Comparison among Feature Encoding Techniques for HIV 1 Protease Cleavage Specificity
Yazarlar (3)
Balıkesir Üniversitesi, Türkiye
Prof. Dr. Murat GÖK
Yalova Üniversitesi, Türkiye
Balıkesir Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
|
| Makale Alt Türü | Diğer hakemli uluslarası dergilerde yayınlanan tam makale |
| Dergi Adı | International Journal of Intelligent Systems and Applications in Engineering |
| Dergi ISSN | 2147-6799 Scopus Dergi |
| Dergi Tarandığı Indeksler | IndexCopernicus, Google Scholar, GetCITED, ScienceCentral, JournalTocs |
| Makale Dili | İngilizce |
| Basım Tarihi | 04-2015 |
| Cilt No | 3 |
| Sayı | 2 |
| Sayfalar | 62 / 66 |
| Makale Linki | http://ijisae.atscience.org/article/view/1065000149/pdf_16 |
| Özet |
| HIV-1 protease which is responsible for the generation of infectious viral particles by cleaving the virus polypeptides, play an indispensable role in the life cycle of HIV-1. Knowledge of the substrate specificity of HIV-1 protease will pave the way of development of efficacious HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, many efforts have been devoted. Last decade, several works have approached the prediction of HIV-1 protease cleavage site problem by applying a number of methods from the field of machine learning. However, it is still difficult for researchers to choose the best method due to the lack of an effective and up-to-date comparison. Here, we have made an extensive study on feature encoding techniques for the problem of HIV-1 protease specificity on diverse machine learning algorithms. Also, for the first time, we applied OEDICHO technique, which is a combination of orthonormal encoding and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid index database, to predict HIV-1 protease cleavage sites. |
| Anahtar Kelimeler |
