Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

2017 ◽  
Vol 482 ◽  
pp. 796-807 ◽  
Author(s):  
Syed Muhammad Saqlain Shah ◽  
Safeera Batool ◽  
Imran Khan ◽  
Muhammad Usman Ashraf ◽  
Syed Hussnain Abbas ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Heart Disease ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Diagnosis ◽  
Heart Disease Diagnosis ◽  
Probabilistic Principal Component Analysis

scholarly journals Effects of Collagen–Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4117
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Cell Lines ◽  
Time Course ◽  
Expression Profiles ◽  
Principal Component ◽  
Component Analysis ◽  
Altered Expression ◽  
Unsupervised Feature Extraction

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

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