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| Content Provider | PubMed Central |
|---|---|
| Author | Tian, Mi Rajasekaran, Sanguthevar Merlin, Jerlin Camilus Michael, Gryk Schiller, Martin R. |
| Editor | Srinivasan, Narayanaswamy |
| Copyright Year | 2012 |
| 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. |
| Related Links | http://dx.doi.org/10.1371/journal.pone.0045589 |
| Starting Page | 45589 |
| File Format | |
| ISSN | 19326203 |
| e-ISSN | 19326203 |
| Journal | PLoS ONE |
| Issue Number | 9 |
| Volume Number | 7 |
| Language | English |
| Publisher | Public Library of Science |
| Publisher Date | 2012-09-01 |
| Access Restriction | Open |
| Rights Holder | Public Library of Science |
| Subject Keyword | Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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