Comparison of Software Packages for Bayesian Network Learning in Gene Regulatory Relationship Mining

被引：0
作者：
Kang, Yu [1 ]
Yang, Xuan [1 ]
Sun, Menghai [1 ]
Hu, Junfan [1 ]
Zhong, Zhiman [1 ]
Liu, Jianxiao [1 ]
机构：
[1] Huazhong Agr Univ, Coll Informat, Wuhan, Hubei, Peoples R China
来源：
2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) | 2017年
基金：
中国国家自然科学基金;
关键词：
Bayesian network; Gene regulatory; Software package; Alarm; ALGORITHM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Gene regulatory has rapidly become a popular approach to understand the complex regulatory mechanisms in cellular systems. Mining gene regulatory relationship and thus to construct the gene regulatory network of utmost interest and has become a challenging computational problem. In order to realize this aim, a large number of tools and packages have been developed. But there is no clear consensus about which tool is the best practices for users' specific requirements, especially, when a user has no background. Aim to solve this problem, we compare 9 kinds of widely used software packages about Bayesian network learning in gene regulatory relationship mining. Our experiment results demonstrate different packages have different learning efficiency and accuracy, and it can provide general guidelines for choosing a robust selection.
引用
收藏
页码：2010 / 2015
页数：6
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