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Objective To investigate whether previously curated chronic lymphocytic leukemia (CLL) risk genes could be leveraged in gene marker selection for the diagnosis and prediction of CLL. Methods A CLL genetic database (CLL_042017) was developed through a comprehensive CLL-gene relation data analysis, in which 753 CLL target genes were curated. Expression values for these genes were used for case-control classification of four CLL datasets, with a sparse representation-based variable selection (SRVS) approach employed for feature (gene) selection. Results were compared with outcomes obtained by using analysis of variance (ANOVA)-based gene selection approaches. Results For each of the four datasets, SRVS selected a subset of genes from the 753 CLL target genes, resulting in significantly higher classification accuracy, compared with randomly selected genes (100%, 100%, 93.94%, 89.39%). The SRVS method outperformed ANOVA in terms of classification accuracy. Conclusion Gene markers selected from the 753 CLL genes could enable significantly greater accuracy in the prediction of CLL. SRVS provides an effective method for gene marker selection.
Pubmed ID: 29996709
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Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
View all literature mentionsAmerican company incorporated that develops, manufactures and markets integrated systems for the analysis of genetic variation and biological function. Provides a line of products and services that serve the sequencing, genotyping and gene expression and proteomics markets. Its headquarters are located in San Diego, California.
View all literature mentionsFunctional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
View all literature mentions