Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest

2017 ◽  
Vol 9 (2) ◽  
pp. 14-30 ◽  
Author(s):  
Kazutaka Nishiwaki ◽  
Katsutoshi Kanamori ◽  
Hayato Ohwada
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest ◽  
Microarray Data ◽  
Gene Selection ◽  
Microarray Gene Expression Data ◽  
Gene Selection Method ◽  
Microarray Gene Expression ◽  
Learning Technique ◽  
Disease Related Genes

A significant amount of microarray gene expression data is available on the Internet, and researchers are allowed to analyze such data freely. However, microarray data includes thousands of genes, and analysis using conventional techniques is too difficult. Therefore, selecting informative gene(s) from high-dimensional data is very important. In this study, the authors propose a gene selection method using random forest as a machine learning technique. They applied this method to microarray data on Alzheimer's disease and conducted an experiment to rank genes. The authors' results indicated some genes that have been investigated for their relevance to Alzheimer's disease, proving that their proposed cognitive method was successful in finding disease-related genes using microarray data.

Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest

2020 ◽  
pp. 1391-1404
Author(s):  
Kazutaka Nishiwaki ◽  
Katsutoshi Kanamori ◽  
Hayato Ohwada
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest ◽  
Microarray Data ◽  
Gene Selection ◽  
Microarray Gene Expression Data ◽  
High Dimensional ◽  
Microarray Gene Expression ◽  
Disease Related Genes ◽  
Microarray Gene

A significant amount of microarray gene expression data is available on the Internet, and researchers are allowed to analyze such data freely. However, microarray data includes thousands of genes, and analysis using conventional techniques is too difficult. Therefore, selecting informative gene(s) from high-dimensional data is very important. In this study, the authors propose a gene selection method using random forest as a machine learning technique. They applied this method to microarray data on Alzheimer's disease and conducted an experiment to rank genes. The authors' results indicated some genes that have been investigated for their relevance to Alzheimer's disease, proving that their proposed cognitive method was successful in finding disease-related genes using microarray data.

APPLICATIONS OF SUPPORT VECTOR MACHINES TO CANCER CLASSIFICATION WITH MICROARRAY DATA

2005 ◽  
Vol 15 (06) ◽  
pp. 475-484 ◽  
Author(s):  
FENG CHU ◽  
LIPO WANG
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Microarray Gene Expression Data ◽  
Cancer Classification ◽  
Support Vector ◽  
Microarray Gene Expression ◽  
Fisher Ratio ◽  
Data Dimensionality Reduction ◽  
Vector Machines ◽  
Reduction Methods

Microarray gene expression data usually have a large number of dimensions, e.g., over ten thousand genes, and a small number of samples, e.g., a few tens of patients. In this paper, we use the support vector machine (SVM) for cancer classification with microarray data. Dimensionality reduction methods, such as principal components analysis (PCA), class-separability measure, Fisher ratio, and t-test, are used for gene selection. A voting scheme is then employed to do multi-group classification by k(k - 1) binary SVMs. We are able to obtain the same classification accuracy but with much fewer features compared to other published results.

scholarly journals Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method

2019 ◽  
Vol 9 ◽  
Author(s):  
Yang Hu ◽  
Tianyi Zhao ◽  
Tianyi Zang ◽  
Ying Zhang ◽  
Liang Cheng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Data Integration ◽  
Integration Method ◽  
Disease Related Genes

scholarly journals Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness

2014 ◽  
Vol 6 ◽  
pp. 115-125 ◽  
Author(s):  
A.V. Lebedev ◽  
E. Westman ◽  
G.J.P. Van Westen ◽  
M.G. Kramberger ◽  
A. Lundervold ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest

scholarly journals k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

2019 ◽  
Vol 10 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Changrui Liao ◽  
Gin-Den Chen ◽  
Chi-Chang Chang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest ◽  
Protein Identification ◽  
Disease Protein ◽  
N Gram

scholarly journals Recruitment and Retention of Participant and Study Partner Dyads in Two Multinational Alzheimer’s Disease Registration Trials

2020 ◽  
Author(s):  
Olivia M Bernstein ◽  
Joshua D. Grill ◽  
Daniel L. Gillen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Forest ◽  
Random Forest Model ◽  
P Value ◽  
Confounding Factors ◽  
Forest Model ◽  
Partner Type ◽  
Study Partner ◽  
The Relationship

Abstract Background: Early study exit is detrimental to statistical power and increases the risk for bias in Alzheimer’s disease clinical trials. Previous analyses in early phase academic trials demonstrated associations between rates of trial incompletion and participants’ study partner type, with participants enrolling with non-spouse study partners being at greater risk.Methods: We conducted secondary analyses of two multinational phase III trials of semagacestat, an oral gamma secretase inhibitor, for mild-to-moderate AD dementia. Cox’s proportional hazards regression model was used to estimate the relationship between study partner type and the risk of early exit from the trial after adjustment for a priori identified potential confounding factors. Additionally, we used a random forest model to identify top predictors of dropout.Results: Among participants with spousal, adult child, and other study partners, respectively, 35%, 38%, and 36% dropped out or died prior to protocol-defined study completion, respectively. In unadjusted models, the risk of trial incompletion differed by study partner type (unadjusted p-value=0.027 for test of differences by partner type), but in models adjusting for potential confounding factors the differences were not statistically significant (p-value=0.928). In exploratory modeling, participant age was identified as the primary characteristic to explain the relationship between study partner type and the risk of failing to complete the trial. Participant age was also the strongest predictor of trial incompletion in the random forest model.Conclusions: After adjustment for age, no qualitative differences in the risk of incompletion were observed when comparing participants with different study partner types in these trials. Differences between our findings and the findings of previous studies may be explained by differences in trial phase, size, geographic regions, or the composition of academic and non-academic sites.

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