ReviewJournal of personalized medicine2020
What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis?
Review in Journal of personalized medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Genome-Wide Gene-Set Analysis Identifies Molecular Mechanisms Associated with ALS.International journal of molecular sciences · 2023Pooled it
- AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease.iScience · 2024Article
- Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning.Molecular medicine (Cambridge, Mass.) · 2023Article
- Genome-Wide Gene-Set Analysis Approaches in Amyotrophic Lateral Sclerosis.Journal of personalized medicine · 2022Review
- Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands) · 2022Article
- Understanding Neuromuscular Health and Disease: Advances in Genetics, Omics, and Molecular Function.Journal of personalized medicine · 2021Article
- Amyotrophic Lateral Sclerosis and Frontotemporal Lobar Degenerations: Similarities in Genetic Background.Diagnostics (Basel, Switzerland) · 2021Review
- Bayesian Network as a Decision Tool for Predicting ALS Disease.Brain sciences · 2021Article
- snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data.F1000Research · 2021Article
- Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.
Indexed as
Identifiers
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.