Rengül Çetin-Atalay

The paper uses transfer learning to predict drug-target interactions for mitochondrial one-carbon metabolism enzymes with limited data, identifying potential repurposable drugs.

The paper introduces MoCL-GAT, a self-supervised learning framework for molecular representation, achieving state-of-the-art performance on molecular property prediction tasks.

This study investigates the role of arginine and its metabolism in TGF-β-induced activation of lung fibroblasts, finding that arginine depletion inhibits mTORC1 activation and collagen production.

The paper shows HIF-1α regulates glycolytic shifts and mitochondrial function differently in bone marrow-derived macrophages compared to tissue-resident alveolar macrophages.

This study investigates how ATF4 and mTOR regulate metabolic reprogramming in TGF-β-treated lung fibroblasts, identifying their roles in amino acid homeostasis and glycolytic pathways.

This study shows that Gα12 and Gα13 proteins are essential for TGF-β-induced myofibroblast differentiation by regulating Smad phosphorylation and receptor expression.

DRUIDom predicts compound-target interactions using protein domain modularity, identifying 27,032 high-confidence associations and validating them experimentally on LIM kinases.

The paper introduces CROssBAR, a resource that integrates biomedical data and uses deep learning to predict entity relations, displaying results in knowledge graphs for systems-level analysis of diseases like COVID-19.

DEEPScreen predicts drug-target interactions using convolutional neural networks with 2-D compound representations, achieving better or comparable performance to existing methods.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.