ArticleEvolutionary applications2019
Prediction of deleterious mutations in coding regions of mammals with transfer learning.
Article in Evolutionary applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 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.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Measuring the change in deleterious mutation burden of barley germplasm conserved over one regeneration cycle.G3 (Bethesda, Md.) · 2026Article
- Accumulating Heterozygous Deleterious Mutations in Conserved Soybean Germplasm over Successive Regenerations.Plants (Basel, Switzerland) · 2025Article
- Elevated Mutation Burdens in Canadian Oat and Wheat Cultivars Released over the Past Century.Cells · 2025Article
- Sodium azide mutagenesis induces a unique pattern of mutations.PLoS genetics · 2025Article
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
- Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness.Frontiers in genetics · 2022Article
- PON-All: Amino Acid Substitution Tolerance Predictor for All Organisms.Frontiers in molecular biosciences · 2022Article
- Review
- Corrigendum.Evolutionary applications · 2019Article
- The genomics of domestication special issue editorial.Evolutionary applications · 2019Article
- Prediction of deleterious mutations in coding regions of mammals with transfer learning.Evolutionary applications · 2019Article
Corrections and comments
- Erratum issuedCorrigendum.2019
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The genomes of mammals contain thousands of deleterious mutations. It is important to be able to recognize them with high precision. In conservation biology, the small size of fragmented populations results in accumulation of damaging variants. Preserving animals with less damaged genomes could optimize conservation efforts. In breeding of farm animals, trade-offs between farm performance versus general fitness might be better avoided if deleterious mutations are well classified. In humans, the problem of such a precise classification has been successfully solved, in large part due to large databases of disease-causing mutations. However, this kind of information is very limited for other mammals. Here, we propose to better use information available on human mutations to enable classification of damaging mutations in other mammalian species. Specifically, we apply transfer learning-machine learning methods-improving small dataset for solving a focal problem (recognizing damaging mutations in our companion and farm animals) due to the use of much large datasets available for solving a related problem (recognizing damaging mutations in humans). We validate our tools using mouse and dog annotated datasets and obtain significantly better results in companion to the SIFT classifier. Then, we apply them to predict deleterious mutations in cattle genomewide dataset.
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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.