Causality modeling for directed disease network | Zendy

Sunjoo Bang | Zendy; Jae-Hoon Kim | Zendy; Hyunjung Shin | Zendy

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Causality modeling for directed disease network

Author(s) -

Sunjoo Bang,

Jae-Hoon Kim,

Hyunjung Shin

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw439

Subject(s) - causality (physics) , computer science , disease , data science , data mining , causal structure , kegg , medicine , biology , gene , pathology , gene expression , physics , transcriptome , quantum mechanics , biochemistry

Causality between two diseases is valuable information as subsidiary information for medicine which is intended for prevention, diagnostics and treatment. Conventional cohort-centric researches are able to obtain very objective results, however, they demands costly experimental expense and long period of time. Recently, data source to clarify causality has been diversified: available information includes gene, protein, metabolic pathway and clinical information. By taking full advantage of those pieces of diverse information, we may extract causalities between diseases, alternatively to cohort-centric researches.

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