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Stepwise iterative maximum likelihood clustering approach

Overview of attention for article published in BMC Bioinformatics, August 2016
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Stepwise iterative maximum likelihood clustering approach
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1184-5
Pubmed ID

Alok Sharma, Daichi Shigemizu, Keith A. Boroevich, Yosvany López, Yoichiro Kamatani, Michiaki Kubo, Tatsuhiko Tsunoda


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. 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. 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. A Matlab package of this method (SIML) is provided at the web-link http://www.riken.jp/en/research/labs/ims/med_sci_math/ .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 13%
Researcher 3 13%
Student > Bachelor 3 13%
Student > Master 3 13%
Student > Doctoral Student 1 4%
Other 5 22%
Unknown 5 22%
Readers by discipline Count As %
Computer Science 5 22%
Medicine and Dentistry 4 17%
Agricultural and Biological Sciences 3 13%
Biochemistry, Genetics and Molecular Biology 1 4%
Mathematics 1 4%
Other 4 17%
Unknown 5 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 August 2016.
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