Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque
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Abstract
Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., ‘drug treats disease’, ‘gene interacts with gene’). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.
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Embedding techniques can reduce the dimensionality of input data by several orders of magnitude without affecting the predictive capacity of downstream AI/ML models.
“It has been shown that embedding techniques can reduce the dimensionality of the input data by several orders of magnitude without affecting the predictive capacity of downstream AI/ML models.196”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [196]
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- Authors
- Fernández-Torras, A., Duran-Frigola, M., Bertoni, M., Locatelli, M., & Aloy, P.
- Affiliations
- Institute for Research in Biomedicine (ES)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Corresponding author
- Aloy, Patrick
- Venue
- Nature Communications
- Published
- 2022-09-09
- Volume
- 13
- Issue
- 1
- Pages
- 5304-5304
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9463154
- OpenAlex
- W4295198017
- Cited by
- 59
- References
- 86
- Citations by year
- 2026: 11 · 2025: 10 · 2024: 22 · 2023: 13 · 2022: 3
- FWCI
- 4.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Generalitat de Catalunya (001-P-001682, 001-P-001647)
- European Commission (101003633)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- biomedical knowledge graphs, knowledge graph embedding, biomedical data integration, data harmonization, protein-protein interaction network, drug response prediction, drug repurposing, biological entities, machine learning features
- MeSH terms
- Machine Learning, Pattern Recognition, Automated, Proteins, Knowledge, Knowledge Bases
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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