ArticleGenes2020
A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis.
Article in Genes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed, 30 citations in OpenAlex.
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Antlers on does: An unexpected role of macrophages in deer biology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration.Heliyon · 2024Article
- Identification of proteins' expression pathway and the effective miRNAs for the treatment of human papillomavirus-induced cervical cancer: in-silico analyses-experimental research.Annals of medicine and surgery (2012) · 2024Article
- Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning.Molecular medicine (Cambridge, Mass.) · 2023Article
- Single-cell transcriptome reveals core cell populations and androgen-RXFP2 axis involved in deer antler full regeneration.Cell regeneration (London, England) · 2022Article
- Emerging insights into the complex genetics and pathophysiology of amyotrophic lateral sclerosis.The Lancet. Neurology · 2022Review
- Advances in the genetic classification of amyotrophic lateral sclerosis.Current opinion in neurology · 2021Review
- DGLinker: flexible knowledge-graph prediction of disease-gene associations.Nucleic acids research · 2021Article
- Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer.Frontiers in oncology · 2021Article
- Moving Toward Patient-Tailored Treatment in ALS and FTD: The Potential of Genomic Assessment as a Tool for Biological Discovery and Trial Recruitment.Frontiers in neuroscience · 2021Review
- What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis?Journal of personalized medicine · 2020Review
- From Multi-Omics Approaches to Precision Medicine in Amyotrophic Lateral Sclerosis.Frontiers in neuroscience · 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
4 authors at 2 institutions in 1 country.
Funding
Abstract
Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants have been associated with ALS and are responsible for about 15% of all cases. Although our knowledge of the genetic landscape of this disease is improving, it remains limited. Machine learning models trained on the available protein-protein interaction and phenotype-genotype association data can use our current knowledge of the disease genetics for the prediction of novel candidate genes. Here, we describe a knowledge-based machine learning method for this purpose. We trained our model on protein-protein interaction data from IntAct, gene function annotation from Gene Ontology, and known disease-gene associations from DisGeNet. Using several sets of known ALS genes from public databases and a manual review as input, we generated a list of new candidate genes for each input set. We investigated the relevance of the predicted genes in ALS by using the available summary statistics from the largest ALS genome-wide association study and by performing functional and phenotype enrichment analysis. The predicted sets were enriched for genes associated with other neurodegenerative diseases known to overlap with ALS genetically and phenotypically, as well as for biological processes associated with the disease. Moreover, using ALS genes from ClinVar and our manual review as input, the predicted sets were enriched for ALS-associated genes (ClinVar
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What Socratic 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 Socratic graph.