Cross-Platform Pathway Activity Transformation and Classification of Microarray Data

2015 ◽  
pp. 139-148 ◽  
Author(s):  
Worrawat Engchuan ◽  
Asawin Meechai ◽  
Sissades Tongsima ◽  
Jonathan H. Chan
Keyword(s):  
Microarray Data ◽  
Pathway Activity ◽  
Cross Platform

Handling batch effects on cross-platform classification of microarray data

2016 ◽  
Vol 8 (1) ◽  
pp. 59 ◽  
Author(s):  
Worrawat Engchuan ◽  
Asawin Meechai ◽  
Sissades Tongsima ◽  
Jonathan H. Chan
Keyword(s):  
Microarray Data ◽  
Batch Effects ◽  
Cross Platform

Gene Selection and Classification of Human Lymphoma from Microarray Data

2005 ◽  
pp. 379-390
Author(s):  
Joarder Kamruzzaman ◽  
Suryani Lim ◽  
Iqbal Gondal ◽  
Rezaul Begg
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Human Lymphoma

An entropy-based classification of breast cancerous genes using microarray data

2018 ◽  
Vol 32 (7) ◽  
pp. 2397-2404
Author(s):  
Mausami Mondal ◽  
Rahul Semwal ◽  
Utkarsh Raj ◽  
Imlimaong Aier ◽  
Pritish Kumar Varadwaj
Keyword(s):  
Microarray Data

scholarly journals An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Nicoletta Dessì ◽  
Barbara Pes
Keyword(s):  
Microarray Data ◽  
Selection Criteria ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Research Area ◽  
Svm Classifier ◽  
Ranking Methods ◽  
Wrapper Approach ◽  
Relevant Gene

The classification of cancers from gene expression profiles is a challenging research area in bioinformatics since the high dimensionality of microarray data results in irrelevant and redundant information that affects the performance of classification. This paper proposes using an evolutionary algorithm to select relevant gene subsets in order to further use them for the classification task. This is achieved by combining valuable results from different feature ranking methods into feature pools whose dimensionality is reduced by a wrapper approach involving a genetic algorithm and SVM classifier. Specifically, the GA explores the space defined by each feature pool looking for solutions that balance the size of the feature subsets and their classification accuracy. Experiments demonstrate that the proposed method provide good results in comparison to different state of art methods for the classification of microarray data.

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