Education

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1 National Autonomous University of Mexico (UNAM) 2021 -- 2025 Bach… Juri… <chr>

Relevant Coursework: Calculus, Statistics, Linear Algebra, Molecular and Cell Biology, Statistical Models in Human Genetics, Human Genomics in Health and Disease, Cancer Genomics, Evolutionary Genomics, Bioinformatics and Statistics, Machine Learning Applications in Genomics.

Research Experience

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1 Laboratory of Molecular Cancer Biology, Center for Ca… Augu… Rese… Leuv… <chr>

Supervisors: Jean-Christophe Marine, PhD. & Joanna Pozniak, PhD.

Analyzed the perivascular niche in melanoma using Molecular Cartography spatial transcriptomics data.
Designed pipelines for transcript assignment, quality assessment, and robust cell-type annotation.
Built spatial neighbor graphs and co-occurrence models to study cell–cell interactions.
Contributed to and adapted Python pipelines for processing multiplexed tissue imaging data (NanoNail and CIVO micro-dosing devices)
Implemented automated image preprocessing and segmentation workflows using Cellpose and Dask for large-scale datasets.
Performed spatial analyses and clustering to evaluate treatment responses at single-cell resolution.
Processed and analyzed bulk RNA-seq data from treated melanoma cell lines using HPC workflows.
Identified treatment-specific gene signatures and enriched pathways through differential expression and functional analysis.
Quantified volumetric and surface changes in melanoma cell lines under Palbociclib treatment.
Applied vector-based models to characterize magnitude and direction of cellular responses.

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1 Cancer Genomics and Bioinformatics Lab, International… May … Rese… Juri… <chr>

Supervisors: Carla Daniela Robles Espinoza, PhD. & Martha Estefanía Vázquez Cruz, PhD.

Analyzed spatial proteomics data from acral lentiginous melanoma patient samples using GeoMx Digital Spatial Profiler (35 protein markers).
Developed and benchmarked machine learning models (Random Forest, Logistic Regression) to predict ulceration status from tumor and TME regions.
Applied correlation filtering, PCA, and upsampling with 10-fold cross-validation (PR-AUC ≈ 0.98 in TME samples).
Identified immune-related markers (e.g., CD8, PD-1) as the strongest predictors of ulceration.
Documented analyses in R and prepared a technical report with reproducible workflows.
Gained hands-on training in molecular biology techniques, including nucleic acid extraction, quantification, and agarose gel electrophoresis.
Cultured mammalian cell lines (NIH 3T3, A375 melanoma): thawing, passaging, contamination detection, and cryopreservation.
Performed bacterial transformation, plasmid amplification, and purification with quality control.
Applied functional assays (foci formation, crystal violet staining, scratch, proliferation) to assess oncogenic potential.
Conducted mammalian cell transfection and antibiotic selection; prepared buffers, reagents, and sterile culture media.

Publications

Vazquez-Cruz, M. E., Basurto-Lozada, P., Molina-Aguilar, C., Orozco-Ruiz, S., Van Haastrecht, B., Simonin-Wilmer, I., Martinez-Said, H., Alvarez-Cano, A., Garcia-Ortega, D. Y., Hidalgo-Miranda, A., Hinojosa-Ugarte, D., Ferreira, I., Tavares-De-La-Paz, L. A., Olguin, J. E., Salinas, I., Rodriguez-Perez, A., Martinez-Gomez, J. M., Van Der Zee, I., Grimes, D. R., . . . Robles-Espinoza, C. D. (2025). The microenvironment of ulcerated acral melanoma is characterised by an inflammatory milieu and an enhanced humoral immune response. medRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2025.05.05.25325616

Presentations & Talks

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1 Mini-Symposium: Acral Melanoma Research in Latin Amer… May … Mach… Inte… <chr>

Presented research on applying machine learning (Random Forest, Logistic Regression, LASSO) to spatial proteomics data from Mexican acral melanoma patients.
Discussed methodology (data preprocessing, feature selection, PCA, upsampling) and evaluation metrics (PR-AUC).
Discussed where such models could fit into melanoma prognosis in the clinic.

Teaching & Mentorship Experience

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1 5th Summer School: Introduction to Genomic Sciences, … July… Invi… Remo… <chr>

Talk title: Genes, code, and decisions: lessons on starting a scientific career.

Delivered an interactive talk as part of the “My Science and My Life” series.
Shared personal trajectory and lessons for early-career students, combining genomics, coding, and career decision-making.

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1 Undergraduate Program in Genomic Sciences, UNAM November 20… Gues… Remo… <chr>

Assisted in teaching “Introduction to Single-Cell and Spatial Transcriptomics” lecture for third-semester undergraduate students.
Clarified core concepts and answered student questions.

Achievements & Funding

UNAM High Academic Performance Scholarship 2023-2024.
UNAM High Academic Performance Scholarship 2022-2023.
UNAM High Academic Performance Scholarship 2021-2022.
UNAM Financial Support Scholarship 2021.

Courses

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  <chr>        <glue>        <chr>                                   <chr> <lis>
1 VIB Training December 2024 Hands-on introduction to targeted spat… Leuv… <lgl>
2 VIB Training November 2024 Machine Learning and Deep Learning Wor… Ghen… <lgl>
3 VIB Training November 2024 Nextflow for reproducible and automate… Leuv… <lgl>
4 VIB Training October 2024  Introduction to NGS analysis            Leuv… <lgl>
5 VIB Training October 2024  Docker and Apptainer (Singularity) for… Leuv… <lgl>

Technical Skills & Interests

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1 R, Python, Bash, SQL, MATLAB, Git                      <NA>  Prog… <NA>  <chr>
2 GitHub, High Performance Computing (HPC), Conda, Dock… <NA>  Bioi… <NA>  <chr>
3 tidyverse, ggplot2, plotly, seaborn, matplotlib, pand… <NA>  Data… <NA>  <chr>
4 Bulk RNA-seq, Single-cell RNA-seq, Spatial Transcript… <NA>  Omic… <NA>  <chr>
5 DNA/RNA extraction and quantification, Mammalian cell… <NA>  Labo… <NA>  <chr>
6 Spanish (native), English (proficient)                 <NA>  Lang… <NA>  <chr>
7 Computational biology and cancer genomics              <NA>  Inte… <NA>  <chr>

I work on computational biology and cancer genomics, mostly with spatial and single-cell omics. My aim is to apply statistical and machine learning methods to cellular heterogeneity and to how tumors and their microenvironment respond to therapy. I also build reproducible multi-omics workflows that scale on high-performance computing systems.