Classification of Riboswitch Families Using Block Location-Based Feature Extraction (BLBFE) Method
| Autor: | Mousa Shamsi, Mohammad Saeid Hejazi, Faegheh Golabi, Mohammad H. Sedaaghi, Abolfazl Barzegar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Riboswitch
sequential blocks Computer science riboswitch Feature extraction Decision tree Pharmaceutical Science 010402 general chemistry 01 natural sciences 03 medical and health sciences Classification rate Probabilistic neural network Sensitivity (control systems) General Pharmacology Toxicology and Pharmaceutics 030304 developmental biology Block (data storage) 0303 health sciences business.industry non-coding rna lcsh:RM1-950 Pattern recognition blbfe performance measures Linear discriminant analysis 0104 chemical sciences lcsh:Therapeutics. Pharmacology classification block location-based feature extraction Artificial intelligence business Research Article |
| Zdroj: | Advanced Pharmaceutical Bulletin, Vol 10, Iss 1, Pp 97-105 (2020) Advanced Pharmaceutical Bulletin |
| ISSN: | 2251-7308 2228-5881 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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