| Issue |
BIO Web Conf.
Volume 75, 2023
The 5th International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering (BioMIC 2023)
|
|
|---|---|---|
| Article Number | 01008 | |
| Number of page(s) | 6 | |
| Section | Bioinformatics and Data Mining | |
| DOI | https://doi.org/10.1051/bioconf/20237501008 | |
| Published online | 15 November 2023 | |
N-Grams Modeling for Protein Secondary Structure Prediction: Exploring Local Features and Optimal CNN Parameters
1 Master Program of Computer Science, Department of Computer Science and Electronics, Universitas Gadjah Mada, Indonesia
2 Department of Computer Science and Electronics, Universitas Gadjah Mada, Indonesia
* Corresponding author: afia@ugm.ac.id
This study explores the potential of n-gram modeling in protein secondary structure prediction. Experiments are conducted on three datasets using bigrams, trigrams, and a combination of the best n-grams with PSSM profiles. Optimal parameters for Convolutional Neural Networks (CNNs) are investigated. Results indicate that bigrams outperform trigrams in Q8 accuracy. Adding another feature, that is, PSSM, can improve model performance. Deeper convolution layers and longer convolution sizes enhance accuracy. Both bigrams and trigrams demonstrate similar performance trends, with bigrams slightly more effective. The study offers insights into local feature extraction, which is n-grams for protein modeling. These findings contribute to protein structure analysis and bioinformatics advancements, facilitating improved protein function prediction.
Key words: Protein secondary structure prediction / n-grams / sequence labeling / convolutional neural network / bioinformatics
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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