Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons
Abstract The detection of exons is an important area of research in genomic sequence analysis. Many signal-processing methods have been established successfully for detecting the exons based on their periodicity property. However, some improvement is still required to increase the identification acc...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-86672-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571806218715136 |
|---|---|
| author | K. Jayasree Malaya Kumar Hota |
| author_facet | K. Jayasree Malaya Kumar Hota |
| author_sort | K. Jayasree |
| collection | DOAJ |
| description | Abstract The detection of exons is an important area of research in genomic sequence analysis. Many signal-processing methods have been established successfully for detecting the exons based on their periodicity property. However, some improvement is still required to increase the identification accuracy of exons. So, an efficient computational model is needed. Therefore, for the first time, we are introducing an optimized convolutional neural network (optCNN) for classifying the exons and introns. The study aims to identify the best CNN model that provides improved accuracy for the classification of exons by utilizing the optimization algorithm. In this case, an African Vulture Optimization Algorithm (AVOA) is used for optimizing the layered architecture of the CNN model along with its hyperparameters. The CNN model generated with AVOA yielded a success rate of 97.95% for the GENSCAN training set and 95.39% for the HMR195 dataset. The proposed approach is compared with the state-of-the-art methods using AUC, F1-score, Recall, and Precision. The results reveal that the proposed model is reliable and denotes an inventive method due to the ability to automatically create the CNN model for the classification of exons and introns. |
| format | Article |
| id | doaj-art-997fab15dd2c4c41b2904cfb88c48f35 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-997fab15dd2c4c41b2904cfb88c48f352025-02-02T12:19:08ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-025-86672-xOptimized convolutional neural network using African vulture optimization algorithm for the detection of exonsK. Jayasree0Malaya Kumar Hota1Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of TechnologyDepartment of Communication Engineering, School of Electronics Engineering, Vellore Institute of TechnologyAbstract The detection of exons is an important area of research in genomic sequence analysis. Many signal-processing methods have been established successfully for detecting the exons based on their periodicity property. However, some improvement is still required to increase the identification accuracy of exons. So, an efficient computational model is needed. Therefore, for the first time, we are introducing an optimized convolutional neural network (optCNN) for classifying the exons and introns. The study aims to identify the best CNN model that provides improved accuracy for the classification of exons by utilizing the optimization algorithm. In this case, an African Vulture Optimization Algorithm (AVOA) is used for optimizing the layered architecture of the CNN model along with its hyperparameters. The CNN model generated with AVOA yielded a success rate of 97.95% for the GENSCAN training set and 95.39% for the HMR195 dataset. The proposed approach is compared with the state-of-the-art methods using AUC, F1-score, Recall, and Precision. The results reveal that the proposed model is reliable and denotes an inventive method due to the ability to automatically create the CNN model for the classification of exons and introns.https://doi.org/10.1038/s41598-025-86672-xConvolutional neural network (CNN)ExonsModified Gabor wavelet transform (MGWT)Three base periodicity properties (TBP)African vulture optimization algorithm (AVOA) |
| spellingShingle | K. Jayasree Malaya Kumar Hota Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons Scientific Reports Convolutional neural network (CNN) Exons Modified Gabor wavelet transform (MGWT) Three base periodicity properties (TBP) African vulture optimization algorithm (AVOA) |
| title | Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons |
| title_full | Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons |
| title_fullStr | Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons |
| title_full_unstemmed | Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons |
| title_short | Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons |
| title_sort | optimized convolutional neural network using african vulture optimization algorithm for the detection of exons |
| topic | Convolutional neural network (CNN) Exons Modified Gabor wavelet transform (MGWT) Three base periodicity properties (TBP) African vulture optimization algorithm (AVOA) |
| url | https://doi.org/10.1038/s41598-025-86672-x |
| work_keys_str_mv | AT kjayasree optimizedconvolutionalneuralnetworkusingafricanvultureoptimizationalgorithmforthedetectionofexons AT malayakumarhota optimizedconvolutionalneuralnetworkusingafricanvultureoptimizationalgorithmforthedetectionofexons |