scholarly journals Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer

2020 ◽  
Vol 18 ◽  
pp. 1509-1524 ◽  
Author(s):  
Jocelyn Gal ◽  
Caroline Bailleux ◽  
David Chardin ◽  
Thierry Pourcher ◽  
Julia Gilhodes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Machine Learning Methods

scholarly journals Identifying structural changes with unsupervised machine learning methods

2018 ◽  
Vol 98 (5) ◽  
Author(s):  
Nicholas Walker ◽  
Ka-Ming Tam ◽  
Brian Novak ◽  
M. Jarrell
Keyword(s):  
Machine Learning ◽  
Structural Changes ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Machine Learning Methods

Machine Learning for Chaotic Rayleigh Transitions

2021 ◽  
Author(s):  
Ben Tribelhorn ◽  
H. E. Dillon
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Natural Convection ◽  
Prandtl Number ◽  
Learning System ◽  
Lorenz Equations ◽  
Time Step ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Machine Learning Methods

Abstract This paper is a preliminary report on work done to explore the use of unsupervised machine learning methods to predict the onset of turbulent transitions in natural convection systems. The Lorenz system was chosen to test the machine learning methods due to the relative simplicity of the dynamic system. We developed a robust numerical solution to the Lorenz equations using a fourth order Runge-Kutta method with a time step of 0.001 seconds. We solved the Lorenz equations for a large range of Raleigh ratios from 1–1000 while keeping the geometry and Prandtl number constant. We calculated the spectral density, various descriptive statistics, and a cluster analysis using unsupervised machine learning. We examined the performance of the machine learning system for different Raleigh ratio ranges. We found that the automated cluster analysis aligns well with well known key transition regions of the convection system. We determined that considering smaller ranges of Raleigh ratios may improve the performance of the machine learning tools. We also identified possible additional behaviors not shown in z-axis bifurcation plots. This unsupervised learning approach can be leveraged on other systems where numerical analysis is computationally intractable or more difficult. The results are interesting and provide a foundation for expanding the study for Prandtl number and geometry variations. Future work will focus on applying the methods to more complex natural convection systems, including the development of new methods for Nusselt correlations.

Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination

2019 ◽  
Vol 177 (3) ◽  
pp. 591-601 ◽  
Author(s):  
Takeshi Murata ◽  
Takako Yanagisawa ◽  
Toshiaki Kurihara ◽  
Miku Kaneko ◽  
Sana Ota ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Alternative Decision
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