Sharma, Alokanand and López, Y. and Tsunoda, T. (2017) Divisive hierarchical maximum likelihood clustering. BMC Bioinformatics, 18 (16). p. 546. ISSN 1471-2105
Full text not available from this repository. (Request a copy)Abstract
Background
Biological data comprises various topologies or a mixture of forms, which makes its analysis extremely complicated. With this data increasing in a daily basis, the design and development of efficient and accurate statistical methods has become absolutely necessary. Specific analyses, such as those related to genome-wide association studies and multi-omics information, are often aimed at clustering sub-conditions of cancers and other diseases. Hierarchical clustering methods, which can be categorized into agglomerative and divisive, have been widely used in such situations. However, unlike agglomerative methods divisive clustering approaches have consistently proved to be computationally expensive.
Results
The proposed clustering algorithm (DRAGON) was verified on mutation and microarray data, and was gauged against standard clustering methods in the literature. Its validation included synthetic and significant biological data. When validated on mixed-lineage leukemia data, DRAGON achieved the highest clustering accuracy with data of four different dimensions. Consequently, DRAGON outperformed previous methods with 3-,4- and 5-dimensional acute leukemia data. When tested on mutation data, DRAGON achieved the best performance with 2-dimensional information.
Conclusions
This work proposes a computationally efficient divisive hierarchical clustering method, which can compete equally with agglomerative approaches. The proposed method turned out to correctly cluster data with distinct topologies. A MATLAB implementation can be extraced from.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | USP RSC Assistant |
| Date Deposited: | 15 Jan 2018 04:05 |
| Last Modified: | 15 Jan 2018 04:05 |
| URI: | https://repository.usp.ac.fj/id/eprint/10515 |
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