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Privacy-preserving decision tree for epistasis detection

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journal contribution
posted on 05.02.2021, 05:49 by Qingfeng Chen, X Zhang, R Zhang
© 2019, The Author(s). The interaction between gene loci, namely epistasis, is a widespread biological genetic phenomenon. In genome-wide association studies(GWAS), epistasis detection of complex diseases is a major challenge. Although many approaches using statistics, machine learning, and information entropy were proposed for epistasis detection, the privacy preserving for single nucleotide polymorphism(SNP) data has been largely ignored. Thus, this paper proposes a novel two-stage approach. A fusion strategy assists in combining and sorting the SNPs importance scores obtained by the relief and mutual information, thereby obtaining a candidate set of SNPs. This avoids missing some SNPs with strong interaction. Furthermore, differentially private decision tree is applied to search for SNPs. This achieves the efficient epistasis detection of complex diseases on the basis of privacy preserving compared with heuristic methods. The recognition rate on simulation data set is more than 90%. Also, several susceptible loci including rs380390 and rs1329428 are found in the real data set for Age-related Macular Degeneration (AMD). This demonstrates that our method is promising in epistasis detection.

History

Publication Date

01/01/2019

Journal

Cybersecurity

Volume

2

Issue

1

Article Number

7

Pagination

12p. (p. 1-12)

Publisher

SpringerOpen

ISSN

2523-3246

Rights Statement

The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.

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