Ahmet Süreyya Rifaioğlu

CROssBARv2 integrates heterogeneous biomedical data into a knowledge graph with embeddings and LLM-driven tools for predictive modeling and question-answering.

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

SpaCEy identifies spatial tissue patterns linked to clinical outcomes using explainable graph neural networks in proteomic cancer data.

The paper identifies a spatiotemporal cancer cell trajectory in glioblastoma that explains tumor heterogeneity across regions and subclones.

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.

The paper evaluates sequence- and structure-based methods to predict specificity-determining positions in Axl complexes, finding sequence-based methods overpredict and that combining them with HADDOCK improves predictions.

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.