Ali Tuğrul Balcı

The paper identifies tissue-specific transcriptional regulatory programs in rats undergoing endurance exercise training by integrating chromatin accessibility, DNA methylation, and gene expression data.

The paper introduces MORALE, a domain adaptation framework for cross-species TF binding prediction, achieving state-of-the-art performance without adversarial training.

This paper introduces dLEM, a differentiable loop extrusion model that predicts genome folding mechanisms from contact maps and integrates with deep learning to infer extrusion parameters from genomic features.

The paper introduces motifDiff, a tool for variant effect prediction using biophysical TF binding models, showing it effectively scores variants and provides insights into human accelerated regions.

The paper introduces L0 segmentation, a framework for extracting locally coherent signals from diverse epigenomic data, showing it can identify subtle features missed by other methods.

The paper introduces tiSFM, an interpretable neural network for sequence-to-function learning, which outperforms existing models in predicting open chromatin and transcription factor activities.

This study profiles multi-omic changes in multiple tissues of rats during endurance exercise training, identifying molecular adaptations and sex-specific responses across time.