Prediction of DNA sequences using adaptative neuro-fuzzy inference system

作     者：Assia Mihi Nourredine Boucenna Kheir Benmahammmed 

作者机构：Faculty of Engineering Mohammed Kheider University Avenue Sidi Okba Biskra Algeria Department of Electronics Faculty of Engineering Mohamed El Bachir El Ibrahimi University Bordj Bou Arrdridj El Annasser Algeria Department of Electronics Faculty of EngineeringFerhat Abesse University El maabouda Setif Algeria 

出 版 物：《International Journal of Biomathematics》 (生物数学学报（英文版）)

年 卷 期：2018年第11卷第4期

页      面：19-56页

核心收录：

学科分类：12[管理学] 071010[理学-生物化学与分子生物学] 081704[工学-应用化学] 07[理学] 08[工学] 0710[理学-生物学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0817[工学-化学工程与技术] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：DNA sequence adaptative neuro-fuzzy inference system(ANFIS) fuzzy logic wavelet transform genomic signal. 

摘      要：Accurate prediction and detection of the DNA regions or their underlying structural patterns are constant difficulties for researchers. Feature extraction and functional classification of genomic sequences is an interesting area of research. Many computational techniques have already been applied including the artificial neural network （ANN）, nonlinear model, spectrogram and statistical techniques. In this paper, some features are extracted from the wavelet coefficient and second set of features are extracted from the frequency of transition of nucleotides. These two features sets are examined. The purpose was to investigate the abilities of these parameters to predict critical segment in the DNA sequence. The neuro-fuzzy system was used for prediction. The performance of the neuro-fuzzy system was evaluated in terms of training performance and prediction accuracies. Two genomic sequences of the classes： prokaryotic and eukaryotic were used, as an example, （Escherichia coli） and （Caenorhabditis elegans） sequences were selected.