An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM.
| Autor: | Karakaya O; Research and Development Inc., Turkcell Technology, İstanbul, Turkey., Kilimci ZH; Department of Information Systems Engineering, Kocaeli University, Kocaeli, Turkey. |
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| Jazyk: | angličtina |
| Zdroj: | PeerJ. Computer science [PeerJ Comput Sci] 2024 Feb 20; Vol. 10, pp. e1831. Date of Electronic Publication: 2024 Feb 20 (Print Publication: 2024). |
| DOI: | 10.7717/peerj-cs.1831 |
| Abstrakt: | Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe, FastText, One-Hot-Encoding approaches are evaluated as embedding techniques for the purpose of extracting peptide sequences. Then, the output of embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for the Independent dataset, thence determining new state-of-the-art. Competing Interests: Onur Karakaya is employed as a software developer at Turkcell Technology telecommunication. (©2024 Karakaya and Kilimci.) |
| Databáze: | MEDLINE |
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