Biomedical, University of Texas at San Antonio, United States
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Short Bio
Dr. Mario A. Flores is an Assistant Professor at the University of Texas at San Antonio, specializing in artificial intelligence models for disease phenotype predictions, biomarker identification, and explainable mechanisms. His innovative research integrates various AI techniques to enhance our understanding of disease progression, particularly in oncology.
Education
Dr. Flores holds a Bachelor’s degree in Electronics Engineering from the Metropolitan Autonomous University, a Master’s in Applied Mathematics, and a PhD in Electrical Engineering (Computational Biology) from the University of Texas at San Antonio. He completed his postdoctoral fellowship at the National Center for Biotechnology Information (NCBI), NIH.
Experience
Since 2020, Dr. Flores has served as an Assistant Professor with joint appointments in Electrical and Computer Engineering (ECE) and Biomedical Engineering (BME) at UTSA. His prior roles include NIH Postdoctoral Fellow at NCBI and Research Associate at the Greehey Children’s Cancer Research Institute, showcasing his extensive experience in computational biology and bioinformatics.
Research Interests
Dr. Flores’s research focuses on developing AI tools for disease gene dependence prediction, utilizing spatially resolved transcriptomics, single-cell RNA sequencing, and Electronic Health Records (EHRs) to analyze tumor microenvironments. His work aims to bridge gaps in understanding disease mechanisms and improve patient outcomes through precision medicine.
Awards
Dr. Flores has received numerous awards for his research, including funding from the NIH for projects on neural circuits inhibiting pain, and recognition from the AIM-AHEAD Fellowship program, supporting his efforts to address health disparities in minority populations.
Publications Top Notes
Dr. Flores has authored several impactful publications, including:
New tools for spatial biology transcriptomics & proteomics in immuno-oncology, Immuno-Oncology Insights, 2023.
Deep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis, Brief in Bioinformatics, 2022.
Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions, Cancers, 2022.