Achieving High Accuracy Prediction of Minimotifs

Tian Mi, Sanguthevar Rajasekaran, Jerlin Camilus Merlin, Michael Gryk and Martin R Schiller

PLOS ONE, 2012, vol. 7, issue 9, 1-7

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.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0045589

DOI: 10.1371/journal.pone.0045589

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