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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Bibliographic Details
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
Description
Summary: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.
ISSN:2045-2322