Document Type
Article
Disciplines
Life Sciences
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.
Recommended Citation
Mi, Tian; Rajasekaran, Sanguthevar; Merlin, Jerlin C.; and Gryk, Michael R., "Achieving High Accuracy Prediction of Minimotifs" (2012). UCHC Articles - Research. 129.
https://digitalcommons.lib.uconn.edu/uchcres_articles/129
Comments
originally published in :
PLoS One. 2012; 7(9): e45589. Published online 2012 September 27. doi: 10.1371/journal.pone.0045589 PMCID: PMC3459956 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.