Volkan Atalay

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

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.

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.

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.