ArticleFrontiers in plant science2018
A Pipeline for Classifying Deleterious Coding Mutations in Agricultural Plants.
Article in Frontiers in plant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- Arabidopsis as a model for translational research.The Plant cell · 2025Review
- A foundational large language model for edible plant genomes.Communications biology · 2024Article
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
- PPVED: A machine learning tool for predicting the effect of single amino acid substitution on protein function in plants.Plant biotechnology journal · 2022Article
- PON-All: Amino Acid Substitution Tolerance Predictor for All Organisms.Frontiers in molecular biosciences · 2022Article
- PlaPPISite: a comprehensive resource for plant protein-protein interaction sites.BMC plant biology · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The impact of deleterious variation on both plant fitness and crop productivity is not completely understood and is a hot topic of debates. The deleterious mutations in plants have been solely predicted using sequence conservation methods rather than function-based classifiers due to lack of well-annotated mutational datasets in these organisms. Here, we developed a machine learning classifier based on a dataset of deleterious and neutral mutations in
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Registered trials
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