Identifying differential miR and gene consensus patterns in peripheral blood of patients with cardiovascular diseases from literature data.
Agnė ŠatrauskienėRokas NavickasAleksandras LaucevičiusHeinrich J HuberPublished in: BMC cardiovascular disorders (2017)
We conclude that our analysis using gene and miR scores allows the extraction of meaningful markers and the elucidation of differential pathological functions between cardiac diseases and provides a novel approach for literature screening for miR and gene consensus patterns. The analysis is easy to use and extendable upon further emergent literature as we provide an Excel sheet for this analysis to the community.
Keyphrases
- cell proliferation
- long non coding rna
- systematic review
- peripheral blood
- long noncoding rna
- genome wide
- copy number
- cardiovascular disease
- healthcare
- genome wide identification
- left ventricular
- gene expression
- machine learning
- heart failure
- metabolic syndrome
- transcription factor
- cardiovascular events
- atrial fibrillation
- deep learning