Combined Clustering Methods for Microarray Data Analysis

Raul Malutan; Pedro Gómez Vilda; Monica Borda

doi:10.4028/www.scientific.net/AEF.8-9.508

Paper Titles

The Synchronization of Two Metriplectic Systems Arisen from the Lü System
p.471

Compressed Domain Computationally Efficient Processing Scheme for JPEG Image Filtering
p.480

Research Concerning the Springback Prediction in the Bending Operations
p.490

Matlab Toolbox for RF Receiver Modeling
p.500

Combined Clustering Methods for Microarray Data Analysis
p.508

A 3D Approach to Co-Occurrence Matrix Features Used in Dynamic Textures Indexing
p.516

On some Operator Functional Equations in Locally Convex Algebras
p.527

Sparse Features for Finger Detection
p.535

Optimization of a Multi-Touch Sensing Device for Biomedical Applications
p.545

HomeAdvanced Engineering ForumAdvanced Engineering Forum Vols. 8-9Combined Clustering Methods for Microarray Data...

Combined Clustering Methods for Microarray Data Analysis

Abstract:

Data classification has an important role in analyzing high dimensional data. In this paper Gene Shaving algorithm was used for a previous supervised classification and once the cluster information was obtained, data was classified again with supervised algorithms like Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) for an optimal clustering. These algorithms have proven to be useful when the classes of the training data and the attributes of each class are well established. The algorithms were run on several data sets, observing that the quality of the obtained clusters is dependent on the number of clusters specified.

By email View Pdf

You have full access to the following eBook

Interdisciplinary Research in Engineering: Steps towards Breakthrough Innovation for Sustainable Development

Read eBook

Info:

Periodical:

Advanced Engineering Forum (Volumes 8-9)

Pages:

508-515

DOI:

https://doi.org/10.4028/www.scientific.net/AEF.8-9.508

Citation:

Cite this paper

Online since:

June 2013

Authors:

Raul Malutan, Pedro Gómez Vilda, Monica Borda

Keywords:

Classification, Clustering, Gene Shaving, Microarray, Supervised Methods

Export:

RIS, BibTeX

Permissions:

Creative Commons CC BY 4.0

Citation:

References

[1] S. González, L. Guerra , V. Robles, JM. Peña, F. Famili, CliDaPa: A new approach to combining clinical data with DNA microarrays, Intelligent Data Analysis Journal, 14(2) (2010) 207 – 223.

DOI: 10.3233/ida-2010-0417

Google Scholar

[2] T. Hastie et. al., Gene shaving as a method for identifying distinct sets of genes with similar expression patterns, Genome Biology, I(2) (2000) 1-21.

Google Scholar

[3] W. L. Martinez, A. R. Martinez, Exploratory Data Analysis with MATLAB, CRC Press LLC, (2005).

Google Scholar

[4] P. Sherrod, DTREG Predictive Modeling Software manual, (2001) 289-295.

Google Scholar

[5] G. Shakhnarovich, T. Darrell, P. Indyk (eds. ), Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing series), The MIT Press, (2006).

DOI: 10.7551/mitpress/4908.001.0001

Google Scholar

[6] D. Chowdary et al, Prognostic gene expression signatures can be measured in tissues collected in RNAlater preservative, J Mol Diagn, 8(1) (2006) 31 – 39.

DOI: 10.2353/jmoldx.2006.050056

Google Scholar

[7] S. Armstrong et al, MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia, Nature Genetics, 30 (2001) 41 – 47.

DOI: 10.1038/ng765

Google Scholar

[8] R. Malutan, B. Belean, P.G. Vilda, M. Borda, Two way clustering of microarray data using a hybrid approach, in Proc. of 34th Int. Conf. on Telecommunications and Signal Processing, (2011) 417 - 420.

DOI: 10.1109/tsp.2011.6043698

Google Scholar