Classification of Riboswitch Families Using Block Location-Based Feature Extraction (BLBFE) Method

Classification of Riboswitch Families Using Block Location-Based Feature Extraction (BLBFE) Method


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دانشگاه علوم پزشکی تبریز
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نویسندگان: فائقه گلابی , ابوالفضل برزگر , محمد سعید حجازی

کلمات کلیدی: Riboswitch, non-coding RNA, sequential blocks, block location-based feature extraction, BLBFE, classification, performance measures.

نشریه: 952 , 1 , 10 , 2020

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نویسنده ثبت کننده مقاله فائقه گلابی
مرحله جاری مقاله تایید نهایی
دانشکده/مرکز مربوطه دانشکده علوم نوین پزشکی
کد مقاله 73050
عنوان فارسی مقاله Classification of Riboswitch Families Using Block Location-Based Feature Extraction (BLBFE) Method
عنوان لاتین مقاله Classification of Riboswitch Families Using Block Location-Based Feature Extraction (BLBFE) Method
ناشر 5
آیا مقاله از طرح تحقیقاتی و یا منتورشیپ استخراج شده است؟ خیر
عنوان نشریه (خارج از لیست فوق)
نوع مقاله Original Article
نحوه ایندکس شدن مقاله ایندکس شده سطح دو – PubMed
آدرس لینک مقاله/ همایش در شبکه اینترنت

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Purpose: Riboswitches are special non-coding sequences usually located in mRNAs’ un-translated regions and regulate gene expression and consequently cellular function. Furthermore, their interaction with antibiotics has been recently implicated. This raises more interest in development of bioinformatics tools for riboswitch studies. Herein, we describe the development and employment of novel block location-based feature extraction (BLBFE) method for classification of riboswitches. Methods: We have already developed and reported a sequential block finding (SBF) algorithm which, without operating alignment methods, identifies family specific sequential blocks for riboswitch families. Herein, we employed this algorithm for 7 riboswitch families including lysine, cobalamin, glycine, SAM-alpha, SAM-IV, cyclic-di-GMP-I and SAH. Then the study was extended toward implementation of BLBFE method for feature extraction. The outcome features were applied in various classifiers including linear discriminant analysis (LDA), probabilistic neural network (PNN), decision tree and k-nearest neighbors (KNN) classifiers for classification of the riboswitch families. The performance of the classifiers was investigated according to performance measures such as correct classification rate (CCR), accuracy, sensitivity, specificity and f-score. Results: As a result, average CCR for classification of riboswitches was 87.87%. Furthermore, application of BLBFE method in 4 classifiers displayed average accuracies of 93.98% to 96.1%, average sensitivities of 76.76% to 83.61%, average specificities of 96.53% to 97.69% and average f-scores of 74.9% to 81.91%. Conclusion: Our results approved that the proposed method of feature extraction; i.e. BLBFE method; can be successfully used for classification and discrimination of the riboswitch families with high CCR, accuracy, sensitivity, specificity and f-score values.

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نویسنده نفر چندم مقاله
فائقه گلابیاول
ابوالفضل برزگرچهارم
محمد سعید حجازیپنجم

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apb-10-97 (1).pdf1399/05/111186858دانلود