Biology subjects

Atas Guvenilir, H.

Publications and source records attributed to Atas Guvenilir, H..

3 recordsLinked to original sources

bioRxiv2026

KG-Orchestra: An Open-Source Multi-Agent Framework for Evidence-Based Biomedical Knowledge Graphs Enrichment.

1.Biomedical Knowledge Graphs (BKGs) offer integrative representations of complex biology, yet their utility is compromised by the limitations of current construction methods: manual curation offers high fidelity but is unscalable, whereas purely automated Large Language Model (LLM) approaches often yield broad networks lacking mechanistic granularity. We present KG-Orchestra, an open-source multi-agent framework designed to build specialized, directional, cause-and-effect BKGs by enriching seed graphs. The framework focuses on increasing granularity within specific topics by leveraging Retrieval-Augmented Generation (RAG) to autonomously acquire, validate, and integrate evidence. The system orchestrates specialized agents for retrieval, schema alignment, and triplet validation with explicit, traceable provenance, transforming sparse seeds into dense, high-resolution resources. We evaluated KG-Orchestra on two specialized contexts--the mechanistic link between Nelivaptan and Alzheimers Disease (NADKG) and the complex probiotic interactions within the gut-brain axis (ProPreSyn-GBA)--across varying computational budgets. Our benchmarking results demonstrate that Qwen 3 variants deliver superior reasoning performance and that hybrid retrieval strategies significantly enhance evidence relevance. Furthermore, the multi-agent architecture ensures high triplet integrity and biological validity through iterative cross-checking and self-correction. The framework remains computationally flexible, deploying from single laptop GPUs to high-performance clusters. By bridging knowledge gaps and adding context-aware entities, KG-Orchestra increases reliability while validating seed assertions against up-to-date sources. This versatility supports critical downstream applications, including completing missing mechanistic pathways, integrating novel entities for drug repurposing, constructing targeted subgraphs from entity lists, and retroactively validating graph evidence for transparent auditing.

bioinformatics↗

bioRxiv2025

ECLIPSE: Exploration of Complex Ligand-Protein Interactions through Learning from Systems-level Heterogeneous Biomedical Knowledge Graphs

Discovering new, efficacious molecules remains slow and costly; rigorous data science-driven systems-level approaches are therefore essential to prioritise hypotheses and de-risk drug development. In this study, we present ECLIPSE, a systems-level framework for compound/ligand-protein interaction (CPI) representation and prediction, combining heterogeneous knowledge graphs (KGs), which encode large-scale entity-relation structure, with graph neural networks that exploit relational inductive biases to perform inference on graph-structured data. ECLIPSE uses our comprehensive biomedical KG-based platform, CROssBAR, incorporating genes/proteins, drugs, compounds, pathways, diseases, and phenotypes, along with their multi-layered relationships. Each entity is assigned input features derived from language or graph representation learning models and projected via type-specific neural network layers. To process these featurized biomedical KGs for bioactivity prediction, we employed the heterogeneous graph transformer (HGT) architecture. In contrast to the majority of GNN algorithms, which are restricted to homogenous graphs, HGT can handle graph heterogeneity and maintain node-and edge-type dependent representations through its attention mechanism. ECLIPSE achieves strong performance on challenging, protein-family- specific CPI benchmarks compared with baseline and state-of-the-art methods; ablations confirm performance gains from modelling graph heterogeneity and all feature sources. Use-case analyses on a druggable kinase (PIM1) and a historically undruggable receptor (HER3) illustrate generalizability across target classes and activity ranges. By leveraging direct and indirect relationships embedded in biomedical KGs, ECLIPSE provides context-aware CPI inference that is scalable to real-world settings. Code, datasets, and trained models are released to support reproducibility and reuse.

bioinformatics↗

bioRxiv2025

CBM KG: A Comorbidity-Centric Knowledge Graph Uncovering Causal Pathomechanisms Between COVID-19 and Neurodegenerative Diseases

SummaryCOVID-19 is increasingly recognized as a potential trigger or accelerator of neurodegenerative diseases such as Alzheimers and Parkinsons. To systematically explore the putative molecular and clinical associations between them, we present CBM KG (Causal Biological Mechanisms Knowledge Graph)--a manually curated, comorbidity-centric resource developed within the EU-funded COMMUTE project. CBM KG integrates over 2,800 cause-and-effect or correlative relationships from 63 peer-reviewed publications, highlighting key mechanisms such as viral entry routes, blood-brain barrier alteration, microglial activation, neuroinflammation, and APOE {varepsilon}4-associated susceptibility. Each relationship in the graph is fully traceable to its source evidence, ensuring transparency and reproducibility. Unlike general-purpose or single disease-focused knowledge graphs, CBM KG is specifically designed to represent causal biological mechanisms spanning both infectious and neurodegenerative processes. By encoding directional, cause-and-effect relationships, it supports the interpretation of clinical co-occurrences through plausible mechanistic links between overlapping disease pathways, offering high-resolution insights at both molecular and clinical levels. Availability and implementationThe BEL files, Neo4j database, and Cytoscape visualization files are publicly available at: https://github.com/SCAI-BIO/CBM-Comorbidity-KG.

bioinformatics↗