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Detection of Breast Cancer by the Identification of Circulating Tumor Cells Using Association Rule Mining

Detection of Breast Cancer by the Identification of Circulating Tumor Cells Using Association Rule Mining

Jananee S., Nedunchelian R.
Copyright: © 2016 |Volume: 6 |Issue: 1 |Pages: 9
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466691360|DOI: 10.4018/IJKDB.2016010102
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MLA

Jananee S., and Nedunchelian R. "Detection of Breast Cancer by the Identification of Circulating Tumor Cells Using Association Rule Mining." IJKDB vol.6, no.1 2016: pp.17-25. http://doi.org/10.4018/IJKDB.2016010102

APA

Jananee S. & Nedunchelian R. (2016). Detection of Breast Cancer by the Identification of Circulating Tumor Cells Using Association Rule Mining. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 6(1), 17-25. http://doi.org/10.4018/IJKDB.2016010102

Chicago

Jananee S., and Nedunchelian R. "Detection of Breast Cancer by the Identification of Circulating Tumor Cells Using Association Rule Mining," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 6, no.1: 17-25. http://doi.org/10.4018/IJKDB.2016010102

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Abstract

Circulating Tumor Cells (CTCs) are cells that have shed into the vasculate from the primary tumor and circulate into the blood stream. In this proposed work, the major genes causing the breast cancer is identified by the principle of Association Rule. The trained set and training set is made to upload on the data store. By associating each row of a training set to all the rows of the trained data is done and the report is generated. The Baum welch process is called for the estimation of actual probabilities and emission probabilities by calculating its log likelihood factor which gives the high Priority gene values that are responsible for the cause of cancer. Based on this cell category is splitted into three clusters such as carcinoma level, metastasis level and Kaposi sarcoma. On each cluster it finds the highest priority value in it and classifies into high, low and medium values. On extraction of these higher gene values yields the major responsible genes causing breast cancer. Finally, the obtained results are validated through hierarchical clustering.

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