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Çarşamba · 16 Eylül 2026 · 10.30

Deciphering Gene Regulatory Logic through Interpretable Deep Learning

Dr. Damla Övek Baydar

Doktora Sonrası Araştırmacı, Norwegian Centre for Molecular Biosciences and Medicine (NCMBM), University of Oslo

• Tarih ve saat16 Eylül 2026 · 10.30
• YerHacettepe YZVM · Seminer Odası

Özet (İngilizce)

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.

Perşembe · 17 Eylül 2026 · 10.30

Improving Denoising and Diagnostic Accuracy in Low-Dose Computed Tomography through Noise Modelling

Doç. Dr. Şaban Öztürk

Ankara Hacı Bayram Veli Üniversitesi · Ulusal Manyetik Rezonans Araştırma Merkezi (UMRAM), Bilkent Üniversitesi

• Tarih ve saat17 Eylül 2026 · 10.30
• YerHacettepe YZVM · Seminer Odası

Özet (İngilizce)

Computed tomography (CT) is widely used in clinical diagnosis because of its high spatial resolution and rapid imaging capability. However, the biological effects of exposure to ionizing radiation and its association with long-term cancer risk make radiation dose reduction particularly important, especially in repeated examinations. Reducing the radiation dose decreases the number of detected X-ray photons, which in turn increases image noise and may reduce the visibility of low-contrast or diagnostically relevant structures. Although deep learning-based methods have achieved substantial progress in low-dose CT denoising, challenges such as oversmoothing, loss of fine anatomical details, and the reliability of image content generated by generative models mean that the balance between noise suppression and preservation of diagnostic information remains an important research problem.

This talk will focus on modelling noise in low-dose CT and preserving diagnostic information while reducing noise using deep learning methods. In this context, recent developments in low-dose CT denoising will be discussed together with our previous work, ongoing research on noise modelling, and new approaches that more explicitly account for dose and noise characteristics. Finally, future research directions toward more reliable preservation of diagnostically important structures through the joint use of projection-domain and image-domain information will be presented.