ArticleHuman mutation2017
Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.
Article in Human mutation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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Who cites it
19 citing papers in PubMed.
- Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler.Current genomics · 2024Article
- Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations.Research (Washington, D.C.) · 2023Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- An oncogenic splice variant of PDGFRα in adult glioblastoma as a therapeutic target for selective CDK4/6 inhibitors.Scientific reports · 2022Article
- A method to delineate de novo missense variants across pathways prioritizes genes linked to autism.Science translational medicine · 2021Article
- Article
- Technologies and Computational Analysis Strategies for CRISPR Applications.Molecular cell · 2020Review
- TraPS-VarI: Identifying genetic variants altering phosphotyrosine based signalling motifs.Scientific reports · 2020Article
- CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation.Human mutation · 2019Article
- Assessment of predicted enzymatic activity of α-N-acetylglucosaminidase variants of unknown significance for CAGI 2016.Human mutation · 2019Article
- Solution structure and functional investigation of human guanylate kinase reveals allosteric networking and a crucial role for the enzyme in cancer.The Journal of biological chemistry · 2019Article
- An optimized prediction framework to assess the functional impact of pharmacogenetic variants.The pharmacogenomics journal · 2019Article
- Computational analysis of functional SNPs in Alzheimer's disease-associated endocytosis genes.PeerJ · 2019Article
- Computational Assessment of the Pharmacological Profiles of Degradation Products of Chitosan.Frontiers in bioengineering and biotechnology · 2019Article
- Prediction and interpretation of deleterious coding variants in terms of protein structural stability.Scientific reports · 2018Article
- Pharmacogenomics of GPCR Drug Targets.Cell · 2018Article
- Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data.Frontiers in pharmacology · 2018Review
- Objective assessment of the evolutionary action equation for the fitness effect of missense mutations across CAGI-blinded contests.Human mutation · 2017Review
- Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.Human mutation · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship.
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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.