An improved deep learning model for hierarchical classification of protein families.

Although genes carry information, proteins are the main role player in providing all the functionalities of a living organism. Massive amounts of different proteins involve in every function that occurs in a cell. These amino acid sequences can be hierarchically classified into a set of families and...

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Autores principales:	Pahalage Dhanushka Sandaruwan, Champi Thusangi Wannige
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/613f32d306a4401e82639bad9a8c56d2
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spelling	oai:doaj.org-article:613f32d306a4401e82639bad9a8c56d22021-12-02T20:16:45ZAn improved deep learning model for hierarchical classification of protein families.1932-620310.1371/journal.pone.0258625https://doaj.org/article/613f32d306a4401e82639bad9a8c56d22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258625https://doaj.org/toc/1932-6203Although genes carry information, proteins are the main role player in providing all the functionalities of a living organism. Massive amounts of different proteins involve in every function that occurs in a cell. These amino acid sequences can be hierarchically classified into a set of families and subfamilies depending on their evolutionary relatedness and similarities in their structure or function. Protein characterization to identify protein structure and function is done accurately using laboratory experiments. With the rapidly increasing huge amount of novel protein sequences, these experiments have become difficult to carry out since they are expensive, time-consuming, and laborious. Therefore, many computational classification methods are introduced to classify proteins and predict their functional properties. With the progress of the performance of the computational techniques, deep learning plays a key role in many areas. Novel deep learning models such as DeepFam, ProtCNN have been presented to classify proteins into their families recently. However, these deep learning models have been used to carry out the non-hierarchical classification of proteins. In this research, we propose a deep learning neural network model named DeepHiFam with high accuracy to classify proteins hierarchically into different levels simultaneously. The model achieved an accuracy of 98.38% for protein family classification and more than 80% accuracy for the classification of protein subfamilies and sub-subfamilies. Further, DeepHiFam performed well in the non-hierarchical classification of protein families and achieved an accuracy of 98.62% and 96.14% for the popular Pfam dataset and COG dataset respectively.Pahalage Dhanushka SandaruwanChampi Thusangi WannigePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258625 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pahalage Dhanushka Sandaruwan Champi Thusangi Wannige An improved deep learning model for hierarchical classification of protein families.
description	Although genes carry information, proteins are the main role player in providing all the functionalities of a living organism. Massive amounts of different proteins involve in every function that occurs in a cell. These amino acid sequences can be hierarchically classified into a set of families and subfamilies depending on their evolutionary relatedness and similarities in their structure or function. Protein characterization to identify protein structure and function is done accurately using laboratory experiments. With the rapidly increasing huge amount of novel protein sequences, these experiments have become difficult to carry out since they are expensive, time-consuming, and laborious. Therefore, many computational classification methods are introduced to classify proteins and predict their functional properties. With the progress of the performance of the computational techniques, deep learning plays a key role in many areas. Novel deep learning models such as DeepFam, ProtCNN have been presented to classify proteins into their families recently. However, these deep learning models have been used to carry out the non-hierarchical classification of proteins. In this research, we propose a deep learning neural network model named DeepHiFam with high accuracy to classify proteins hierarchically into different levels simultaneously. The model achieved an accuracy of 98.38% for protein family classification and more than 80% accuracy for the classification of protein subfamilies and sub-subfamilies. Further, DeepHiFam performed well in the non-hierarchical classification of protein families and achieved an accuracy of 98.62% and 96.14% for the popular Pfam dataset and COG dataset respectively.
format	article
author	Pahalage Dhanushka Sandaruwan Champi Thusangi Wannige
author_facet	Pahalage Dhanushka Sandaruwan Champi Thusangi Wannige
author_sort	Pahalage Dhanushka Sandaruwan
title	An improved deep learning model for hierarchical classification of protein families.
title_short	An improved deep learning model for hierarchical classification of protein families.
title_full	An improved deep learning model for hierarchical classification of protein families.
title_fullStr	An improved deep learning model for hierarchical classification of protein families.
title_full_unstemmed	An improved deep learning model for hierarchical classification of protein families.
title_sort	improved deep learning model for hierarchical classification of protein families.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/613f32d306a4401e82639bad9a8c56d2
work_keys_str_mv	AT pahalagedhanushkasandaruwan animproveddeeplearningmodelforhierarchicalclassificationofproteinfamilies AT champithusangiwannige animproveddeeplearningmodelforhierarchicalclassificationofproteinfamilies AT pahalagedhanushkasandaruwan improveddeeplearningmodelforhierarchicalclassificationofproteinfamilies AT champithusangiwannige improveddeeplearningmodelforhierarchicalclassificationofproteinfamilies
_version_	1718374470607962112

An improved deep learning model for hierarchical classification of protein families.

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