Sofia Salazar-Magaña
sofia.salazarmagana@bcm.edu | sogusama@gmail.com | GitHub | ORCID
Education
PhD Student in Quantitative and Computational Biosciences 2026 –
Baylor College of Medicine, Houston, TX
Bachelor’s degree in Genomic Sciences 2020 – 2024
National Autonomous University of Mexico (UNAM), Querétaro, México.
Research Experience
Data analyst October 2024 – April 2026
Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
PI: Hae Kyung Im, PhD
● Applied a novel lab-developed method TFXcan, to leverage deep learning-based DNA-to-epigenome
predictions to investigate TF-binding alterations associated with breast cancer risk.
● Applied the scPrediXcan framework in two projects: (1) training deep-learning models for
alternative polyadenylation (APA) prediction, and (2) modeling gene expression across dGTEx
developmental contexts.
Undergraduate research assistant July 2023 – October 2024
University of Chicago, Remotely.
PI: Hae Kyung Im, PhD
● Compared the performance of Borzoi and Enformer deep learning models for DNA sequence-based
prediction of epigenomic features.
● Provided technical support for lab-developed software enabling TWAS analyses.
Undergraduate research assistant January 2022 – October 2024
International Laboratory for Human Genome Research, National Autonomous University of Mexico,
Querétaro, México.
PI: Alejandra Medina-Rivera, PhD
● Analyzed RNA-seq data from the Mexican Lupus Registry (LupusMX) to identify key differentially
expressed genes and assembled gene regulatory networks, contributing novel insights into the
disease etiology within an understudied population.
● Assembled a bioinformatic pipeline for RNA-seq data analysis, enabling lab-wide workflows.
Contributions
Foguet, C., Gil, L., Xu, Y., Salazar-Magaña, S., Ritchie, S. C., Persyn, E., Im, H. K., Inouye, M., &
Lambert, S. A. (2026). OmicsPred as a centralized resource for genetic prediction of multi-omic traits.
Nature Genetics. https://doi.org/10.1038/s41588-026-02726-4
Zhou, Y., Adeluwa, T., Zhu, L., Salazar-Magaña, S., Sumner, S., Kim, H., Gona, S., Nyasimi, F., Kulkarni,
R., Powell, J. E., Madduri, R., Liu, B., Chen, M., & Im, H. K. (2025). scPrediXcan integrates deep learning
methods and single-cell data into a cell-type-specific transcriptome-wide association study framework. Cell
Genomics, 5(5), 100875. https://doi.org/10.1016/j.xgen.2025.100875
Hernández-Ledesma A. L., Coss-Navarrete, E. L., Salazar-Magaña, S., Ramírez-Espinosa, D.,
Tinajero-Nieto, L., Torres-Valdez, E., Peña-Ayala, A. H., Félix-Rodriguez, G., Frontana-Vázquez, G., García
Sotelo, J. S., Thomas-Chollier, M., Trynka, G., Rosetti, F, Fernandez-Valverde, S. L., Gutiérrez-Arcelus, M.,
Alpízar-Rodríguez, D. & Medina-Rivera, A. (2026). Gene expression profiling of dendritic cell tolerance
dysfunction in women with systemic lupus erythematosus. Frontiers in Immunology 17, 1771959.
https://doi.org/10.3389/fimmu.2026.1771959
Zhou, Y., Sumner, S., Adeluwa, T., Zhu, L., Salazar-Magaña, S., Kim, H., Gona, S., Nyasimi, F., Kulkarni,
R., Powell, J., Madduri, R., Liu, B., Chen, M., & Im, H. K. (2026). Protocol to perform cell-type-specific
transcriptome-wide association study using scPrediXcan framework. STAR Protocols, 7(1), 104306.
https://doi.org/10.1016/j.xpro.2025.104306
Ogonowski, N. S., Cao, F., Flores-Ocampo, V., Salazar-Magaña, S., Seviiri, M., Song, L., Kugelman, J.,
MacGregor, S., Cuellar-Partida, G., Gharahkhani, P., Im, H. K., Harding, I. H., Yang, J., Nayler, S., Kumar,
K. R., Martin, N. G., Diaz-Torres, S., & Rentería, M. E. GWAS Meta-Analysis of Essential Tremor Uncovers
47 Novel Genetic Loci and Biological Pathways Linked to Brain Morphometry of the Ventral Diencephalon
and Cerebellum. Preprint. https://doi.org/10.1101/2025.11.10.25339873
Conferences
Linking transcription factor binding variation to overall and subtype-specific breast cancer risk
variants using a deep learning-based approach. (Poster). American Society of Human Genetics (ASHG)
Annual Conference, 2025.
Deep learning predicts TF binding variation, illuminating breast and ovarian cancer biology. (Poster).
American Society of Human Genetics (ASHG) Annual Conference, 2024.
Studying the Systemic Lupus Erythematosus expression signature and contextualizing it in a Mexican
cohort. (Talk). Academic Days at LIIGH Institutional Symposium, 2024.
Lupus peripheral blood meta-analysis with Recount3 & Recount3-like processed RNA-seq data.
(Poster). Academic Days at LIIGH Institutional Symposium, 2023.
Lupus peripheral blood meta-analysis with Recount3 & Recount3-like processed RNA-seq data.
(Poster). ISCB-LA SoIBio BioNetMX International Conference on Bioinformatics, 2022.
Teaching Experience
TA at Deep Learning in Genomics course, University of Chicago. Spring ‘26 & ‘25
TA at Quantitative Genomics Training Boot Camp, Columbia University. Summer ‘25 & ‘24
Lecturer at bioinformatics institutional courses, International Laboratory for 2024 & 2023
Human Genome Research.
Certifications & Relevant courses
● Intro to AI-driven Science on Supercomputers, Argonne National Laboratory.
Credential ID: a1ff9ac49d6d6754ce667fae36f8ad83f8fe93a5
● Google’s Data Analytics Professional Certificate
Credential ID: KEBDX9FHLDSG
● Workshop: Analysis of biological and biomedical transcriptomic data using Artificial
Intelligence (AI) and Machine Learning (ML) methods with R, ISCB-LA SoIBio BioNetMX
International Conference on Bioinformatics, 2022.
● Workshop: Introduction to the logical modeling of cellular networks, with applications to
cancer and immunology, International Laboratory for Human Genome Research.
Skills
● Programming (R, Python, Bash, C++, Git, PHP, SQL, Perl).
● Bioinformatics (bulk and single-cell RNA-seq, genome alignment, epigenomic data analysis e.g
ChIP-seq, ATAC-seq).
● Deep learning and acceleration hardware: Pytorch, Tensorflow, CUDA, Intel XPU, MPS.
● Statistics, TWAS, ShinyApp.
● High Performance Computing (SGE, SLURM).
Languages
Spanish (Native) | English (Proficient – TOEFL iBT 118) | French (Basic)
