Cargando…

Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum

Hormone-binding proteins (HBPs) are specific carrier proteins that bind to a given hormone. A soluble carrier hormone binding protein (HBP), which can interact non-covalently and specifically with growth hormone, modulates or inhibits hormone signaling. HBP is essential for the growth of life, despi...

Descripción completa

Detalles Bibliográficos
Autores principales: Butt, Ahmad Hassan, Alkhalifah, Tamim, Alturise, Fahad, Khan, Yaser Daanial
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252793/
https://www.ncbi.nlm.nih.gov/pubmed/37296792
http://dx.doi.org/10.3390/diagnostics13111940
_version_ 1785056255575851008
author Butt, Ahmad Hassan
Alkhalifah, Tamim
Alturise, Fahad
Khan, Yaser Daanial
author_facet Butt, Ahmad Hassan
Alkhalifah, Tamim
Alturise, Fahad
Khan, Yaser Daanial
author_sort Butt, Ahmad Hassan
collection PubMed
description Hormone-binding proteins (HBPs) are specific carrier proteins that bind to a given hormone. A soluble carrier hormone binding protein (HBP), which can interact non-covalently and specifically with growth hormone, modulates or inhibits hormone signaling. HBP is essential for the growth of life, despite still being poorly understood. Several diseases, according to some data, are caused by HBPs that express themselves abnormally. Accurate identification of these molecules is the first step in investigating the roles of HBPs and understanding their biological mechanisms. For a better understanding of cell development and cellular mechanisms, accurate HBP determination from a given protein sequence is essential. Using traditional biochemical experiments, it is difficult to correctly separate HBPs from an increasing number of proteins because of the high experimental costs and lengthy experiment periods. The abundance of protein sequence data that has been gathered in the post-genomic era necessitates a computational method that is automated and enables quick and accurate identification of putative HBPs within a large number of candidate proteins. A brand-new machine-learning-based predictor is suggested as the HBP identification method. To produce the desirable feature set for the method proposed, statistical moment-based features and amino acids were combined, and the random forest was used to train the feature set. During 5-fold cross validation experiments, the suggested method achieved 94.37% accuracy and 0.9438 F1-scores, respectively, demonstrating the importance of the Hahn moment-based features.
format Online
Article
Text
id pubmed-10252793
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-102527932023-06-10 Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum Butt, Ahmad Hassan Alkhalifah, Tamim Alturise, Fahad Khan, Yaser Daanial Diagnostics (Basel) Article Hormone-binding proteins (HBPs) are specific carrier proteins that bind to a given hormone. A soluble carrier hormone binding protein (HBP), which can interact non-covalently and specifically with growth hormone, modulates or inhibits hormone signaling. HBP is essential for the growth of life, despite still being poorly understood. Several diseases, according to some data, are caused by HBPs that express themselves abnormally. Accurate identification of these molecules is the first step in investigating the roles of HBPs and understanding their biological mechanisms. For a better understanding of cell development and cellular mechanisms, accurate HBP determination from a given protein sequence is essential. Using traditional biochemical experiments, it is difficult to correctly separate HBPs from an increasing number of proteins because of the high experimental costs and lengthy experiment periods. The abundance of protein sequence data that has been gathered in the post-genomic era necessitates a computational method that is automated and enables quick and accurate identification of putative HBPs within a large number of candidate proteins. A brand-new machine-learning-based predictor is suggested as the HBP identification method. To produce the desirable feature set for the method proposed, statistical moment-based features and amino acids were combined, and the random forest was used to train the feature set. During 5-fold cross validation experiments, the suggested method achieved 94.37% accuracy and 0.9438 F1-scores, respectively, demonstrating the importance of the Hahn moment-based features. MDPI 2023-06-01 /pmc/articles/PMC10252793/ /pubmed/37296792 http://dx.doi.org/10.3390/diagnostics13111940 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Butt, Ahmad Hassan
Alkhalifah, Tamim
Alturise, Fahad
Khan, Yaser Daanial
Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum
title Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum
title_full Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum
title_fullStr Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum
title_full_unstemmed Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum
title_short Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum
title_sort ensemble learning for hormone binding protein prediction: a promising approach for early diagnosis of thyroid hormone disorders in serum
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252793/
https://www.ncbi.nlm.nih.gov/pubmed/37296792
http://dx.doi.org/10.3390/diagnostics13111940
work_keys_str_mv AT buttahmadhassan ensemblelearningforhormonebindingproteinpredictionapromisingapproachforearlydiagnosisofthyroidhormonedisordersinserum
AT alkhalifahtamim ensemblelearningforhormonebindingproteinpredictionapromisingapproachforearlydiagnosisofthyroidhormonedisordersinserum
AT alturisefahad ensemblelearningforhormonebindingproteinpredictionapromisingapproachforearlydiagnosisofthyroidhormonedisordersinserum
AT khanyaserdaanial ensemblelearningforhormonebindingproteinpredictionapromisingapproachforearlydiagnosisofthyroidhormonedisordersinserum