独创性
下游(制造业)
上游(联网)
计算机科学
网络分析
基因调控网络
数据挖掘
通路分析
对比度(视觉)
一套
调节器
分析
数据科学
计算生物学
人工智能
生物
基因
基因表达
工程类
遗传学
计算机网络
运营管理
新古典经济学
电气工程
考古
经济
历史
作者
A. Krämer,Jeff Green,Jack Pollard,Stuart Tugendreich
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2013-12-13
卷期号:30 (4): 523-530
被引量:4840
标识
DOI:10.1093/bioinformatics/btt703
摘要
Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets.We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets.The causal analytics tools 'Upstream Regulator Analysis', 'Mechanistic Networks', 'Causal Network Analysis' and 'Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com).Supplementary material is available at Bioinformatics online.
科研通智能强力驱动
Strongly Powered by AbleSci AI