The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data
Open accessAbstract
With modern technologies such as microarray, deep sequencing, and liquid chromatography-mass spectrometry (LC-MS), it is possible to measure the expression levels of thousands of genes/proteins simultaneously to unravel important biological processes. A very first step towards elucidating hidden patterns and understanding the massive data is the application of clustering techniques. Nonlinear relations, which were mostly unutilized in contrast to linear correlations, are prevalent in high-throughput data. In many cases, nonlinear relations can model the biological relationship more precisely and reflect critical patterns in the biological systems. Using the general dependency measure, Distance Based on Conditional Ordered List (DCOL) that we introduced before, we designed the nonlinearK-profiles clustering method, which can be seen as the nonlinear counterpart of theK-means clustering algorithm. The method has a built-in statistical testing procedure that ensures genes not belonging to any cluster do not impact the estimation of cluster profiles. Results from extensive simulation studies showed thatK-profiles clustering not only outperformed traditional linearK-means algorithm, but also presented significantly better performance over our previous General Dependency Hierarchical Clustering (GDHC) algorithm. We further analyzed a gene expression dataset, on whichK-profile clustering generated biologically meaningful results.
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Metadata
- Authors
- Wang, K., Zhao, Q., Lu, J., & Yu, T.
- Venue
- BioMed Research International
- Publisher
- Wiley
- Published
- 2015
- Volume
- 2015
- Pages
- 1-10
- ISSN
- 1110-7243 (ISSN-L)
- 2314-6133
- 2314-6141
- OpenAlex
- W2095691234
- Cited by
- 989
- References
- 23
- FWCI
- 47.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
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