Wednesday · 16 September 2026 · 10:30
Deciphering Gene Regulatory Logic through Interpretable Deep Learning
Dr. Damla Övek Baydar
Postdoctoral Researcher, Norwegian Centre for Molecular Biosciences and Medicine (NCMBM), University of Oslo
Abstract
Transcriptional gene regulation governs cellular identity by translating the static genomic sequence into dynamic gene expression programs. A central component of this process is the binding of transcription factors (TFs) to cis-regulatory elements such as promoters and enhancers. Deciphering the regulatory logic of TF-DNA interactions remains a central challenge, as TF binding is governed by complex, context-dependent interactions that extend beyond canonical sequence motifs. Traditional models, such as position frequency matrices, fail to capture these combinatorial and structural dependencies.
This talk will explore how interpretable deep learning models can learn high-resolution, context-aware representations of TF binding directly from genomic sequence. These approaches are complemented by scalable computational pipelines and database infrastructure that enable the training, interpretation, and integration of such models into JASPAR, an international open- access community resource and ELIXIR core data resource. Model-derived representations further provide a framework for systematically characterizing regulatory grammar across cellular contexts. Building on these efforts, the talk will conclude with a research program centered on multimodal and interpretable AI for regulatory genomics. Integrating sequence, structural, and chromatin features with computational modeling, this program seeks to uncover the mechanistic principles governing gene regulation and its perturbation in disease. More broadly, the goal is to develop AI methods that are not only predictive but also interpretable, biologically grounded, and capable of generating testable hypotheses about regulatory mechanisms.
