Description
José A. Rodríguez-Martínez, Associate Professor at the University of Puerto Rico, will discuss their recent work on using computational models to identify cardiovascular disease-associated single-nucleotide polymorphisms (SNPs) that alter the DNA binding of the human cardiac transcription factor GATA4, and validated these findings through in vitro and cellular experiments.
Overview of Presentation
- Understand how non-coding genetic variants can impact transcription factor binding and gene regulation, particularly in the context of cardiovascular diseases.
- Apply machine learning models, such as gapped kmer support vector machine (GKM SVM) for predicting changes in DNA-binding affinity of transcription factors.
- Understand methods used for validating computational predictions, including electrophoretic mobility shift assays and luciferase reporter assays.
- Compare Predictive Models: Understand the differences between SVM-based and position weight matrix-based models in predicting the impact of genetic variants on transcription factor binding.
