Hui Liu

This paper introduces LiveMat, a framework that transforms literature into a computable design space for living materials, improving reasoning across domains.

scDrugAtlas integrates single-cell drug response data to study tumor heterogeneity and drug resistance across multiple cancer types and organisms.

This paper introduces SSDA4Drug, a semi-supervised transfer learning method that improves single-cell drug response prediction using bulk transcriptomic data, showing superior performance with few labeled samples.

The paper analyzes single-cell and spatial transcriptomes to identify co-inhibitory cell-cell communications in lung adenocarcinoma histological progression, revealing key interactions linked to immunosuppression and poor clinical outcomes.

This paper uses weakly supervised contrastive learning to infer molecular subtypes and recurrence risk in breast cancer from unannotated pathology images, achieving strong cross-cohort performance and alignment with pathologist annotations.

This paper introduces wsi2rppa, a weakly supervised contrastive learning framework that infers protein levels and drug response in breast cancer from whole slide images, achieving 0.79 AUC for trastuzumab response prediction.

This paper introduces ATMTCR, a contrastive learning model for predicting TCR-antigen binding specificity, showing improved performance over existing methods.

This paper introduces a self-supervised learning method, ATMOL, for molecular property prediction using attention-wise graph masking and contrastive learning, achieving state-of-the-art performance.

SynLethDB 2.0 is a database that compiles synthetic lethal gene pairs and related knowledge to aid in anticancer drug discovery, including over 16,000 new synthetic lethal interactions.

DeepDDS uses a graph neural network with attention to predict synergistic drug combinations, outperforming existing methods on benchmark and independent test data.

This paper introduces pathDNN, a pathway-guided deep neural network that improves interpretability and predictive performance in drug sensitivity modeling for cancer cells.

DrugCombDB integrates drug combination data with quantitative dose-response information to support experimental and computational screening of drug combinations.