A hybrid deep learning model for classification of plant transcription factor proteins
Yazarlar (2)
Dr. Öğr. Üyesi Ali Burak ÖNCÜL Kastamonu Üniversitesi, Türkiye
Doç. Dr. Yüksel Çelik Karabük Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Signal Image and Video Processing (Q3) | ||
| Dergi ISSN | 1863-1703 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 12-2022 |
| Kabul Tarihi | 21-11-2022 | Yayınlanma Tarihi | 01-12-2022 |
| Cilt / Sayı / Sayfa | 17 / 5 / 2055–2061 | DOI | 10.1007/s11760-022-02419-5 |
| Makale Linki | http://dx.doi.org/10.1007/s11760-022-02419-5 | ||
| Özet |
| Studies on the amino acid sequences, protein structure, and the relationships of amino acids are still a large and challenging problem in biology. Although bioinformatics studies have progressed in solving these problems, the relationship between amino acids and determining the type of protein formed by amino acids are still a problem that has not been fully solved. This problem is why the use of some of the available protein sequences is also limited. This study proposes a hybrid deep learning model to classify amino acid sequences of unknown species using the amino acid sequences in the plant transcription factor database. The model achieved 98.23% success rate in the tests performed. With the hybrid model created, transcription factor proteins in the plant kingdom can be easily classified. The fact that the model is hybrid has made its layers lighter. The training period has decreased, and the success has … |
| Anahtar Kelimeler |
| CNN | Deep learning | GRU | Hybrid models | Protein classification | Word2Vec |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 4 |
| Scopus | 9 |
| Google Scholar | 13 |