Area of research
Molecular Biology · Artificial Intelligence
Research interest
Research interests include Bioinformatics and Genomic Networks, Gene expression and cancer classification, AI in cancer detection, and Single-cell and spatial transcriptomics.
Identification of Alzheimer's disease subtypes and biomarkers from human multi-omics data using subspace merging algorithm.
Identification of high-risk cells in single-cell spatially resolved transcriptomics data using Diagnostic Evidence GAuge of Single-cells with spatial smoothing.
Novel Computational Pipeline Enables Reliable Diagnosis of Inverted Urothelial Papilloma and Distinguishes It From Urothelial Carcinoma.
Leveraging transcription factor physical proximity for enhancing gene regulation inference.
Deep Transfer Learning Links Benign Glands to Prostate Cancer Progression via Transcriptomics.
A dynamic modeling approach to predict water inflow during tunnel excavation in relatively uniform rock masses
1q amplification and PHF19 expressing high-risk cells are associated with relapsed/refractory multiple myeloma.
Social and Behavior Factors of Alzheimer's Disease and Related Dementias: A National Study in the U.S.
Identification and validation of feature genes associated with M1 macrophages in preeclampsia
Identifying 1q amplification and PHF19 expressing high-risk cells associated with relapsed/refractory multiple myeloma
Artificial Intelligence in Omics.
Optimal transport- and kernel-based early detection of mild cognitive impairment patients based on magnetic resonance and positron emission tomography images.
MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification.
Intron retention-induced neoantigen load correlates with unfavorable prognosis in multiple myeloma.
Evaluating mismatch repair deficiency for solid tumor immunotherapy eligibility: immunohistochemistry versus microsatellite molecular testing
TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining.
BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images.
WEVar: a novel statistical learning framework for predicting noncoding regulatory variants
Optimal Transport-Based Early Detection of Mild Cognitive Impairment Patients Based on Magnetic Resonance Images
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma.
WEVar: a novel statistical learning framework for predicting noncoding regulatory variants
A Deep Learning Approach for Tissue Spatial Quantification and Genomic Correlations of Histopathological Images
Integration of molecular features with clinical information for predicting outcomes for neuroblastoma patients.
Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer.
Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients' survival time.
PTR Explorer: An approach to identify and explore Post Transcriptional Regulatory mechanisms using proteogenomics
TSUNAMI: Translational Bioinformatics Tool Suite For Network Analysis And Mining
Proteogenomic Analysis of Surgically Resected Lung Adenocarcinoma
Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images