Frida M. Delgadillo

Frida M. Delgadillo is a PhD researcher at the University of Texas at El Paso (UTEP) whose work focuses on bioinformatics, genomics, transcriptomics, and computational biology. Her research sits at the intersection of biology and data science, using computational approaches to investigate complex biological systems and identify molecular patterns associated with human disease.

A major focus of Frida's research is cancer genomics, including work investigating transcriptomic signatures and molecular pathways associated with health disparities. Her research has included analyzing gene-expression data to identify differentially expressed genes, regulatory pathways, and molecular signatures, with a particular focus on diseases affecting Hispanic and Latino populations. She has also contributed to research involving the transcriptomic profiling of Hispanic colorectal cancer disparities and computational analysis of disease-related molecular pathways.

Frida has also worked across a broader range of computational biology applications. She co-authored a published review examining the use of omics approaches in reproductive biology and blood-feeding insects, and has contributed to research investigating gene-regulatory pathways involved in mosquito reproduction. These projects demonstrate experience applying bioinformatics and genomic analysis across both human health and environmental/biological research.

Her research environment at UTEP's Border Biomedical Research Center provides experience with interdisciplinary research combining biological sciences, computational analysis, and biomedical applications. She has also been involved in research using large-scale genomic and transcriptomic datasets, including projects exploring the computational infrastructure needed to analyze genetic and transcriptomic contributions to cancer health disparities.

As a research mentor, Frida can help students move from a broad interest in biology or medicine toward a specific, computationally tractable research question. She can guide students through literature review, identifying appropriate public datasets, designing bioinformatics workflows, analyzing gene-expression and genomic data, interpreting results, and developing a coherent research narrative. Her background is particularly valuable for students who want to conduct dry-lab research without requiring access to a traditional laboratory.

Frida is particularly well suited to students interested in bioinformatics, computational biology, genomics, transcriptomics, cancer research, genetics, molecular biology, health disparities, and disease biology. Her experience working with real biological datasets makes her well positioned to help students develop projects that combine meaningful biological questions with rigorous computational analysis.