Format

Send to

Choose Destination
See comment in PubMed Commons below
PLoS One. 2012;7(9):e45589. doi: 10.1371/journal.pone.0045589. Epub 2012 Sep 27.

Achieving high accuracy prediction of minimotifs.

Author information

1
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.

Abstract

The low complexity of minimotif patterns results in a high false-positive prediction rate, hampering protein function prediction. A multi-filter algorithm, trained and tested on a linear regression model, support vector machine model, and neural network model, using a large dataset of verified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives. An optimal threshold for the best accuracy reaches an overall accuracy above 90%, while a stringent threshold for the best specificity generates less than 1% false positives or even no false positives and still produces more than 90% true positives for the linear regression and neural network models. The minimotif multi-filter with its excellent accuracy represents the state-of-the-art in minimotif prediction and is expected to be very useful to biologists investigating protein function and how missense mutations cause disease.

PMID:
23029121
PMCID:
PMC3459956
DOI:
10.1371/journal.pone.0045589
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Public Library of Science Icon for PubMed Central
    Loading ...
    Support Center