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Finding associations among SNPS for prostate cancer using collaborative filtering

Published: 29 October 2012 Publication History

Abstract

Prostate cancer is the second leading cause of cancer related deaths among men. Because of the slow growing nature of prostate cancer, sometimes surgical treatment is not required for less aggressive cancers. Recent debates over prostate-specific antigen (PSA) screening have drawn new attention to prostate cancer. Genome-based screening can potentially help in assessing the risk of developing prostate cancer. Due to the complicated nature of prostate cancer, studying the entire genome is essential to find genomic traits. Due to the high cost of studying all Single Nucleotide Polymorphisms (SNPs), it is essential to find tag SNPs which can represent other SNPs. Earlier methods to find tag SNPs using associations between SNPs either use SNP's location information or are based on data of very few SNP markers in each sample. Our study is based on 2300 samples with 550,000 SNPs each. We have not used SNP location information or any predefined standard cut-offs to find tag SNPs. Our approach is based on using collaborative filtering methods to find pairwise associations among SNPs and thus list top-N tag SNPs. We have found 25 tag SNPs which have highest similarities to other SNPs. In addition we found 16 more SNPs which have high correlation with the known high risk SNPs that are associated with prostate cancer. We used some of these newly found SNPs with 5 different classification algorithms and observed some improvement in prostate cancer prediction accuracy over using the original known high risk SNPs.

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Cited By

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  • (2014)Soft Computing Based Epidemical Crisis PredictionIntelligent Methods for Cyber Warfare10.1007/978-3-319-08624-8_2(43-67)Online publication date: 4-Sep-2014
  • (2013)Epidemiological Data Analysis in TerraFly Geo-spatial CloudProceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2013.166(485-490)Online publication date: 4-Dec-2013

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cover image ACM Conferences
DTMBIO '12: Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
October 2012
92 pages
ISBN:9781450317160
DOI:10.1145/2390068
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2012

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Author Tags

  1. collaborative filtering
  2. prostate cancer
  3. snp
  4. snp association
  5. tag snps

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Cited By

View all
  • (2014)Soft Computing Based Epidemical Crisis PredictionIntelligent Methods for Cyber Warfare10.1007/978-3-319-08624-8_2(43-67)Online publication date: 4-Sep-2014
  • (2013)Epidemiological Data Analysis in TerraFly Geo-spatial CloudProceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2013.166(485-490)Online publication date: 4-Dec-2013

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