التفاصيل البيبلوغرافية
| العنوان: |
Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique |
| المؤلفون: |
Hasan Zulfiqar, Qin-Lai Huang, Hao Lv, Zi-Jie Sun, Fu-Ying Dao, Hao Lin |
| المصدر: |
International Journal of Molecular Sciences; Volume 23; Issue 3; Pages: 1251 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2022 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
deep learning, alteration, features vector, genomics, algorithm |
| جغرافية الموضوع: |
agris |
| الوصف: |
4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in Geobacter pickeringii. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Molecular Informatics; https://dx.doi.org/10.3390/ijms23031251 |
| DOI: |
10.3390/ijms23031251 |
| الاتاحة: |
https://doi.org/10.3390/ijms23031251 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.747A2BF3 |
| قاعدة البيانات: |
BASE |