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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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 |
_version_ | 1784859404957384704 |
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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) |
format | Online Article Text |
id | pubmed-9791427 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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