Sharma, Alokanand and Boroevich, Keith and Shigemizu, Daichi and Kamatani, Yoichiro and Kubo, Michiaki and Tsunoda, T.
(2017)
Hierarchical maximum likelihood clustering approach.
IEEE Transactions on Biomedical Engineering, 64
(1).
pp. 112-122.
ISSN 0018-9294
Abstract
Objective:
In this work, we focused on developing a clustering approach for biological data. In many biological
analyses, such as multi-omics data analysis and genome-wide
association studies (GWAS) analysis, it is crucial to find groups of data belonging to subtypes of diseases or tumors. Methods:
Conventionally, the k-means clustering algorithm is
overwhelmingly applied in many areas including biological
sciences. There are, however, several alternative clustering algorithms that can be applied, including support vector clustering. In this paper, taking into consideration the nature of biological data, we propose a maximum likelihood clustering scheme based on a hierarchical framework.
Results: This method can perform clustering even when the data belonging to different groups overlap. It can also perform clustering when the number of samples is lower than the data dimensionality.
Conclusion: The proposed scheme is free from selecting initial settings to begin the search process. In addition, it does not require the computation of the first and second derivative of likelihood functions, as is required by many other maximum likelihood based methods.
Significance: This algorithm uses distribution and centroid
information to cluster a sample and was applied to biological data. A Matlab implementation of this method can be downloaded from the web-link
http://www.riken.jp/en/research/labs/ims/med_sci_math/.
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