Speaker: Alfonso Rincón Pérez.
Abstract: Understanding the underlying genetic and epigenetic mechanisms that govern gene expression remains a fundamental challenge in molecular biology. This seminar presents a comprehensive data-driven approach to deciphering these regulatory layers, starting from the foundational genetics to the processing and feature extraction of multi-omic sequencing data. We will first discuss our initial machine learning experiments aimed at predicting distinct transcriptional states based on spatial chromatin profiles. Building upon these early findings, the talk will then address the complexities introduced by statistically non-significant genes and transcriptomic technical noise. Finally, we will introduce our ongoing computational frameworks, which shift towards continuous predictive modeling to more accurately capture the true biological magnitude of gene regulation beyond rigid statistical thresholds.