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Causality modeling for directed disease network

机译:定向疾病网络的因果关系建模

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Motivation: 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.
机译:动机:两种疾病之间的因果关系是旨在预防,诊断和治疗的药物的有价值信息。 传统的群体为中心的研究能够获得非常客观的结果,但是,它们要求昂贵的实验费用和长时间。 最近,澄清因果关系的数据源已经多样化:可用信息包括基因,蛋白质,代谢途径和临床信息。 通过充分利用这些不同的信息,我们可能会提取疾病之间的因果,或者以与群体为中心的研究。

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