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- W4327704282 endingPage "2261" @default.
- W4327704282 startingPage "2253" @default.
- W4327704282 abstract "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." @default.
- W4327704282 created "2023-03-18" @default.
- W4327704282 creator A5026505260 @default.
- W4327704282 creator A5030046423 @default.
- W4327704282 creator A5041539298 @default.
- W4327704282 creator A5047920804 @default.
- W4327704282 creator A5061563279 @default.
- W4327704282 creator A5073409235 @default.
- W4327704282 creator A5082012882 @default.
- W4327704282 date "2023-01-01" @default.
- W4327704282 modified "2023-09-29" @default.
- W4327704282 title "Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods" @default.
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