{"type":"ScholarlyArticle","title":"FSM: A Fast Similarity Measurement for Gene Regulatory Networks via Genes' Influence Power","authors":[{"name":"Zhongzhou Liu","slug":"zhongzhou-liu","given":"Zhongzhou","family":"Liu","affiliation":"School of Computer Science , Wuhan University , China"},{"name":"Wenbin Hu","slug":"wenbin-hu","given":"Wenbin","family":"Hu","affiliation":"School of Computer Science , Wuhan University , China; Shenzhen Research Institute , Wuhan University , China"}],"abstract":"The problem of graph similarity measurement is fundamental in both complex networks and bioinformatics researches. Gene regulatory networks (GRNs) describe the interactions between the molecules in organisms, and are widely studied in the fields of medical AI. By measuring the similarity between GRNs, significant information can be obtained to assist the applications like gene functions prediction, drug development and medical diagnosis. Most of the existing similarity measurements have been focusing on the graph isomorphisms and are usually NP-hard problems. Thus, they are not suitable for applications in biology and clinical research due to the complexity and large-scale features of real-world GRNs. In this paper, a fast similarity measurement method called FSM for GRNs is proposed. Unlike the conventional measurements, it pays more attention to the differences between those influential genes. For the convenience and reliability, a new index defined as influence power is adopted to describe the influential genes which have greater position in a GRN. FSM was applied in nine datasets of various scales and is compared with state-of-art methods. The results demonstrated that it ran significantly faster than other methods without sacrificing measurement performance.","doi":"10.24963/ijcai.2019/632","url":"https://ijcai.org/proceedings/2019/632","pdf":"https://ijcai.org/proceedings/2019/0632.pdf","markdown":"https://ijcai.org/proceedings/2019/632.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/632","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning Applications","track":"Main track","number":632,"pages":{"first":4547,"last":4553},"keywords":["Machine Learning Applications: Bio;Medicine","Machine Learning: Data Mining","Machine Learning Applications: Networks","Multidisciplinary Topics and Applications: Biology and Medicine"],"videos":[],"references":[{"position":1,"raw":"[Borgwardt and Kriegel, 2005] Karsten M Borgwardt and Hans-Peter Kriegel. 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