Abstract
Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods.
Highlights
In recent years, the application of causal inference in bioinformatics has become more extensive, and plays a very important role in the development of this field
Shi [9,10] elaborated the thought and implementation process of brain storm optimization (BSO) algorithm, and used the classical test function to simulate the BSO algorithm, and the results showed the effectiveness of BSO algorithm
The graph space was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method
Summary
The application of causal inference in bioinformatics has become more extensive, and plays a very important role in the development of this field. It is used for the discovery of the causal relationships between genes and the development of symptoms [1], and how to analyze the phenomenon of synthetic lethality [2,3] in biomedicine, which arises when a combination of mutations in two or more genes leads to cell death. The causal inference is the internal generative mechanism of the research data and the traditional statistical learning is the joint distribution of observation variables. Causal inference has already been applied in many fields, such as gene therapy, economic prediction, etc
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
