Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP. / Johansen, Morten Bo; Izarzugaza, Jose M G; Brunak, Søren; Petersen, Thomas Nordahl; Gupta, Ramneek.

In: P L o S One, Vol. 8, No. 7, 07.2013, p. e68370.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Johansen, MB, Izarzugaza, JMG, Brunak, S, Petersen, TN & Gupta, R 2013, 'Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP', P L o S One, vol. 8, no. 7, pp. e68370. https://doi.org/10.1371/journal.pone.0068370

APA

Johansen, M. B., Izarzugaza, J. M. G., Brunak, S., Petersen, T. N., & Gupta, R. (2013). Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP. P L o S One, 8(7), e68370. https://doi.org/10.1371/journal.pone.0068370

Vancouver

Johansen MB, Izarzugaza JMG, Brunak S, Petersen TN, Gupta R. Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP. P L o S One. 2013 Jul;8(7):e68370. https://doi.org/10.1371/journal.pone.0068370

Author

Johansen, Morten Bo ; Izarzugaza, Jose M G ; Brunak, Søren ; Petersen, Thomas Nordahl ; Gupta, Ramneek. / Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP. In: P L o S One. 2013 ; Vol. 8, No. 7. pp. e68370.

Bibtex

@article{28cbc2b0a7d14fbab0cbea90567d1da8,
title = "Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP",
abstract = "We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: https://www.cbs.dtu.dk/services/NetDiseaseSNP.",
author = "Johansen, {Morten Bo} and Izarzugaza, {Jose M G} and S{\o}ren Brunak and Petersen, {Thomas Nordahl} and Ramneek Gupta",
year = "2013",
month = jul,
doi = "10.1371/journal.pone.0068370",
language = "English",
volume = "8",
pages = "e68370",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "7",

}

RIS

TY - JOUR

T1 - Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

AU - Johansen, Morten Bo

AU - Izarzugaza, Jose M G

AU - Brunak, Søren

AU - Petersen, Thomas Nordahl

AU - Gupta, Ramneek

PY - 2013/7

Y1 - 2013/7

N2 - We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: https://www.cbs.dtu.dk/services/NetDiseaseSNP.

AB - We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: https://www.cbs.dtu.dk/services/NetDiseaseSNP.

U2 - 10.1371/journal.pone.0068370

DO - 10.1371/journal.pone.0068370

M3 - Journal article

C2 - 23935863

VL - 8

SP - e68370

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 7

ER -

ID: 58426758