A novel protein structural classes' prediction system using fuzzy classification
Publish place: 11th Intelligent Systems Conference
Publish Year: 1391
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICS11_295
تاریخ نمایه سازی: 14 مهر 1392
Abstract:
In this paper we have employed an interpretable FRBCS for classification of protein structural classes, since there is a lack of comprehensible classification systems in this field. In fact the aim of a FRBCS is improvement in the performance of systems and in some studies comprehensibility has also been taken into account. In addition, a fuzzy system which generates a rule base with fewer and shorter general rules provides more comprehensible system. The proposed fuzzy classification method generalizes antecedent fuzzy sets, hence generates interpretable fuzzy rules for biologists to predict protein structural classes. Prediction of protein structural classes is an important problem in protein science, as it provides useful information toward determination of overall protein structure and functions. The resulting classification system achieved high prediction accuracy with jackknife cross-validation test for two benchmark datasets, and the generated rule base is compact and comprehensible
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Authors
F. Meimandi Parizi
School of Electrical and Computer Engineering Shiraz University Shiraz, Iran
E.G. Mansoori
School of Electrical and Computer Engineering Shiraz University Shiraz, Iran