A self-consistent probabilistic formulation for inference of interactions

Abstract 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 for...

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Main Authors:	Jorge Fernandez-de-Cossio, Jorge Fernandez-de-Cossio-Diaz, Yasser Perera-Negrin
Format:	Article
Language:	English
Published:	Nature Publishing Group 2020-12-01
Series:	Scientific Reports
Online Access:	https://doi.org/10.1038/s41598-020-78496-8

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spelling	doaj-1b80e32ab13549fb8b8eefb9245883242020-12-13T12:30:34ZengNature Publishing GroupScientific Reports2045-23222020-12-0110111610.1038/s41598-020-78496-8A self-consistent probabilistic formulation for inference of interactionsJorge Fernandez-de-Cossio0Jorge Fernandez-de-Cossio-Diaz1Yasser Perera-Negrin2Bioinformatics Department, Center for Genetic Engineering and Biotechnology (CIGB)Systems Biology Department, Center of Molecular ImmunologyMolecular Oncology Group, Pharmaceutical Division, Center for Genetic Engineering and Biotechnology (CIGB)Abstract 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.https://doi.org/10.1038/s41598-020-78496-8
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Jorge Fernandez-de-Cossio Jorge Fernandez-de-Cossio-Diaz Yasser Perera-Negrin
spellingShingle	Jorge Fernandez-de-Cossio Jorge Fernandez-de-Cossio-Diaz Yasser Perera-Negrin A self-consistent probabilistic formulation for inference of interactions Scientific Reports
author_facet	Jorge Fernandez-de-Cossio Jorge Fernandez-de-Cossio-Diaz Yasser Perera-Negrin
author_sort	Jorge Fernandez-de-Cossio
title	A self-consistent probabilistic formulation for inference of interactions
title_short	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_sort	self-consistent probabilistic formulation for inference of interactions
publisher	Nature Publishing Group
series	Scientific Reports
issn	2045-2322
publishDate	2020-12-01
description	Abstract 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.
url	https://doi.org/10.1038/s41598-020-78496-8
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A self-consistent probabilistic formulation for inference of interactions

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