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. 2021 Jan 14:3:528828.
doi: 10.3389/fdata.2020.528828. eCollection 2020.

Identifying Clinical and Genomic Features Associated With Chronic Kidney Disease

M Megan Moreno  1 , Travaughn C Bain  1 , Melissa S Moreno  1 , Katherine C Carroll  1   2   3 , Emily R Cunningham  1   2   4 , Zoe Ashton  1 , Roby Poteau  1 , Ersoy Subasi  5 , Michael Lipkowitz  6 , Munevver Mine Subasi  1
Affiliations

Identifying Clinical and Genomic Features Associated With Chronic Kidney Disease

M Megan Moreno et al. Front Big Data. .

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.

Keywords: AASK; chronic kidney disease; classification; decision trees; genomic analysis.

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Conflict of interest statement

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.

Figures

FIGURE 1
FIGURE 1
AASK clinical composite events–all patients.
FIGURE 2
FIGURE 2
AASK clinical composite events–proteinuria.
FIGURE 3
FIGURE 3
AASK Patients stratified by GFR slope with degree of proteinuria superimposed.
FIGURE 4
FIGURE 4
Chronic GFR slope of AASK patients in the reduced data.
FIGURE 5
FIGURE 5
PCA plot of AASK patients in the reduced data: * Rapid Progressors and * Slow Progressors
FIGURE 6
FIGURE 6
C4.5 decision tree for AASK samples.
FIGURE 7
FIGURE 7
Heatmap of the C4.5 patterns for AASK samples.
FIGURE 8
FIGURE 8
Receiver operating curves (ROC).
See this image and copyright information in PMC

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