Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases.

ABSTRACT:

Objective

It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery.

Methods

We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 p

SUBMITTER: Veroneze R 

PROVIDER: S-EPMC7531780 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature