Biology subjects

Aamer, N.

Publications and source records attributed to Aamer, N..

4 recordsLinked to original sources

bioRxiv2025

Towards Understanding The Relationship Between Brain Disorders and the Gut Microbiome with Explainable Graph Neural Networks

MotivationThe communication between the gut microbiome and the brain, known as the microbiome-gut-brain axis (MGBA), is emerging as a critical factor in neurological and psychiatric disorders. This communication involves complex pathways including neural, hormonal, and immune interactions that enable gut microbes to modulate brain function and behavior. However, the specific mechanisms through which gut microbes influence brain function remain poorly understood, and existing computational efforts to understand these mechanisms are simplistic or have limited scope. ResultsThis work presents a comprehensive approach for elucidating the interactions that allows gut microbes to influence brain disorders. We construct a large curated biomedical knowledge graph comprising 586,318 nodes across 16 entity types and 3,573,936 edges spanning 103 relation types, integrating ontological and experimental data relevant to the MGBA. On this graph, we train GNN-GBA, a GraphSAGE-based graph neural network with a DistMult relation-aware decoder, achieving an AUC-ROC of 0.997 and an F1-score of 0.981 on link prediction, outperforming nine baseline methods across four categories. Using GNNExplainer, we extract and rank mechanistic pathways connecting gut microbes to brain disorders, and demonstrate their stability across multiple random initializations. GNN-GBA successfully identified pathways for 125 brain disorders, revealing shared metabolite hubs (including flavonoids, bile acids, and short-chain fatty acids) that mediate gut-brain communication across diverse neurological conditions. Furthermore, we show that the top pathways are consistent with existing literature for three common disorders. Lastly, we develop an interactive dashboard (GutBrainExplorer) to explore thousands of potential mechanistic pathways across 125 brain disorders, which is publicly available at https://sds-genetic-interaction-analysis.opendfki.de/gut_brain/. AvailabilityCode and data are available at https://github.com/naafey-aamer/GNN-GBA. Contactnaafey.aamer@cs.rptu.de

bioinformatics↗

bioRxiv2025

Automating AI Discovery for Biomedicine Through Knowledge Graphs And LLM Agents

The biomedical domains accelerating progress in understanding, early detection, and treatment of diseases has created an exponentially growing and overwhelming body of literature. Researchers must rely on this literature to find relevant information, but navigating this vast landscape has become increasingly challenging, especially for interdisciplinary AI-biomedicine researchers who need to stay current across both highly fast-paced fields. Despite the emergence of LLM systems, retrieving precise, domain-specific literature remains a significant challenge. This paper addresses these challenges by integrating knowledge graphs with scientific literature embedded in large language models to expedite biomedical discovery. We employ a novel strategy to discover the most relevant pathways between biomedical entities in knowledge graphs. These pathways are then leveraged by a multi-agent LLM system to formulate facts from literature, design AI predictors for understanding discovered pathways, and propose wet-lab experiments to validate AI predictions. This approach creates a comprehensive end-to-end methodology for biomedical discovery. Experiments with various biomedical entity pairs demonstrate the frameworks ability to identify highly relevant pathways and design plausible, complex AI predictors with wet lab validation experiments across diverse therapeutic areas. We developed Intelliscope, a web-based dashboard making this framework available to researchers worldwide. This first-of-its-kind platform could significantly accelerate scientific discoveries, potentially leading to breakthroughs in disease understanding, drug repurposing, and therapeutic development.

bioinformatics↗

bioRxiv2025

COMIC: Explainable Drug Repurposing via Contrastive Masking for Interpretable Connections

Many diseases worldwide remain untreated due to the slow and expensive process of drug development. Repurposing existing FDA-approved drugs offers a faster solution, especially with the assistance of artificial intelligence. Despite advancements in AI-driven drug repurposing, current approaches either have lackluster performance or fail to highlight the intricate pathways through which drugs act on diseases. The clinical utility of AI-driven drug repurposing remains constrained by these limitations, particularly for rare and undertreated diseases where data is scarce. To address the need for a precise and explainable predictor, this paper introduces COMIC (COntrastive Masking with Interpretable Connections), a predictor that employs a multi channel architecture consisting of a feature masking branch, which identifies critical drug-disease interaction patterns by extracting the most informative features, and a path masking branch, which highlights relevant biological pathways through which drugs exert their therapeutic effects. Comprehensive evaluation of the COMIC predictor on the PrimeKG knowledge graph (comprising 17,080 diseases, and 4M+ relationships) with nine distinct disease area splits demonstrated a 9.55% average performance improvement over the current state-of-the-art. The practical applicability of the proposed predictor is evaluated on a set of the most recent 30 FDA-approved repurposed drug disease pairs. The COMIC predictor successfully identified 21 of these pairs with high confidence scores. To facilitate real-time drug repurposing investigations, we have developed a publicly available web-based interface for the COMIC predictor (https://drp.opendfki.de/). This application takes disease names as input and returns a ranked list of potential repurposing candidates, along with predicted mechanistic pathways elucidating the drug-disease interactions.

bioinformatics↗

bioRxiv2025

BIND: Large-Scale Biological Interaction Network Discovery through Knowledge Graph-Driven Machine Learning

The complex interactions between biological entities provide valuable insights into fundamental life processes, which pave the way to a deeper understanding of disease mechanisms for the development of innovative therapeutic strategies. To enable large-scale predictions of biological interactions, multifarious AI-driven predictors have been developed. However, most of these are developed by leveraging information from only a limited subset of interaction types, and the broader interaction types landscape could facilitate AI algorithms to learn more informative patterns to predict unknown interactions. To address the need of a robust and precise interaction predictor, we introduce BIND (Biological Interaction Network Discovery), a predictor that leverages simultaneous learning across 10 biological entities and 30 interaction types. To develop BIND, we first evaluate 11 distinct Knowledge Graph Embedding Methods on the largest public biomedical interaction dataset namely PrimeKG. For each relation type, we extracted entity embeddings from the top 5 performing Knowledge Graph Embedding Models (KGEMs) and input them into 7 distinct machine learning classifiers. Rigorous evaluation of 1,050 predictive pipelines demonstrated that specific combinations of KGEMs and classifiers achieved F1 scores of 90% to 99% across various interaction types. Comprehensive evaluation across each relation type identified the top-performing predictive pipelines, which became the foundation of the BIND web application. To reveal the practical utility of our web application in identifying novel biological interactions, we conducted a case study on drug-phenotype interactions. The application gave 1,355 high confidence predictions, from which potential interactions were subsequently validated by scientific evidence found within the existing literature (Table 5). We believe BINDs web applications public access will serve as a valuable tool for biologists to identify unknown interactions that can be subsequently validated through wet-lab experiments. O_TBL View this table: org.highwire.dtl.DTLVardef@18666a0org.highwire.dtl.DTLVardef@bbb608org.highwire.dtl.DTLVardef@1ac062borg.highwire.dtl.DTLVardef@c90ea1org.highwire.dtl.DTLVardef@1df9369_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 5.C_FLOATNO O_TABLECAPTIONPredicted Drug-Phenotype Associations with Strong Literature Support C_TABLECAPTION C_TBL

bioinformatics↗