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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jorge Fernandez-de-Cossio, Jorge Fernandez-de-Cossio-Diaz, Yasser Perera-Negrin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1b80e32ab13549fb8b8eefb924588324
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1b80e32ab13549fb8b8eefb924588324
record_format	dspace
spelling	oai:doaj.org-article:1b80e32ab13549fb8b8eefb9245883242021-12-02T15:11:49ZA self-consistent probabilistic formulation for inference of interactions10.1038/s41598-020-78496-82045-2322https://doaj.org/article/1b80e32ab13549fb8b8eefb9245883242020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78496-8https://doaj.org/toc/2045-2322Abstract 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.Jorge Fernandez-de-CossioJorge Fernandez-de-Cossio-DiazYasser Perera-NegrinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-16 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jorge Fernandez-de-Cossio Jorge Fernandez-de-Cossio-Diaz Yasser Perera-Negrin A self-consistent probabilistic formulation for inference of interactions
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.
format	article
author	Jorge Fernandez-de-Cossio Jorge Fernandez-de-Cossio-Diaz Yasser Perera-Negrin
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 Portfolio
publishDate	2020
url	https://doaj.org/article/1b80e32ab13549fb8b8eefb924588324
work_keys_str_mv	AT jorgefernandezdecossio aselfconsistentprobabilisticformulationforinferenceofinteractions AT jorgefernandezdecossiodiaz aselfconsistentprobabilisticformulationforinferenceofinteractions AT yasserpereranegrin aselfconsistentprobabilisticformulationforinferenceofinteractions AT jorgefernandezdecossio selfconsistentprobabilisticformulationforinferenceofinteractions AT jorgefernandezdecossiodiaz selfconsistentprobabilisticformulationforinferenceofinteractions AT yasserpereranegrin selfconsistentprobabilisticformulationforinferenceofinteractions
_version_	1718387615084838912

A self-consistent probabilistic formulation for inference of interactions

Ejemplares similares