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Identifying Subtypes of Cancer Using Genomic Data by Applying Data Mining Techniques

Identifying Subtypes of Cancer Using Genomic Data by Applying Data Mining Techniques

Tejal Upadhyay, Samir Patel
Copyright: © 2019 |Volume: 8 |Issue: 3 |Pages: 10
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781522566342|DOI: 10.4018/IJNCR.2019070104
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MLA

Upadhyay, Tejal, and Samir Patel. "Identifying Subtypes of Cancer Using Genomic Data by Applying Data Mining Techniques." IJNCR vol.8, no.3 2019: pp.55-64. http://doi.org/10.4018/IJNCR.2019070104

APA

Upadhyay, T. & Patel, S. (2019). Identifying Subtypes of Cancer Using Genomic Data by Applying Data Mining Techniques. International Journal of Natural Computing Research (IJNCR), 8(3), 55-64. http://doi.org/10.4018/IJNCR.2019070104

Chicago

Upadhyay, Tejal, and Samir Patel. "Identifying Subtypes of Cancer Using Genomic Data by Applying Data Mining Techniques," International Journal of Natural Computing Research (IJNCR) 8, no.3: 55-64. http://doi.org/10.4018/IJNCR.2019070104

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Abstract

This article is about the study of genomics structures and identifying cancer types from it. It divides into six parts. The first part is about the introduction of cancer, types of cancers, how cancer arises, etc. The second part is about the genomic study and how cancer is related to that, which features are used for the study. The third part is about the software which the authors have used to study these genomic structures, which data sets are used, and what is the final output for this study. The fourth part shows the proposed algorithm for the study. The fifth part shows the data preprocessing and clustering. Different preprocessing and clustering algorithms are used. The sixth part shows the results and conclusion with a future scope. The genomics data which is used by this article is taken from the Cancer Genome Atlas data portal which is freely available. Some applied imputation techniques fill up for the missing values and important features are extracted. Different clustering algorithms are applied on genome dataset and results are generated.

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