Erva Ulusoy

SELFormerMM integrates multiple molecular modalities to improve property prediction, outperforming single-modality models.

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

OmniPath integrates molecular knowledge from 168 resources to support multi-omics analysis, offering curated data, predictions, and tools for causal modeling across bulk, single-cell, and spatial data.

ProtHGT predicts protein functions using a heterogeneous graph transformer that integrates biological knowledge graphs and language models, achieving state-of-the-art performance across GO sub-ontologies.

This study introduces PROBE, a benchmarking framework for evaluating protein representations on function-related tasks, demonstrating its use with ESM2 and three multimodal PLMs.

Domain2GO predicts protein domain-GO term associations using statistical resampling and co-annotation patterns, achieving high performance in molecular function and biological process prediction with low computational cost.