Data Science and Computational Biology Lab

Research

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Dr. Vanessa Aguiar-Pulido is passionate about problem solving in biomedical research, with a focus on developing tools and algorithms that will impact diagnosis and therapeutics of rare and complex genetic disorders. Her background and interests include big data analytics, machine learning, artificial intelligence, bioinformatics, neuroscience, data mining, ontologies, biomedical data integration, health informatics, epigenetics and omics in general.

Her lab is at the interface of Computer Science and Biomedical Sciences, and her research focuses on understanding the underlying genetic causes of monogenic and complex disorders, how these causes can be influenced by environmental factors, and how these findings can be applied to clinical practice.

The advent of high-throughput sequencing technologies has ushered in a new era of genetic inquiry, decreasing the economic costs and turnaround time notably. However, with this growth of 'omic' data, computational challenges arise. Not only the volume of data is increasing but also the dimensionality, expanding the search space exponentially. As a result, many problems involve such a large set of possible solutions that finding the optimal one in a reasonable amount of time is not feasible. Therefore, developing new approaches for 'intelligent' data analysis is necessary. The work of Dr. Aguiar-Pulido shows how artificial intelligence can be applied to identify potential genetic risk factors in complex genetic disorders (i.e., those that require genetic predisposition and environmental factors for the patient to develop the disease). As part of her research, she has developed methods based on evolutionary computation and strategies using machine learning for candidate gene analysis and identification. In her most recent work, she devised a comprehensive approach that uses machine learning (more specifically, embedded feature selection) to pinpoint biological pathways affected in structural birth defects employing ethnically diverse cohorts. Approaches such as the latter are fundamental to further human disease research in the age of precision medicine.

Funded projects:

• Deep learning and explainable AI to improve race-bias in the treatment of breast cancer. University of Miami - iDSC "Expanding the Use of Collaborative Data Science at UM" (Principal Investigator)
• Machine learning to study and improve race-bias in the treatment of breast cancer. NanoString - GeoMx Whole Transcriptome Atlas Grant: University of Miami (Principal Investigator)
• Center for Accelerated Real Time Analytics (CARTA) (2021-2022). NSF (Project Leader)
• Epilepsy Multiplatform Variant Prediction (2020-2025). NIH (Co-Investigator)
• Progenitor regulation underlying cortical interneuron specification (2018-2023). NIH (Key Personnel)
• Risk Genes and Environmental Interactions in Neural Tube Defects (2018-2023). NIH (Researcher)
• Hybrid sequencing for improved genetic diagnosis in clinical settings (2019-2020). Sackler Research Grant (Principal Investigator)

View Publications