Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types
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...
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| Format: | Book Section |
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Springer
2020
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| Online Access: | http://eprints.sunway.edu.my/1436/ |
| _version_ | 1848802057799073792 |
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| author | Pawar, Shrikant Stanam, Aditya Lahiri, Chandrajit * |
| author2 | Rojas, I. |
| author_facet | Rojas, I. Pawar, Shrikant Stanam, Aditya Lahiri, Chandrajit * |
| author_sort | Pawar, Shrikant |
| building | SU Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T21:17:18Z |
| format | Book Section |
| id | sunway-1436 |
| institution | Sunway University |
| institution_category | Local University |
| last_indexed | 2025-11-14T21:17:18Z |
| publishDate | 2020 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | sunway-14362020-10-01T06:57:09Z http://eprints.sunway.edu.my/1436/ Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types Pawar, Shrikant Stanam, Aditya Lahiri, Chandrajit * QH301 Biology 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. Springer Rojas, I. Valenzuela, O. Rojas, F. Herrera, L. Ortuno, F. 2020 Book Section PeerReviewed 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 http://doi.org/10.1007/978-3-030-45385-5_52 doi:10.1007/978-3-030-45385-5_52 |
| spellingShingle | QH301 Biology Pawar, Shrikant Stanam, Aditya Lahiri, Chandrajit * Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types |
| title | Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types |
| title_full | Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types |
| title_fullStr | Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types |
| title_full_unstemmed | Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types |
| title_short | Clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types |
| title_sort | clustering reveals common check-point and growth factor receptor genes expressed in six different cancer types |
| topic | QH301 Biology |
| url | http://eprints.sunway.edu.my/1436/ http://eprints.sunway.edu.my/1436/ http://eprints.sunway.edu.my/1436/ |