推论
因果推理
计算机科学
基因调控网络
因果关系(物理学)
机器学习
人工智能
熵(时间箭头)
数据挖掘
传递熵
计量经济学
最大熵原理
数学
生物
基因
基因表达
物理
量子力学
生物化学
作者
Syed Sazzad Ahmed,Swarup Roy,Jugal Kalita
标识
DOI:10.1109/tcbb.2018.2853728
摘要
Causality inference is the use of computational techniques to predict possible causal relationships for a set of variables, thereby forming a directed network. Causality inference in Gene Regulatory Networks (GRNs) is an important, yet challenging task due to the limits of available data and lack of efficiency in existing causality inference techniques. A number of techniques have been proposed and applied to infer causal relationships in various domains, although they are not specific to regulatory network inference. In this paper, we assess the effectiveness of methods for inferring causal GRNs. We introduce seven different inference methods and apply them to infer directed edges in GRNs. We use time-series expression data from the DREAM challenges to assess the methods in terms of quality of inference and rank them based on performance. The best method is applied to Breast Cancer data to infer a causal network. Experimental results show that Causation Entropy is best, however, highly time-consuming and not feasible to use in a relatively large network. We infer Breast Cancer GRN with the second-best method, Transfer Entropy. The topological analysis of the network reveals that top out-degree genes such as SLC39A5which are considered central genes, play important role in cancer progression.
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