Evaluating clustering algorithms for genetic regulatory network structural inference

Christopher Fogelberg, Vasile Palade

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

2 Citations (Scopus)

Abstract

Modern biological research increasingly recognises the importance of genome-wide gene regulatory network inference; however, a range of statistical, technological and biological factors make it a difficult and intractable problem. One approach that some research has used is to cluster the data and then infer a structural model of the clusters. When using this kind of approach it is very important to choose the clustering algorithm carefully. In this paper we explicitly analyse the attributes that make a clustering algorithm appropriate, and we also consider how to measure the quality of the identified clusters. Our analysis leads us to develop three novel cluster quality measures that are based on regulatory overlap. Using these measures we evaluate two modern candidate algorithms: FLAME, and KMART. Although FLAME was specifically developed for clustering gene expression profile data, we find that KMART is probably a better algorithm to use if the goal is to infer a structural model of the clusters.

Original languageEnglish
Title of host publicationResearch and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII
PublisherSpringer
Pages137-149
Number of pages13
ISBN (Print)9781848829824
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2009 - Cambridge, United Kingdom
Duration: 15 Dec 200917 Dec 2009

Conference

Conference29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2009
CountryUnited Kingdom
CityCambridge
Period15/12/0917/12/09

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • Cite this

    Fogelberg, C., & Palade, V. (2010). Evaluating clustering algorithms for genetic regulatory network structural inference. In Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII (pp. 137-149). Springer. https://doi.org/10.1007/978-1-84882-983-1_10