Title |
Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach
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Published in |
Journal of Structural and Functional Genomics, December 2011
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DOI | 10.1007/s10969-011-9120-4 |
Pubmed ID | |
Authors |
Majid Mohammad Beigi, Mohaddeseh Behjati, Hassan Mohabatkar |
Abstract |
Matrix metalloproteinase (MMPs) and disintegrin and metalloprotease (ADAMs) belong to the zinc-dependent metalloproteinase family of proteins. These proteins participate in various physiological and pathological states. Thus, prediction of these proteins using amino acid sequence would be helpful. We have developed a method to predict these proteins based on the features derived from Chou's pseudo amino acid composition (PseAAC) server and support vector machine (SVM) as a powerful machine learning approach. With this method, for ADAMs and MMPs families, an overall accuracy and Matthew's correlation coefficient (MCC) of 95.89 and 0.90% were achieved respectively. Furthermore, the method is able to predict two major subclasses of MMP family; Furin-activated secreted MMPs and Type II trans-membrane; with MCC of 0.89 and 0.91%, respectively. The overall accuracy for Furin-activated secreted MMPs and Type II trans-membrane was 98.18 and 99.07, respectively. Our data demonstrates an effective classification of Metalloproteinase family based on the concept of PseAAC and SVM. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Russia | 1 | 5% |
Unknown | 21 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 6 | 27% |
Lecturer > Senior Lecturer | 2 | 9% |
Student > Bachelor | 2 | 9% |
Student > Master | 2 | 9% |
Researcher | 1 | 5% |
Other | 1 | 5% |
Unknown | 8 | 36% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 7 | 32% |
Computer Science | 2 | 9% |
Biochemistry, Genetics and Molecular Biology | 1 | 5% |
Medicine and Dentistry | 1 | 5% |
Chemistry | 1 | 5% |
Other | 2 | 9% |
Unknown | 8 | 36% |