Area of research
Molecular Biology · Computational Theory and Mathematics
Research interest
Research interests include Gene expression and cancer classification, Bioinformatics and Genomic Networks, Computational Drug Discovery Methods, and Radiomics and Machine Learning in Medical Imaging.
Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
Prediction of chronic kidney disease progression using recurrent neural network and electronic health records
Converting tabular data into images for deep learning with convolutional neural networks
A cross-study analysis of drug response prediction in cancer cell lines
Ensemble transfer learning for the prediction of anti-cancer drug response
Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling
Imaging-Genomic Study of Head and Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes and Genomic Mechanisms via Integration of The Cancer Genome Atlas and The Cancer Imaging Archive
An Oncolytic Adenovirus Targeting Transforming Growth Factor β Inhibits Protumorigenic Signals and Produces Immune Activation: A Novel Approach to Enhance Anti-PD-1 and Anti-CTLA-4 Therapy
Scalable Bayesian Nonparametric Clustering and Classification
TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma
Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data
Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage
Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data
A Chaperome Subnetwork Safeguards Proteostasis in Aging and Neurodegenerative Disease
TCGA-Assembler: open-source software for retrieving and processing TCGA data