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Knowledge graph embedding for profiling the interaction between transcription factors and their target genes

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be c...

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Autores principales: Wu, Yang-Han, Huang, Yu-An, Li, Jian-Qiang, You, Zhu-Hong, Hu, Peng-Wei, Hu, Lun, Leung, Victor C. M., Du, Zhi-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313080/
https://www.ncbi.nlm.nih.gov/pubmed/37339154
http://dx.doi.org/10.1371/journal.pcbi.1011207
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author Wu, Yang-Han
Huang, Yu-An
Li, Jian-Qiang
You, Zhu-Hong
Hu, Peng-Wei
Hu, Lun
Leung, Victor C. M.
Du, Zhi-Hua
author_facet Wu, Yang-Han
Huang, Yu-An
Li, Jian-Qiang
You, Zhu-Hong
Hu, Peng-Wei
Hu, Lun
Leung, Victor C. M.
Du, Zhi-Hua
author_sort Wu, Yang-Han
collection PubMed
description Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.
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spelling pubmed-103130802023-07-01 Knowledge graph embedding for profiling the interaction between transcription factors and their target genes Wu, Yang-Han Huang, Yu-An Li, Jian-Qiang You, Zhu-Hong Hu, Peng-Wei Hu, Lun Leung, Victor C. M. Du, Zhi-Hua PLoS Comput Biol Research Article Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem. Public Library of Science 2023-06-20 /pmc/articles/PMC10313080/ /pubmed/37339154 http://dx.doi.org/10.1371/journal.pcbi.1011207 Text en © 2023 Wu 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
Wu, Yang-Han
Huang, Yu-An
Li, Jian-Qiang
You, Zhu-Hong
Hu, Peng-Wei
Hu, Lun
Leung, Victor C. M.
Du, Zhi-Hua
Knowledge graph embedding for profiling the interaction between transcription factors and their target genes
title Knowledge graph embedding for profiling the interaction between transcription factors and their target genes
title_full Knowledge graph embedding for profiling the interaction between transcription factors and their target genes
title_fullStr Knowledge graph embedding for profiling the interaction between transcription factors and their target genes
title_full_unstemmed Knowledge graph embedding for profiling the interaction between transcription factors and their target genes
title_short Knowledge graph embedding for profiling the interaction between transcription factors and their target genes
title_sort knowledge graph embedding for profiling the interaction between transcription factors and their target genes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313080/
https://www.ncbi.nlm.nih.gov/pubmed/37339154
http://dx.doi.org/10.1371/journal.pcbi.1011207
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