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dc.contributor.authorSharma, Alok
dc.contributor.authorShigemizu, Daichi
dc.contributor.authorBoroevich, Keith A
dc.contributor.authorLopez, Yosvany
dc.contributor.authorKamatani, Yoichiro
dc.contributor.authorKubo, Michiaki
dc.contributor.authorTsunoda, Tatsuhiko
dc.date.accessioned2017-07-17T03:42:15Z
dc.date.available2017-07-17T03:42:15Z
dc.date.issued2016
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/s12859-016-1184-5
dc.identifier.urihttp://hdl.handle.net/10072/124018
dc.description.abstractBackground: Biological/genetic data is a complex mix of various forms or topologies which makes it quite difficult to analyze. An abundance of such data in this modern era requires the development of sophisticated statistical methods to analyze it in a reasonable amount of time. In many biological/genetic analyses, such as genome-wide association study (GWAS) analysis or multi-omics data analysis, it is required to cluster the plethora of data into sub-categories to understand the subtypes of populations, cancers or any other diseases. Traditionally, the k-means clustering algorithm is a dominant clustering method. This is due to its simplicity and reasonable level of accuracy. Many other clustering methods, including support vector clustering, have been developed in the past, but do not perform well with the biological data, either due to computational reasons or failure to identify clusters. Results: The proposed SIML clustering algorithm has been tested on microarray datasets and SNP datasets. It has been compared with a number of clustering algorithms. On MLL datasets, SIML achieved highest clustering accuracy and rand score on 4/9 cases; similarly on SRBCT dataset, it got for 3/5 cases; on ALL subtype it got highest clustering accuracy for 5/7 cases and highest rand score for 4/7 cases. In addition, SIML overall clustering accuracy on a 3 cluster problem using SNP data were 97.3, 94.7 and 100 %, respectively, for each of the clusters. Conclusions: In this paper, considering the nature of biological data, we proposed a maximum likelihood clustering approach using a stepwise iterative procedure. The advantage of this proposed method is that it not only uses the distance information, but also incorporate variance information for clustering. This method is able to cluster when data appeared in overlapping and complex forms. The experimental results illustrate its performance and usefulness over other clustering methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBioMed Central
dc.relation.ispartofpagefrom319-1
dc.relation.ispartofpageto319-14
dc.relation.ispartofjournalBMC Bioinformatics
dc.relation.ispartofvolume17
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchBioinformatics and computational biology
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.subject.fieldofresearchcode3102
dc.titleStepwise iterative maximum likelihood clustering approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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gro.griffith.authorSharma, Alok


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