A self-consistent probabilistic formulation for inference of interactions.

Fernandez-de-Cossio, Jorge; Fernandez-de-Cossio-Diaz, Jorge; Perera-Negrin, Yasser

Fernandez-de-Cossio, Jorge; Fernandez-de-Cossio-Diaz, Jorge; Perera-Negrin, Yasser.

Afiliação

Fernandez-de-Cossio J; Bioinformatics Department, Center for Genetic Engineering and Biotechnology (CIGB), PO Box 6162, CP10600, Havana, Cuba. jorge.cossio@cigb.edu.cu.
Fernandez-de-Cossio-Diaz J; Systems Biology Department, Center of Molecular Immunology, PO Box 6162, CP10600, Havana, Cuba.
Perera-Negrin Y; Molecular Oncology Group, Pharmaceutical Division, Center for Genetic Engineering and Biotechnology (CIGB), PO Box 6162, CP10600, Havana, Cuba.

Sci Rep ; 10(1): 21435, 2020 12 08.

Article em En | MEDLINE | ID: mdl-33293622

RESUMO

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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Cuba País de publicação: Reino Unido

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