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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F14%3A00077795" target="_blank" >RIV/00216224:14310/14:00077795 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26230/14:PU122817

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

CE - Biochemie

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0037" target="_blank" >EE2.3.30.0037: Zaměstnáním nejlepších mladých vědců k rozvoji mezinárodní spolupráce</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

PLOS Computational Biology

ISSN

1553-734X

e-ISSN

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Svazek periodika

10

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

"nestrankovano"

Kód UT WoS článku

000337948500040

EID výsledku v databázi Scopus

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