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Knowledge database assisted gene marker selection for chronic lymphocytic leukemia.

ABSTRACT: 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.

SUBMITTER: Xiang X

PROVIDER: S-EPMC6134680 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Knowledge database assisted gene marker selection for chronic lymphocytic leukemia.

Xiang Xixi X Wang Yu-Ping YP Cao Hongbao H Zhang Xi X

The Journal of international medical research 20180712 8

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) ...[more]

PMID: 29996709

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Project description:Background: Many tools used to analyze microarrays in different conditions have been described. However, the integration of the deregulated genes within coherent metabolic pathways is lacking. Currently no objective selection criterion, based on biological functions exists, to determine a threshold demonstrating that a gene is indeed differentially expressed. Methodology/Principal Findings: To improve transcriptomic analysis of microarrays, we propose a new statistical approach, which takes into account biological parameters. We present an iterative method to optimise the selection of differentially expressed gene in two experimental conditions. The stringency level of gene selection was associated simultaneously with the p-value of expression variation and the occurrence rate parameter, which is associated with the percentage of donors whose transcriptomic profile is similar. Our method intertwines stringency level settings, biological data and a knowledge database to highlight molecular interactions using networks and pathways. Analysis performed during iterations helped us select the optimal threshold required for the most pertinent selection of differently expressed genes. Conclusions/significance: We have applied this approach to the well documented mechanism of human macrophage response to lipopolysaccharide stimulation. For example, we thus verified that our method was able to determine with the highest degree of accuracy the best threshold for selecting genes, which are truly differentially expressed. Macrophages isolated from six heathy donnor was/or not stimulated. Paired data, i.e. LPS stimulated macrophages versus unstimulated macrophages from the same donor have been compared (eg, Donor1_LPS vs Donor1_NT; see processed data file linked below). The six comparaisons have been globaly analyse using two parameters, i.e. threshod and occurency, associated with a request of a database knowledge. Both parameters has been tune to define the best setting allowing to optimize the selection of differentially expressed genes

Dataset Information

Knowledge database assisted gene marker selection for chronic lymphocytic leukemia.

Publications

Knowledge database assisted gene marker selection for chronic lymphocytic leukemia.

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