Title

Iterative local Gaussian clustering for expressed genes identification linked to malignancy of human colorectal carcinoma

Authors

Ito Wasito^1, *, Siti Zaiton M Hashim¹ and Sri Sukmaningrum²

Affiliation

¹Department of Software Engineering, Faculty of Computer Science and Information Systems, University Technology Malaysia,  Skudai, Johor Bahru, Malaysia; ²Faculty of Biology, University of  Jenderal Soedirman Purwokerto, Central Java, Indonesia

Email

ito@gmx.co.uk; * Corresponding author

Article Type

Prediction Model

Date

received December 14, 2007; accepted December 28, 2007; published online December 30, 2007

Gene expression profiling plays an important role in the identification of biological and clinical properties of human solid tumors such as colorectal carcinoma.  Profiling is required to reveal underlying molecular features for diagnostic and therapeutic purposes.  A non-parametric density-estimation-based approach called iterative local Gaussian clustering (ILGC), was used to identify clusters of expressed genes.   We used experimental data from a previous study by Muro and others consisting of 1,536 genes in 100 colorectal cancer and 11 normal tissues.  In this dataset, the ILGC finds three clusters, two large and one small gene clusters, similar to their results which used Gaussian mixture clustering. The correlation of each cluster of genes and clinical properties of malignancy of human colorectal cancer was analysed for the existence of tumor or normal, the existence of distant metastasis and the existence of lymph node metastasis.

Citation

Wasito, et al., Bioinformation 2(5): 175-181 (2007)

Edited by

O. Miotto, T. W. Tan & S. Ranganathan

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.