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A self-consistent probabilistic formulation for inference of interactions

Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding outcome of an inquired effect, are formulated w...

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Autores principales: Fernandez-de-Cossio, Jorge, Fernandez-de-Cossio-Diaz, Jorge, Perera-Negrin, Yasser
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722874/
https://www.ncbi.nlm.nih.gov/pubmed/33293622
http://dx.doi.org/10.1038/s41598-020-78496-8
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author Fernandez-de-Cossio, Jorge
Fernandez-de-Cossio-Diaz, Jorge
Perera-Negrin, Yasser
author_facet Fernandez-de-Cossio, Jorge
Fernandez-de-Cossio-Diaz, Jorge
Perera-Negrin, Yasser
author_sort Fernandez-de-Cossio, Jorge
collection PubMed
description Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding outcome of an inquired effect, are formulated without an obvious mathematical unity. Consequently, conceptual and practical ambivalences arise. We identify here a probabilistic requirement consistent with that input, and find, by the rules of probability theory, that it leads to a model multiplicative in the complement of the effect. Important practical properties are revealed along these theoretical derivations, that has not been noticed before.
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spelling pubmed-77228742020-12-09 A self-consistent probabilistic formulation for inference of interactions Fernandez-de-Cossio, Jorge Fernandez-de-Cossio-Diaz, Jorge Perera-Negrin, Yasser Sci Rep Article Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding outcome of an inquired effect, are formulated without an obvious mathematical unity. Consequently, conceptual and practical ambivalences arise. We identify here a probabilistic requirement consistent with that input, and find, by the rules of probability theory, that it leads to a model multiplicative in the complement of the effect. Important practical properties are revealed along these theoretical derivations, that has not been noticed before. Nature Publishing Group UK 2020-12-08 /pmc/articles/PMC7722874/ /pubmed/33293622 http://dx.doi.org/10.1038/s41598-020-78496-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Fernandez-de-Cossio, Jorge
Fernandez-de-Cossio-Diaz, Jorge
Perera-Negrin, Yasser
A self-consistent probabilistic formulation for inference of interactions
title A self-consistent probabilistic formulation for inference of interactions
title_full A self-consistent probabilistic formulation for inference of interactions
title_fullStr A self-consistent probabilistic formulation for inference of interactions
title_full_unstemmed A self-consistent probabilistic formulation for inference of interactions
title_short A self-consistent probabilistic formulation for inference of interactions
title_sort self-consistent probabilistic formulation for inference of interactions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722874/
https://www.ncbi.nlm.nih.gov/pubmed/33293622
http://dx.doi.org/10.1038/s41598-020-78496-8
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