetActivity improves gene set activity scores by using interpretable autoencoders, showing better consistency and biological relevance in prostate cancer analysis.

The paper shows glucocorticoid exposure reveals the function of non-coding genetic variants linked to disease risk by influencing gene expression in relevant biological processes.

The paper identifies transcriptional network disruptions in drug-resistant prostate cancer using TraRe, uncovering key regulatory modules and transcription factors associated with resistance.

This study shows glucocorticoids regulate alternative polyadenylation in human B-lymphoblastoid cells, affecting gene function and immune-related pathways.

This study identifies a novel interaction between TREM2 and TDP-43, showing TREM2 mediates microglial neuroprotection against TDP-43-related neurodegeneration.

This paper develops a deep learning framework called TINDL to predict cancer patient drug response and identify drug sensitivity biomarkers using preclinical samples, showing improved performance over other models.

The paper quantifies tyrosine phosphorylation in FFPE tissues to reveal patient-specific signaling networks and identifies oncogenic kinases in cancer tumors.

PANOPLY prioritizes cancer drugs for individual patients using multi-omics data and machine learning, showing effectiveness in a chemotherapy-resistant tumor model.

This study introduces pGENMi, a probabilistic model that integrates multi-omics data to identify transcription factors regulating cell line response to cytotoxic treatment, finding 41% validation rate for predicted associations.

The paper introduces ProGENI, a gene prioritization method that improves drug sensitivity prediction by integrating gene expression and prior biological knowledge, identifying new genes involved in chemoresistance.