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Identifying Clinical and Genomic Features Associated With Chronic Kidney Disease.

Authors :
Moreno MM
Bain TC
Moreno MS
Carroll KC
Cunningham ER
Ashton Z
Poteau R
Subasi E
Lipkowitz M
Subasi MM
Source :
Frontiers in big data [Front Big Data] 2021 Jan 14; Vol. 3, pp. 528828. Date of Electronic Publication: 2021 Jan 14 (Print Publication: 2020).
Publication Year :
2021

Abstract

We apply a pattern-based classification method to identify clinical and genomic features associated with the progression of Chronic Kidney disease (CKD). We analyze the African-American Study of Chronic Kidney disease with Hypertension dataset and construct a decision-tree classification model, consisting 15 combinatorial patterns of clinical features and single nucleotide polymorphisms (SNPs), seven of which are associated with slow progression and eight with rapid progression of renal disease among African-American Study of Chronic Kidney patients. We identify four clinical features and two SNPs that can accurately predict CKD progression. Clinical and genomic features identified in our experiments may be used in a future study to develop new therapeutic interventions for CKD patients.<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2021 Moreno, Bain, Moreno, Carroll, Cunningham, Ashton, Poteau, Subasi, Lipkowitz and Subasi.)

Details

Language :
English
ISSN :
2624-909X
Volume :
3
Database :
MEDLINE
Journal :
Frontiers in big data
Publication Type :
Academic Journal
Accession number :
33693411
Full Text :
https://doi.org/10.3389/fdata.2020.528828