Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation

Wang, Yanzhe; Cai, Weiping; Gu, Liya; Ji, Xuefeng; Shen, Qiusheng

doi:10.1155/2021/4530180

Cited by 30 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to further study the arrangement of different structural elements, some important structural fragments or patterns have been proposed one after another [ 51 ]. The length of the longest segment (MaxSeg SE ) is defined as the following formula:

where MaxLen represents the function of the maximal segment length, and SEG SE is the composed of each segment of the structure element SE [ 52 ]. In order to reduce the length effect, a normalized length of the longest segment (NMaxSeg SE ) is defined

where N is the sequence length.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

Zhang

Wang

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Cervical cancer is one of the main causes of cancer death all over the world. Most diseases such as cervical epithelial atypical hyperplasia and invasive cervical cancer are closely related to the continuous infection of high-risk types of human papillomavirus. Therefore, the high-risk types of human papillomavirus are the key to the prevention and treatment of cervical cancer. With the accumulation of high-throughput and clinical data, the use of systematic and quantitative methods for mathematical modeling and computational prediction has become more and more important. This paper summarizes the mathematical models and prediction methods of the risk types of human papillomavirus, especially around the key steps such as feature extraction, feature selection, and prediction algorithms. We summarized and discussed the advantages and disadvantages of existing algorithms, which provides a theoretical basis for follow-up research.

show abstract

where N is the sequence length.…”

Section: Methodsmentioning

confidence: 99%

“…where MaxLen represents the function of the maximal segment length, and SEG SE is the composed of each segment of the structure element SE [52]. In order to reduce the length effect, a normalized length of the longest segment ðNMaxSeg SE Þ is defined…”

Section: Evolutionary Profilementioning

confidence: 99%

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

Zhang

Wang

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Although several bioinformatics studies have been conducted on AF to identify key mediators and therapeutic targets, most remained at the speculative level without verification, 19 with single-dimensional verification, 20 or included only one dataset. 21 In addition, none of these studies addressed the correlation between mitophagy and AF.…”

Section: Introductionmentioning

confidence: 99%

Establishment of Risk Model and Analysis of Immunoinfiltration Based on Mitophagy-Related Associated Genes in Atrial Fibrillation

Sang¹,

Wang²,

Yan³

et al. 2023

JIR

View full text Add to dashboard Cite

Objective Atrial fibrillation (AF) is a common tachyarrhythmia whose pathogenesis remains elusive. In the present study, we aimed to investigate the pathological mechanism of mitophagy and immunoinfiltration in AF. Methods First, we identified differentially expressed mitophagy-related genes (DEMRGs) based on the GSE79768 and GSE115574 datasets, subjecting them to functional enrichment analysis. STRING, TRRUST, miRNet, miRwalk, and Cytoscape were used to explore the potential regulatory roles of downstream signaling pathways. Subsequently, the random forest method was used to construct the AF risk model, and the DEMRGs most correlated with AF risk were determined by combining the Gini index. ssGSEA algorithm, NMF algorithm, and unsupervised clustering were used to subdivide AF molecular types. We then studied the characteristics of mitophagy- and immune infiltration-related genes in AF. Ultimately, we detected the expression of key genes in canine atrial tissues and HL-1 cells by immunofluorescence and Western blot. Results Mitophagy and immune infiltration were significantly enriched and activated in AF samples. Thirty-seven DEMRGs were screened, of which MAPK1, VDAC1, MAPK14, and MTERF3 were most associated with AF risk. The risk model based on these could identify patients at a high risk of AF. The infiltration of immunocells such as mast cells and neutrophils was significantly different among AF types. Finally, expression verification indicated that the expression trend of four key genes in canine atrial muscle tissue and HL-1 cells was consistent. Conclusion We found that mitophagy may participate in AF progression through immune activation. In addition, the AF risk prediction model composed of VDAC1, MAPK1, MAPK14, and MTERF3 has a good AF prediction performance, which provides new ideas for the study of AF pathogenesis and potential therapeutic targets.

show abstract

Risk factors based vessel‐specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry

Park

Lee

Hong

et al. 2023

Clinical Cardiology

View full text Add to dashboard Cite

Background and Hypothesis The recently introduced Bayesian quantile regression (BQR) machine‐learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel‐specific manner. Methods From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model. Results The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%–50% and 5.6%–7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High‐density lipoprotein cholesterol showed a dynamic association along DS change in the per‐patient analysis. Conclusions This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline‐grade CAD and its progression.

show abstract

Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation

Cited by 30 publications

References 23 publications

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

Establishment of Risk Model and Analysis of Immunoinfiltration Based on Mitophagy-Related Associated Genes in Atrial Fibrillation

Risk factors based vessel‐specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry

Contact Info

Product

Resources

About