التفاصيل البيبلوغرافية
| العنوان: |
Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods |
| المؤلفون: |
Hasan Zulfiqar, Zhiling Guo, Bakanina Kissanga Grace-Mercure, Zhao-Yue Zhang, Hui Gao, Hao Lin, Yun Wu |
| المصدر: |
Computational and Structural Biotechnology Journal, Vol 21, Iss , Pp 2253-2261 (2023) |
| بيانات النشر: |
Elsevier, 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Biotechnology |
| مصطلحات موضوعية: |
Hormone binding proteins, Feature extraction, Feature selection, Machine learning, Computational tools, Biotechnology, TP248.13-248.65 |
| الوصف: |
Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These proteins selectively and non-covalently interact with hormones and promote growth hormone signaling in human and other animals. The HBPs are useful in many medical and commercial fields. Thus, the identification of HBPs is very important because it can help to discover more details about hormone binding proteins. Meanwhile, the experimental methods are time-consuming and expensive for hormone binding proteins recognition. Computational prediction methods have played significant roles in the correct recognition of hormone binding proteins with the use of sequence information and ML algorithms. In this review, we compared and assessed the implementation of ML-based tools in recognition of HBPs in a unique way. We hope that this study will give enough awareness and knowledge for research on HBPs. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2001-0370 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2001037023001253; https://doaj.org/toc/2001-0370 |
| DOI: |
10.1016/j.csbj.2023.03.024 |
| URL الوصول: |
https://doaj.org/article/15a95eb25e7046a7abe782513d043a4f |
| رقم الانضمام: |
edsdoj.15a95eb25e7046a7abe782513d043a4f |
| قاعدة البيانات: |
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