KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens
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Abstract
Characterising context-dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large-scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge- and Context-driven Machine Learning (KCML), a framework that systematically predicts multiple context-specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale-crossing and context-dependent gene functions. KCML is highly generalisable and applicable to various large-scale genetic perturbation screens.
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The KCML study found examples where genes annotated to the same higher-scale process perform different functions at lower scales, reflecting the hierarchical organization of cellular subsystems.
“The study found examples where genes annotated to the same higher-scale process perform different functions at lower scales, reflecting the hierarchical organization of cellular subsystems.”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Sailem et al., 2020
In KCML, a support vector machine classifier for each GO biological process learned to distinguish between phenotypic profiles of genes annotated to that process versus random subsets of other genes.
“In this approach, a support vector machine (SVM) classifier for each GO biological process learned to distinguish between phenotypic profiles of genes annotated to that process versus random subsets of other genes.”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Sailem et al., 2020
KCML is a strategy for using a hierarchical approach to predict gene functions from high-throughput gene perturbation screening.
“Finally, Knowledge- and Context-driven Machine Learning (KCML) is a recently developed strategy for using a hierarchical approach to predict gene functions from high-throughput gene perturbation screening (Sailem et al., 2020).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Sailem et al., 2020
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- Authors
- Sailem, H. Z., Rittscher, J., & Pelkmans, L.
- Affiliations
- University of Oxford (GB)
- University of Zurich (CH)
- Corresponding author
- Sailem, Heba Z
- Venue
- Molecular Systems Biology
- Published
- 2020-03-01
- Volume
- 16
- Issue
- 3
- Pages
- e9083-e9083
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC7059140
- OpenAlex
- W3010525763
- Cited by
- 16
- References
- 74
- Citations by year
- 2026: 4 · 2025: 1 · 2023: 2 · 2022: 1 · 2021: 3 · 2020: 5
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wellcome Trust (204724/Z/16/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- gene function prediction, olfactory receptors, colorectal cancer, TGF-beta signaling, Wnt signaling, clinical outcome prediction
- MeSH terms
- Humans, Phenotype, Prognosis, Colorectal Neoplasms, Signal Transduction, Gene Expression Regulation, Neoplastic, Transforming Growth Factor beta, Receptors, Odorant, HCT116 Cells, Cell Line, Tumor, Systems Biology, Gene Regulatory Networks, Support Vector Machine, Wnt Signaling Pathway, Neoplasm Grading, MCF-7 Cells
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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