چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | فائقه گلابی |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | دانشکده علوم نوین پزشکی |
| کد مقاله | 78647 |
| عنوان فارسی مقاله | طراحی یک روش استخراج ویژگی مبتنی بر موقعیت مکانی بلوک ها (BLBFE) برای طبقه بندی رایبوسویچ ها |
| عنوان لاتین مقاله | Development of a new oligonucleotide block location-based feature extraction (BLBFE) method for the classification of riboswitches |
| نوع ارائه | سخنرانی |
| عنوان کنگره / همایش | دهمین همایش ملی و اولین همایش بین المللی بیوانفورماتیک ایران |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | Iran (Islamic Republic) |
| شهر محل برگزاری کنگره/ همایش | جزیره کیش |
| سال انتشار/ ارائه شمسی | 1400 |
| سال انتشار/ارائه میلادی | 2022 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1400/12/03 الی 1400/12/05 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | https://icb10.ut.ac.ir |
| آدرس علمی (Affiliation) نویسنده متقاضی | Department of Biomedical Engineering, Faculty of Advanced Biomedical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran |
| نویسنده | نفر چندم مقاله |
|---|---|
| محمد سعید حجازی | پنجم |
| ابوالفضل برزگر | چهارم |
| فائقه گلابی | اول |
| عنوان | متن |
|---|---|
| خلاصه مقاله | As knowledge of genetics and genome elements increases, the demand for the development of bioinformatics tools for analyzing these data are raised. Riboswitches are genetic components, usually located in the untranslated regions of mRNAs, that regulate gene expression [1-16]. Additionally, their interaction with antibiotics has been recently suggested, implying a role in antibiotic effects and resistance [9, 10, 17-21]. Following a previously published sequential block finding algorithm [22], herein, we report the development of a new block location-based feature extraction strategy (BLBFE). This procedure utilizes the locations of family-specific sequential blocks on riboswitch sequences as features. Furthermore, the performance of other feature extraction strategies, including mono- and dinucleotide frequencies [23], k-mer [24], DAC, DCC, DACC [25-27], PC-PseDNC-General and SC-PseDNC-General [27, 28] methods, was investigated [29, 30]. KNN [31], LDA [32], naïve Bayes [33-35], PNN [36] and decision tree [37] classifiers accompanied by V-fold cross-validation [38] were applied for all methods of feature extraction, and their performances based on the defined feature extraction strategies were compared. Performance measures of accuracy, sensitivity, specificity and F-score for each method of feature extraction were studied [39, 40]. The proposed feature extraction strategy resulted in a classification of riboswitches with an average correct classification rate (CCR) of 90.8%. Furthermore, the obtained data confirmed the performance of the developed feature extraction method with an average accuracy of 96.1%, an average sensitivity of 90.8%, an average specificity of 97.52% and an average F-score of 90.69%. Our results implied that the proposed feature extraction (BLBFE) method can classify and discriminate riboswitch families with high CCR, accuracy, sensitivity, specificity and F-score values |
| کلمات کلیدی | : Riboswitches; feature extraction; sequential blocks; block location-based feature extraction; classification; performance measures |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| ICB10-Booklet.pdf | 1401/03/22 | 10823535 | دانلود |
| Manuscript for bioinf conf.pdf | 1401/02/19 | 153291 | دانلود |
| ICB10 - Oral Certificate.pdf | 1401/02/19 | 425072 | دانلود |
