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Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph
Constructing a novel bioinformatic workflow by reusing and repurposing fragments crossing workflows is regarded as an error-avoiding and effort-saving strategy. Traditional techniques have been proposed to discover scientific workflow fragments leveraging their profiles and historical usages of thei...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459048/ https://www.ncbi.nlm.nih.gov/pubmed/36092917 http://dx.doi.org/10.3389/fgene.2022.941996 |
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author | Diao, Jin Zhou, Zhangbing Xue, Xiao Zhao, Deng Chen, Shengpeng |
author_facet | Diao, Jin Zhou, Zhangbing Xue, Xiao Zhao, Deng Chen, Shengpeng |
author_sort | Diao, Jin |
collection | PubMed |
description | Constructing a novel bioinformatic workflow by reusing and repurposing fragments crossing workflows is regarded as an error-avoiding and effort-saving strategy. Traditional techniques have been proposed to discover scientific workflow fragments leveraging their profiles and historical usages of their activities (or services). However, social relations of workflows, including relations between services and their developers have not been explored extensively. In fact, current techniques describe invoking relations between services, mostly, and they can hardly reveal implicit relations between services. To address this challenge, we propose a social-aware scientific workflow knowledge graph (S (2) KG) to capture common types of entities and various types of relations by analyzing relevant information about bioinformatic workflows and their developers recorded in repositories. Using attributes of entities such as credit and creation time, the union impact of several positive and negative links in S (2) KG is identified, to evaluate the feasibility of workflow fragment construction. To facilitate the discovery of single services, a service invoking network is extracted form S (2) KG, and service communities are constructed accordingly. A bioinformatic workflow fragment discovery mechanism based on Yen’s method is developed to discover appropriate fragments with respect to certain user’s requirements. Extensive experiments are conducted, where bioinformatic workflows publicly accessible at the myExperiment repository are adopted. Evaluation results show that our technique performs better than the state-of-the-art techniques in terms of the precision, recall, and F1. |
format | Online Article Text |
id | pubmed-9459048 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94590482022-09-10 Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph Diao, Jin Zhou, Zhangbing Xue, Xiao Zhao, Deng Chen, Shengpeng Front Genet Genetics Constructing a novel bioinformatic workflow by reusing and repurposing fragments crossing workflows is regarded as an error-avoiding and effort-saving strategy. Traditional techniques have been proposed to discover scientific workflow fragments leveraging their profiles and historical usages of their activities (or services). However, social relations of workflows, including relations between services and their developers have not been explored extensively. In fact, current techniques describe invoking relations between services, mostly, and they can hardly reveal implicit relations between services. To address this challenge, we propose a social-aware scientific workflow knowledge graph (S (2) KG) to capture common types of entities and various types of relations by analyzing relevant information about bioinformatic workflows and their developers recorded in repositories. Using attributes of entities such as credit and creation time, the union impact of several positive and negative links in S (2) KG is identified, to evaluate the feasibility of workflow fragment construction. To facilitate the discovery of single services, a service invoking network is extracted form S (2) KG, and service communities are constructed accordingly. A bioinformatic workflow fragment discovery mechanism based on Yen’s method is developed to discover appropriate fragments with respect to certain user’s requirements. Extensive experiments are conducted, where bioinformatic workflows publicly accessible at the myExperiment repository are adopted. Evaluation results show that our technique performs better than the state-of-the-art techniques in terms of the precision, recall, and F1. Frontiers Media S.A. 2022-08-26 /pmc/articles/PMC9459048/ /pubmed/36092917 http://dx.doi.org/10.3389/fgene.2022.941996 Text en Copyright © 2022 Diao, Zhou, Xue, Zhao and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Diao, Jin Zhou, Zhangbing Xue, Xiao Zhao, Deng Chen, Shengpeng Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph |
title | Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph |
title_full | Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph |
title_fullStr | Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph |
title_full_unstemmed | Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph |
title_short | Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph |
title_sort | bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459048/ https://www.ncbi.nlm.nih.gov/pubmed/36092917 http://dx.doi.org/10.3389/fgene.2022.941996 |
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