ArticleMolecular systems biology2020
KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens.
Article in Molecular systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Decoding spatial mTOR signalling reveals distinct roles in the tumour microenvironment.NPJ precision oncology · 2026Article
- What will be the future of computational biology for macromolecules in the era of AI?PLoS computational biology · 2026Article
- Leveraging Foundation Models for the Characterization of Small-RNA Properties.Computational and structural biotechnology journal · 2026Article
- A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs.Genomics, proteomics & bioinformatics · 2025Article
- A global genetic interaction network by single-cell imaging and machine learning.Cell systems · 2023Article
- LiveCellMiner: A new tool to analyze mitotic progression.PloS one · 2022Article
- Mapping the multiscale structure of biological systems.Cell systems · 2021Review
- KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens.Molecular systems biology · 2020Article
- DeepScratch: Single-cell based topological metrics of scratch wound assays.Computational and structural biotechnology journal · 2020Article
- Automation, Monitoring, and Standardization of Cell Product Manufacturing.Frontiers in bioengineering and biotechnology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.