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Bird Eye View of Protein Subcellular Localization Prediction

Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locat...

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Autores principales: Kumar, Ravindra, Dhanda, Sandeep Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764902/
https://www.ncbi.nlm.nih.gov/pubmed/33327400
http://dx.doi.org/10.3390/life10120347
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author Kumar, Ravindra
Dhanda, Sandeep Kumar
author_facet Kumar, Ravindra
Dhanda, Sandeep Kumar
author_sort Kumar, Ravindra
collection PubMed
description Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.
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spelling pubmed-77649022020-12-27 Bird Eye View of Protein Subcellular Localization Prediction Kumar, Ravindra Dhanda, Sandeep Kumar Life (Basel) Review Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions. MDPI 2020-12-14 /pmc/articles/PMC7764902/ /pubmed/33327400 http://dx.doi.org/10.3390/life10120347 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Kumar, Ravindra
Dhanda, Sandeep Kumar
Bird Eye View of Protein Subcellular Localization Prediction
title Bird Eye View of Protein Subcellular Localization Prediction
title_full Bird Eye View of Protein Subcellular Localization Prediction
title_fullStr Bird Eye View of Protein Subcellular Localization Prediction
title_full_unstemmed Bird Eye View of Protein Subcellular Localization Prediction
title_short Bird Eye View of Protein Subcellular Localization Prediction
title_sort bird eye view of protein subcellular localization prediction
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764902/
https://www.ncbi.nlm.nih.gov/pubmed/33327400
http://dx.doi.org/10.3390/life10120347
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