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Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants

Investigating network behavior from host-pathogen interactions is challenging. Here, we present the deep-learning-based protocol to construct an immune-related gene network and list the genes involved in the defense response of host to specific biotic stress. The protocol includes the steps to pre-p...

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Detalles Bibliográficos
Autores principales: Kumar, Ravi, Acharya, Vishal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791427/
https://www.ncbi.nlm.nih.gov/pubmed/36525344
http://dx.doi.org/10.1016/j.xpro.2022.101934
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author Kumar, Ravi
Acharya, Vishal
author_facet Kumar, Ravi
Acharya, Vishal
author_sort Kumar, Ravi
collection PubMed
description Investigating network behavior from host-pathogen interactions is challenging. Here, we present the deep-learning-based protocol to construct an immune-related gene network and list the genes involved in the defense response of host to specific biotic stress. The protocol includes the steps to pre-process the interaction pairs and expression profile of plants treated with pathogen/control, feed as input for DLNet algorithm to rank genes based on their contribution to data classification. The top-ranked genes are subjected to module and enrichment analysis. For complete details on the use and execution of this protocol, please refer to Kumar et al. (2022).(1)
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spelling pubmed-97914272022-12-27 Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants Kumar, Ravi Acharya, Vishal STAR Protoc Protocol Investigating network behavior from host-pathogen interactions is challenging. Here, we present the deep-learning-based protocol to construct an immune-related gene network and list the genes involved in the defense response of host to specific biotic stress. The protocol includes the steps to pre-process the interaction pairs and expression profile of plants treated with pathogen/control, feed as input for DLNet algorithm to rank genes based on their contribution to data classification. The top-ranked genes are subjected to module and enrichment analysis. For complete details on the use and execution of this protocol, please refer to Kumar et al. (2022).(1) Elsevier 2022-12-14 /pmc/articles/PMC9791427/ /pubmed/36525344 http://dx.doi.org/10.1016/j.xpro.2022.101934 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Kumar, Ravi
Acharya, Vishal
Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants
title Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants
title_full Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants
title_fullStr Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants
title_full_unstemmed Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants
title_short Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants
title_sort deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791427/
https://www.ncbi.nlm.nih.gov/pubmed/36525344
http://dx.doi.org/10.1016/j.xpro.2022.101934
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