Enhancing Omics Cohort Discovery for Research on Neurodegeneration through Ontology-Augmented Embedding Models

2025-06-16

AI Summary

To address the challenge of semantic heterogeneity and analytical intractability arising from the rapid growth of multi-omics and clinical data in neurodegenerative diseases (NDs), this paper proposes the first semantic indexing framework integrating biomedical ontologies with embedding learning, comprising four stages: metadata standardization, ontology alignment enhancement, PubMedBERT fine-tuning, and natural language question-answering–driven embedding optimization. The framework unifies GEO data mining, UBERON/DOID ontologies, clustering analysis, and automated QA pair generation to construct a semantic embedding space covering 2,801 repositories and over 150,000 samples, mapping 1,700+ heterogeneous tissue labels onto 326 ontology concepts. Retrieval precision improves significantly—from 0.277 to 0.866 (percentile rank: 0.896)—enabling interpretable, queryable cohort discovery and establishing a novel paradigm for precise, knowledge-guided ND data reuse.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The growing volume of omics and clinical data generated for neurodegenerative diseases (NDs) requires new approaches for their curation so they can be ready-to-use in bioinformatics. NeuroEmbed is an approach for the engineering of semantically accurate embedding spaces to represent cohorts and samples. The NeuroEmbed method comprises four stages: (1) extraction of ND cohorts from public repositories; (2) semi-automated normalization and augmentation of metadata of cohorts and samples using biomedical ontologies and clustering on the embedding space; (3) automated generation of a natural language question-answering (QA) dataset for cohorts and samples based on randomized combinations of standardized metadata dimensions and (4) fine-tuning of a domain-specific embedder to optimize queries. We illustrate the approach using the GEO repository and the PubMedBERT pretrained embedder. Applying NeuroEmbed, we semantically indexed 2,801 repositories and 150,924 samples. Amongst many biology-relevant categories, we normalized more than 1,700 heterogeneous tissue labels from GEO into 326 unique ontology-aligned concepts and enriched annotations with new ontology-aligned terms, leading to a fold increase in size for the metadata terms between 2.7 and 20 fold. After fine-tuning PubMedBERT with the QA training data augmented with the enlarged metadata, the model increased its mean Retrieval Precision from 0.277 to 0.866 and its mean Percentile Rank from 0.355 to 0.896. The NeuroEmbed methodology for the creation of electronic catalogues of omics cohorts and samples will foster automated bioinformatic pipelines construction. The NeuroEmbed catalogue of cohorts and samples is available at https://github.com/JoseAdrian3/NeuroEmbed.

Problem

• Enhancing omics cohort discovery for neurodegenerative disease research
• Normalizing heterogeneous metadata using biomedical ontologies and clustering
• Improving retrieval precision for cohort and sample queries

Innovation

• Ontology-augmented embedding for semantic accuracy
• Semi-automated metadata normalization using biomedical ontologies
• Fine-tuned PubMedBERT for enhanced retrieval precision