High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network
Publication in refereed journal


引用次數
替代計量分析
.

其它資訊
摘要Background: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes.
著者Lo LY, Wong ML, Lee KH, Leung KS
期刊名稱BMC Bioinformatics
出版年份2015
月份11
日期25
卷號16
出版社BIOMED CENTRAL LTD
國際標準期刊號1471-2105
語言英式英語
關鍵詞Causality inference; Gene regulatory network; Hidden common cause; High-order dynamic Bayesian Network
Web of Science 學科類別Biochemical Research Methods; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology

上次更新時間 2021-25-10 於 00:27