Area of research
Molecular Biology · Oncology
Research interest
Research interests include Cancer Immunotherapy and Biomarkers, Ferroptosis and cancer prognosis, Metabolomics and Mass Spectrometry Studies, and Cancer-related molecular mechanisms research.
Autophagy in <scp>PE</scp> : Dispute, Role and Potential Target
Predicting response and survival of lung adenocarcinoma under anti-programmed death-1 therapy using biological deep learning
Predicting response and survival of lung adenocarcinoma under anti-programmed death-1 therapy using biological deep learning.
A cell-interacting and multi-correcting method for automatic circulating tumor cells detection
Single cell analysis unveils B cell-dominated immune subtypes in HNSCC for enhanced prognostic and therapeutic stratification
Shaping immune landscape of colorectal cancer by cholesterol metabolites
BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs
Improving compound-protein interaction prediction by focusing on intra-modality and inter-modality dynamics with a multimodal tensor fusion strategy
Nomogram Predicts Prognostic Factors for Head and Neck Cutaneous Melanoma: A Population-Based Analysis
A Novel Competing Endogenous RNA Network Reveals Potential Mechanisms and Biomarkers of Chemoresistance in Lung Adenocarcinoma
Identifying survival of pan-cancer patients under immunotherapy using genomic mutation signature with large sample cohorts.
Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade
E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image
E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image
Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations
Identifying survival of pan-cancer patients under immunotherapy using genomic mutation signature with large sample cohorts
A Novel Acetylation-Immune Subtyping for the Identification of a BET Inhibitor-Sensitive Subgroup in Melanoma
Improving ovarian cancer treatment decision using a novel risk predictive tool
<scp>LncRNA</scp> ‐412.25 activates the <scp>LIF</scp> / <scp>STAT3</scp> signaling pathway in ovarian granulosa cells of Hu sheep by sponging <scp>miR</scp> ‐346
Single-Cell Sequencing of Malignant Ascites Reveals Transcriptomic Remodeling of the Tumor Microenvironment during the Progression of Epithelial Ovarian Cancer
A Novel Risk Score to Predict In-Hospital Mortality in Patients With Acute Myocardial Infarction: Results From a Prospective Observational Cohort
A novel subtype to predict prognosis and treatment response with DNA driver methylation–transcription in ovarian cancer
A novel subtype to predict prognosis and treatment response with DNA driver methylation-transcription in ovarian cancer.
Biologically interpretable deep learning to predict response to immunotherapy in advanced melanoma using mutations and copy number variations
LncRNA-UCA1 regulates lung adenocarcinoma progression through competitive binding to miR-383
Methylation biomarkers with discriminating ability are potential therapeutic targets in lung adenocarcinoma.
A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening
Deficiency of Mettl3 in Bladder Cancer Stem Cells Inhibits Bladder Cancer Progression and Angiogenesis
The Discovery of New Drug-Target Interactions for Breast Cancer Treatment
WaveICA 2.0: a novel batch effect removal method for untargeted metabolomics data without using batch information