scholarly journals A New Maximum-Relevance Criterion for Significant Gene Selection

2006 ◽  
pp. 71-80 ◽  
Author(s):  
Young Bun Kim ◽  
Jean Gao ◽  
Pawel Michalak
Keyword(s):  
Gene Selection ◽  
Relevance Criterion ◽  
Significant Gene

A Review of Gene Selection Tools in Classifying Cancer Microarray Data

2017 ◽  
Vol 12 (3) ◽  
pp. 202-212 ◽  
Author(s):  
Tham W. Shi ◽  
Wong S. Kah ◽  
Mohd S. Mohamad ◽  
Kohbalan Moorthy ◽  
Safaai Deris ◽  
...  
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Selection Tools

Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data

2021 ◽  
pp. 104276
Author(s):  
Arnab Kumar Maity ◽  
Sang Chan Lee ◽  
Linhan Hu ◽  
Deborah Bell-pederson ◽  
Bani K. Mallick ◽  
...  
Keyword(s):  
Gene Selection ◽  
Circadian Gene ◽  
Time To Event ◽  
Rnaseq Data ◽  
Selection For

scholarly journals Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020 ◽  
Vol 21 (S18) ◽  
Author(s):  
Sudipta Acharya ◽  
Laizhong Cui ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Marker Gene ◽  
Biological Data ◽  
Protein Interaction Data ◽  
Marker Genes ◽  
Data Sets ◽  
Gene Markers ◽  
Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

scholarly journals An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3627
Author(s):  
Bo Jin ◽  
Chunling Fu ◽  
Yong Jin ◽  
Wei Yang ◽  
Shengbin Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Gene Selection ◽  
Dimensional Space ◽  
Original Data ◽  
Global Structure ◽  
Biological Data ◽  
Special Treatment ◽  
Selection Scheme ◽  
Unsupervised Feature Selection

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.

scholarly journals A framework for automated gene selection in genomic applications

2021 ◽  
Author(s):  
L. Lazo de la Vega ◽  
W. Yu ◽  
K. Machini ◽  
C. A. Austin-Tse ◽  
L. Hao ◽  
...  
Keyword(s):  
Gene Selection
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