Area of research
Cancer Research · Genetics
Research interest
Research interests include Estrogen and related hormone effects, Cancer Genomics and Diagnostics, Bioinformatics and Genomic Networks, and Cancer, Lipids, and Metabolism.
QSP Modeling Shows Pathological Synergism Between Insulin Resistance and Amyloid-Beta Exposure in Upregulating VCAM1 Expression at the BBB Endothelium.
Lessons learned from a candidate gene study investigating aromatase inhibitor treatment outcome in breast cancer
Influence of gut microbiota on oral drug absorption and metabolism
Supplementary File 3 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Supplementary File 4 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Supplementary File 1 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Supplementary File 2 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks.
Data from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 3 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 1 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 1 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 2 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 4 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 2 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 4 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 3 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.
Supplementary File 3 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Supplementary Figure from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment
Supplementary Data from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment
Supplementary Figure from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment
Supplementary File 3 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Supplementary Figure from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment
Supplementary Figure from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment
Data from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment
Supplementary File 2 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Supplementary Figure from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment
Supplementary File 1 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
Supplementary File 4 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer