PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F14%3A00061012" target="_blank" >RIV/00159816:_____/14:00061012 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1371/journal.pcbi.1003440" target="_blank" >http://dx.doi.org/10.1371/journal.pcbi.1003440</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pcbi.1003440" target="_blank" >10.1371/journal.pcbi.1003440</a>

Result language

angličtina

Original language name

PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

Original language description

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations onprotein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and furtherimprovement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, P

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

EB - Genetics and molecular biology

OECD FORD branch

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Project

<a href="/en/project/ED1.100%2F02%2F0123" target="_blank" >ED1.100/02/0123: St. Anne´s University Hospital Brno - International Clinical Research Center (FNUSA-ICRC)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2014

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PLoS Computational Biology

ISSN

1553-7358

e-ISSN

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Volume of the periodical

10

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

"e1003440"

UT code for WoS article

000337948500040

EID of the result in the Scopus database

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