Loading...
|
Please use this identifier to cite or link to this item:
http://ir.nhri.org.tw/handle/3990099045/13678
|
| Title: | Artificial intelligence-assisted identification of genetic factors predisposing high-risk individuals to asymptomatic heart failure |
| Authors: | Yang, NI;Yeh, CH;Tsai, TH;Chou, YJ;Hsu, PW;Li, CH;Chan, YH;Kuo, LT;Mao, CT;Shyu, YC;Hung, MJ;Lai, CC;Sytwu, HK;Tsai, TF |
| Contributors: | Institute of Molecular and Genomic Medicine;National Institute of Infectious Diseases and Vaccinology |
| Abstract: | Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual's quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseudogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF. |
| Date: | 2021-09-15 |
| Relation: | Cells. 2021 Sep 15;10(9):Article number 2430. |
| Link to: | http://dx.doi.org/10.3390/cells10092430 |
| JIF/Ranking 2023: | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2073-4409&DestApp=IC2JCR |
| Cited Times(WOS): | https://www.webofscience.com/wos/woscc/full-record/WOS:000699164100001 |
| Cited Times(Scopus): | https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85115889196 |
| Appears in Collections: | [徐唯哲] 期刊論文
[司徒惠康] 期刊論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| PUB34572079.pdf | | 3093Kb | Adobe PDF | 223 | View/Open |
|
All items in NHRI are protected by copyright, with all rights reserved.
|
