A Self-consistent Probabilistic Formulation for Inference of Interactions

Overview

Journal Sci Rep

Specialty Science

Date 2020 Dec 9

PMID 33293622

Authors

Jorge Fernandez-de-Cossio

Jorge Fernandez-de-Cossio-Diaz

Yasser Perera-Negrin

Affiliations

Soon will be listed here.

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.

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