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Adv Pharm Bull. 2020;10(1): 97-105.
doi: 10.15171/apb.2020.012
PMID: 32002367
PMCID: PMC6983983
Scopus ID: 85084144769
  Abstract View: 427
  PDF Download: 298

Research Article

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

Faegheh Golabi 1,2 ORCID logo, Mousa Shamsi 1* ORCID logo, Mohammad Hosein Sedaaghi 3 ORCID logo, Abolfazl Barzegar 2,4 ORCID logo, Mohammad Saeid Hejazi 5,6* ORCID logo

1 Genomic Signal Processing Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
2 School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
3 Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
4 Research Institute for Fundamental Sciences (RIFS), University of Tabriz, Tabriz, Iran.
5 Molecular Medicine Research Center, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
6 Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.

Abstract

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.

Keywords: Riboswitch, Non-coding RNA, Sequential blocks, Block location-based feature extraction, BLBFE, Classification, Performance measures
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Submitted: 25 May 2019
Revision: 04 Sep 2019
Accepted: 30 Sep 2019
ePublished: 11 Dec 2019
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