Autophagy dark genes: Can we find them with machine learning?
Open accessAbstract
Identifying novel autophagy (ATG) associated genes in humans remains an important task for understanding this fundamental physiological process. Machine learning (ML) can highlight potentially “missing pieces” linking core ATG genes with understudied, “dark” genes by mining functional genomic data. Here, a set of 103 (out of 288 genes from the Autophagy Database) was used as training set, based on ATG-associated terms annotated from 3 secondary sources: GO (gene ontology), Kyoto Encyclopedia of Genes and Genomes pathway, and UniProt keywords, as additional confirmation of their importance in ATG. As negative labels, an OMIM list of genes associated with monogenic diseases was used (after excluding the 288 ATG-associated genes). Data related to these genes from 17 different sources were compiled and used to derive a trained MetaPath/XGBoost (MPxgb) ML model for distinguishing ATG and non-ATG genes (10-fold cross-validated, 100-times randomized models, median area under the curve = 0.994 ± 0.008). Sixteen ATG-relevant variables explained 64% of the total model gain. Overall, 23% of the top 251 predicted genes are annotated in the Autophagy Database, whereas 193 genes (77%) are not. In 2019, we suggested that some of these 193 genes may represent “ATG dark genes.” A literature search in 2022 for those top 20 predicted ATG dark genes found that 9 were subsequently reported as ATG genes during the intervening 3.5 years. A post-factum evaluation of data leakage (the presence of ATG-associated terms in the top 40 ML features) confirms that 7 out of these 9 genes and 2 out of 3 other recently validated predictions from the bottom 20 are novel. Those genes with the largest number of ATG features would be most likely to yield valuable experimental insights. Modern high-throughput testing would be capable of spanning the full 193 ATG genes list reported here. Our analysis demonstrates that ML can guide genomics research to gain a more complete functional and pathway annotation of complex processes. Key points – A knowledge-graph based machine learning model was designed for predicting unknown autophagy genes via mining functional genomic data. – Literature search validated predicted genes. – Our machine learning models could be generalized and applied to other genomic libraries to uncover dark genes for various functions.
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In the context of the ATG-based ML model, the inclusion of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms can introduce information directly related to the research question.
“In the context of the ATG-based ML model, the inclusion of gene ontology (GO) (64) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (75) terms can introduce information directly related to the research question.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [64]
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Flagged first, then by citations
Research works printed but not linked: 1
- National Library of Medicine (US) NCfBI. Homology-Site Guide. National Library of Medicine (US), National Center for Biotechnology Information. July 18, 2019. Accessed July 18th, 2019. https://www. ncbi.nlm.nih.gov/guide/homology/
Cited by
Sorted by citations
Artificial Intelligence for Drug Discovery: Are We There Yet?
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Self-citation
- Authors
- Ranjbar, M., Yang, J. J., Kumar, P., Byrd, D. R., Bearer, E. L., & Oprea, T. I.
- Affiliations
- University of New Mexico (US)
- Corresponding author
- Oprea, Tudor I.
- Venue
- Natural Sciences
- Publisher
- Wiley
- Published
- 2023-05
- Volume
- 3
- Issue
- 3
- Pages
- e20220067-e20220067
- ISSN
- 2698-6248 (ISSN-L)
- OpenAlex
- W4367626561
- Cited by
- 7
- References
- 132
- Citations by year
- 2026: 4 · 2024: 2 · 2023: 1
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Natural Sciences (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (U24CA224370, U24TR002278, UL1TR001449, P20GM121176, P20AG068077, R01 MD014153)
- Topics
- Health Sciences › Medicine › Epidemiology › Autophagy in Disease and Therapy
- 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
- Keywords
- autophagy, dark genes, gene function prediction, knowledge graphs, XGBoost, meta-paths, functional genomic annotation, Gene Ontology, data leakage
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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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