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Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways
A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic pote...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118548/ https://www.ncbi.nlm.nih.gov/pubmed/33939702 http://dx.doi.org/10.1371/journal.pcbi.1008960 |
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author | Kontio, Juho A. J. Pyhäjärvi, Tanja Sillanpää, Mikko J. |
author_facet | Kontio, Juho A. J. Pyhäjärvi, Tanja Sillanpää, Mikko J. |
author_sort | Kontio, Juho A. J. |
collection | PubMed |
description | A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method’s high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework’s versatility and broad practical relevance. |
format | Online Article Text |
id | pubmed-8118548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81185482021-05-24 Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways Kontio, Juho A. J. Pyhäjärvi, Tanja Sillanpää, Mikko J. PLoS Comput Biol Research Article A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method’s high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework’s versatility and broad practical relevance. Public Library of Science 2021-05-03 /pmc/articles/PMC8118548/ /pubmed/33939702 http://dx.doi.org/10.1371/journal.pcbi.1008960 Text en © 2021 Kontio 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 Kontio, Juho A. J. Pyhäjärvi, Tanja Sillanpää, Mikko J. Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways |
title | Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways |
title_full | Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways |
title_fullStr | Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways |
title_full_unstemmed | Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways |
title_short | Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways |
title_sort | model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118548/ https://www.ncbi.nlm.nih.gov/pubmed/33939702 http://dx.doi.org/10.1371/journal.pcbi.1008960 |
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