Mining Potential Information for Multiclass Microarray Data Using Centroid-Based Dimension Reduction

2015 ◽  
Author(s):  
Shun Guo ◽  
Donghui Guo
Keyword(s):  
Dimension Reduction ◽  
Microarray Data

LTSA Algorithm for Dimension Reduction of Microarray Data

2013 ◽  
Vol 645 ◽  
pp. 192-195 ◽  
Author(s):  
Xiao Zhou Chen
Keyword(s):  
Dimensionality Reduction ◽  
Dimension Reduction ◽  
Manifold Learning ◽  
Microarray Data ◽  
Learning Algorithm ◽  
Medical Applications ◽  
Data Sets ◽  
Learning Method ◽  
Data Dimensionality Reduction ◽  
Low Dimensional

Dimension reduction is an important issue to understand microarray data. In this study, we proposed a efficient approach for dimensionality reduction of microarray data. Our method allows to apply the manifold learning algorithm to analyses dimensionality reduction of microarray data. The intra-/inter-category distances were used as the criteria to quantitatively evaluate the effects of data dimensionality reduction. Colon cancer and leukaemia gene expression datasets are selected for our investigation. When the neighborhood parameter was effectivly set, all the intrinsic dimension numbers of data sets were low. Therefore, manifold learning is used to study microarray data in the low-dimensional projection space. Our results indicate that Manifold learning method possesses better effects than the linear methods in analysis of microarray data, which is suitable for clinical diagnosis and other medical applications.

scholarly journals Deteksi Kanker Berdasarkan Klasifikasi Microarray Data

2018 ◽  
Vol 2 (4) ◽  
pp. 181
Author(s):  
Adiwijaya Adiwijaya
Keyword(s):  
Gene Expression ◽  
Dimension Reduction ◽  
Dna Microarray ◽  
Microarray Data ◽  
Expression Patterns ◽  
Back Propagation ◽  
Good Method ◽  
Accurate Analysis ◽  
Training Time ◽  
Cancer Data

Cancer is one of the diseases that can cause human death in the world and become the biggest cause of death after heart disease. Therefore we need a DNA microarray technology which is used to examine how gene expression patterns change under different conditions, so that the technology is able to detect a person with cancer or not with accurate analysis. The size of the dimension in the microarray data can affect the gene expression analysis that is used to find informative genes, for that we need a good method of dimension reduction and classification so that it can get the best results and accuracy. Many techniques can be applied in DNA microarray, one of them is BPNN Back Propagation Neural Network as a classification and PCA as dimension reduction, where both have been tested in several previous studies. By applying BPNN and PCA on several types of cancer data, it was found that BPNN and PCA get more than 80% accuracy results with training time 0-4 seconds.

scholarly journals Dimension reduction methods for microarray data: a review

2017 ◽  
Vol 4 (1) ◽  
pp. 179-197 ◽  
Author(s):  
Rabia Aziz ◽  
◽  
C.K. Verma ◽  
Namita Srivastava
Keyword(s):  
Dimension Reduction ◽  
Microarray Data ◽  
Reduction Methods
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