Full bibliography
Comparing Machine Learning Models for Intron Classification
Resource type
Authors/contributors
- Kohnert, C. (Author)
- Yu, N. (Author)
- Rosenberg, P. (Author)
- Torgerson, M. (Author)
- Sobky, A.E. (Author)
- Elkharboutly, R. (Author)
- Seesi, S.A. (Author)
Title
Comparing Machine Learning Models for Intron Classification
Abstract
This work explores using Probabilistic Context Free Grammars and Artificial Neural Networks as possible machine learning models for classifying introns into major and minor introns. It presents an intron classification framework that combines probabilistic context free grammars and support vector machines. It also assesses the computational prediction power of these two models in comparison to the Position Weight Matrices technique, which is currently the exclusively used model for intron classification. The comparison is done through experimental analysis, and it shows promising results for Probabilistic Context Free Grammars and Artificial Neural Networks. © 2022 IEEE.
Conference Name
Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Date
2022
Pages
3047-3054
ISBN
978-1-6654-6819-0
Citation Key
kohnertComparingMachineLearning2022
Archive
Scopus
Language
English
Library Catalog
Scopus
Extra
0 citations (Crossref) [2023-10-31]
Citation
Kohnert, C., Yu, N., Rosenberg, P., Torgerson, M., Sobky, A. E., Elkharboutly, R., & Seesi, S. A. (2022). Comparing Machine Learning Models for Intron Classification. 3047–3054. Scopus. https://doi.org/10.1109/BIBM55620.2022.9995549
Link to this record