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A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification

Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria parasite mitochondrial proteins is essential for understanding their functions and identifying novel drug targets. For classifying protein sequences, se...

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Autores principales: Alsanousi, Wafa Alameen, Ahmed, Nosiba Yousif, Hamid, Eman Mohammed, Elbashir, Murtada K., Musa, Mohamed Elhafiz M., Wang, Jianxin, Khan, Noman, Afnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9536844/
https://www.ncbi.nlm.nih.gov/pubmed/36201724
http://dx.doi.org/10.1371/journal.pone.0275195
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author Alsanousi, Wafa Alameen
Ahmed, Nosiba Yousif
Hamid, Eman Mohammed
Elbashir, Murtada K.
Musa, Mohamed Elhafiz M.
Wang, Jianxin
Khan, Noman
Afnan,
author_facet Alsanousi, Wafa Alameen
Ahmed, Nosiba Yousif
Hamid, Eman Mohammed
Elbashir, Murtada K.
Musa, Mohamed Elhafiz M.
Wang, Jianxin
Khan, Noman
Afnan,
author_sort Alsanousi, Wafa Alameen
collection PubMed
description Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria parasite mitochondrial proteins is essential for understanding their functions and identifying novel drug targets. For classifying protein sequences, several adaptive statistical techniques have been devised. Despite significant gains, prediction performance is still constrained by the lack of appropriate feature descriptors and learning strategies in current systems. Moreover, good ground truth data is important for Artificial Intelligence (AI)-based models but there is a lack of that data in the literature. Therefore, in this work, we propose a novel hybrid network that combines 1D Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BGRU) to classify the malaria parasite mitochondrial proteins. Furthermore, we curate a sequential data that are collected from National Center for Biotechnology Information (NCBI) and UniProtKB/Swiss-Prot proteins databanks to prepare a dataset that can be used by the research community for AI-based algorithms evaluation. We obtain 4204 cases after preprocessing of the collected data and denote this set of proteins as PF4204. Finally, we conduct an ablation study on several conventional and deep models using PF4204 and the benchmark PF2095 datasets. The proposed model ‘CNN-BGRU’ obtains the accuracy values of 0.9096 and 0.9857 on PF4204 and PF2095 datasets, respectively. In addition, the CNN-BGRU is compared with state-of-the-arts, where the results illustrate that it can extract robust features and identify proteins accurately.
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spelling pubmed-95368442022-10-07 A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification Alsanousi, Wafa Alameen Ahmed, Nosiba Yousif Hamid, Eman Mohammed Elbashir, Murtada K. Musa, Mohamed Elhafiz M. Wang, Jianxin Khan, Noman Afnan, PLoS One Research Article Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria parasite mitochondrial proteins is essential for understanding their functions and identifying novel drug targets. For classifying protein sequences, several adaptive statistical techniques have been devised. Despite significant gains, prediction performance is still constrained by the lack of appropriate feature descriptors and learning strategies in current systems. Moreover, good ground truth data is important for Artificial Intelligence (AI)-based models but there is a lack of that data in the literature. Therefore, in this work, we propose a novel hybrid network that combines 1D Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BGRU) to classify the malaria parasite mitochondrial proteins. Furthermore, we curate a sequential data that are collected from National Center for Biotechnology Information (NCBI) and UniProtKB/Swiss-Prot proteins databanks to prepare a dataset that can be used by the research community for AI-based algorithms evaluation. We obtain 4204 cases after preprocessing of the collected data and denote this set of proteins as PF4204. Finally, we conduct an ablation study on several conventional and deep models using PF4204 and the benchmark PF2095 datasets. The proposed model ‘CNN-BGRU’ obtains the accuracy values of 0.9096 and 0.9857 on PF4204 and PF2095 datasets, respectively. In addition, the CNN-BGRU is compared with state-of-the-arts, where the results illustrate that it can extract robust features and identify proteins accurately. Public Library of Science 2022-10-06 /pmc/articles/PMC9536844/ /pubmed/36201724 http://dx.doi.org/10.1371/journal.pone.0275195 Text en © 2022 Alsanousi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Alsanousi, Wafa Alameen
Ahmed, Nosiba Yousif
Hamid, Eman Mohammed
Elbashir, Murtada K.
Musa, Mohamed Elhafiz M.
Wang, Jianxin
Khan, Noman
Afnan,
A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification
title A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification
title_full A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification
title_fullStr A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification
title_full_unstemmed A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification
title_short A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification
title_sort novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9536844/
https://www.ncbi.nlm.nih.gov/pubmed/36201724
http://dx.doi.org/10.1371/journal.pone.0275195
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