Pawar, Shrikant and Stanam, Aditya and Lahiri, Chandrajit * (2020) Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types. In: Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science, vol 12108. Springer, Cham, pp. 581-589. ISBN 978-3-030-45385-5
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Cancer diagnosis and prognosis has been significantly impacted by understandings of gene expression data analysis. Several groups have utilized supervised and unsupervised machine learning tools for classification and predictions on gene expression data sets. Clustering, principal component analysis, regression are some important and promising tools for analyzing gene expression data. The complex and multi-dimensions of this data with limited samples makes it challenging to understand common patterns. Several features of high dimensional data contributing to a cluster generated by a finite mixture of underlying probability distributions can be implemented with a model-based clustering method. While some groups have shown that projective clustering and ensemble techniques can be effective to combat these challenges, we have employed clustering on 6 different cancer types to address the problem of multi-dimensionality and extracting common gene expression patterns. Our analysis has provided an expression pattern of 42 genes common throughout all cancer types with most of the genes involved in important check-point and growth factor receptor functions associated with cancer pathophysiology.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | cancer diagnosis; gene expression; clustering analysis |
| Subjects: | Q Science > QH Natural history > QH301 Biology |
| Divisions: | Others > Non Sunway Academics Sunway University > School of Engineering and Technology [formerly School of Science and Technology until 2020] > Dept. Biological Sciences moved to SMLS wef 2021 |
| Depositing User: | Dr Janaki Sinnasamy |
| Related URLs: | |
| Date Deposited: | 30 Sep 2020 08:01 |
| Last Modified: | 01 Oct 2020 06:57 |
| URI: | http://eprints.sunway.edu.my/id/eprint/1436 |
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