{"title":"A comparative study on network motif discovery algorithms.","authors":"Yusuf Kavurucu","doi":"10.1504/ijdmb.2015.066777","DOIUrl":null,"url":null,"abstract":"<p><p>Subgraphs that occur in complex networks with significantly higher frequency than those in randomised networks are called network motifs. Such subgraphs often play important roles in the functioning of those networks. Finding network motifs is a computationally challenging problem. The main difficulties arise from the fact that real networks are large and the size of the search space grows exponentially with increasing network and motif size. Numerous methods have been developed to overcome these challenges. This paper provides a comparative study of the key network motif discovery algorithms in the literature and presents their algorithmic details on an example network.</p>","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"11 2","pages":"180-204"},"PeriodicalIF":0.2000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.066777","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Mining and Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1504/ijdmb.2015.066777","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 8
Abstract
Subgraphs that occur in complex networks with significantly higher frequency than those in randomised networks are called network motifs. Such subgraphs often play important roles in the functioning of those networks. Finding network motifs is a computationally challenging problem. The main difficulties arise from the fact that real networks are large and the size of the search space grows exponentially with increasing network and motif size. Numerous methods have been developed to overcome these challenges. This paper provides a comparative study of the key network motif discovery algorithms in the literature and presents their algorithmic details on an example network.
期刊介绍:
Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.