Resumen de investigación
Mi línea de investigación se centra en el desarrollo de plataformas de aprendizaje automático que permitan contribuir a solucionar problemáticas de salud, particularmente mediante la identificación y/o cuantificación de biomarcadores, la generación de métodos de apoyo diagnóstico, y la descripción de patrones bioquímicos asociados a patologías humanas con el fin de comprender los mecanismos que las subyacen. Tales plataformas se sustentan en el análisis multivariado de datos obtenidos a partir de registros clínicos, y del estudio de muestras biológicas por medio de técnicas de química clínica y bioanalítica. Hoy mi trabajo está principalmente enfocado en patologías ginecológicas y obstétricas, tales como endometriosis, diabetes gestacional, y preeclampsia. En paralelo, contribuyo al estudio de otras problemáticas biomédicas en colaboración con investigadores/as nacionales e internacionales.
Líneas de investigación
- Inteligencia artificial aplicada en biomedicina
- Química clínica y bioanalítica
- Patologías ginecológicas y obstétricas
Cursos que dicta
Pregrado
- Bases Funcionales de la Medicina I (Medicina)
- Bases Funcionales de la Medicina II (Medicina)
- Química General y Orgánica (Tecnología Médica)
Postgrado
- Ciencia de Datos para la Biomedicina (Magister en Ciencias Biomédicas)
- Investigación Dirigida (Doctorado en Inteligencia Artificial)
- Seminario de Investigación I (Doctorado en Inteligencia Artificial)
Publicaciones académicas
- Prediction of maturity-onset diabetes of the young subtypes using machine learning. Frontiers in Digital Health. https://doi.org/10.3389/fdgth.2026.1656161.
- Targeted and untargeted cross-sectional study for sex-specific identification of plasma biomarkers of COVID-19 severity. Analytical and Bioanalytical Chemistry. https://doi.org/10.1007/s00216-024-05706-x.
- Early-Onset Versus Late-Onset Preeclampsia in Bogotá, Colombia: Differential Risk Factor Identification and Evaluation Using Traditional Statistics and Machine Learning. Biomedicines. https://doi.org/10.3390/biomedicines13081958.
- Evaluation of the bioactivity of Berberis microphylla G. Forst (Calafate) leaves infusion. Food Bioscience. https://doi.org/10.1016/j.fbio.2024.105097.
- Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy. Biomedicines. https://doi.org/10.3390/biomedicines12061142.
- Sex and disease severity-based analysis of steroid hormones in ME/CFS. Journal of Endocrinological Investigation. https://doi.org/10.1007/s40618-024-02334-1.
- Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Frontiers in Endocrinology. https://doi.org/10.3389/fendo.2023.1130139.
- Berberis microphylla G. Forst Intake Reduces the Cardiovascular Disease Plasmatic Markers Associated with a High-Fat Diet in a Mice Model. Antioxidants. https://doi.org/10.3390/antiox12020304.
- Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia. PLOS ONE. https://doi.org/10.1371/journal.pone.0280513.
- Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2022.102378.
- Pathophysiological Role of Genetic Factors Associated With Gestational Diabetes Mellitus. Frontiers in Physiology. https://doi.org/10.3389/fphys.2022.769924.
- Analytical performance of Compton/Rayleigh signal ratio by total reflection X‐ray fluorescence (TXRF): A potential methodological tool for sample differentiation. X-Ray Spectrometry. https://doi.org/10.1002/xrs.3273.
- Increased P2×2 receptors induced by amyloid-β peptide participates in the neurotoxicity in alzheimer’s disease. Biomedicine & Pharmacotherapy. https://doi.org/10.1016/j.biopha.2021.111968.
- Maternal thyroid profile in first and second trimester of pregnancy is correlated with gestational diabetes mellitus through machine learning. Placenta. https://doi.org/10.1016/j.placenta.2020.10.015.
- High levels of maternal total tri-iodothyronine, and low levels of fetal free L-thyroxine and total tri-iodothyronine, are associated with altered deiodinase expression and activity in placenta with gestational diabetes mellitus. PLOS ONE. https://doi.org/10.1371/journal.pone.0242743.
- 17 Oxo Sparteine and Lupanine, Obtained from Cytisus scoparius, Exert a Neuroprotection against Soluble Oligomers of Amyloid-β Toxicity by Nicotinic Acetylcholine Receptors. Journal of Alzheimer's Disease. https://doi.org/10.3233/JAD-180945.