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首页> 外文期刊>Journal of International Medical Research >Knowledge database assisted gene marker selection for chronic lymphocytic leukemia
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Knowledge database assisted gene marker selection for chronic lymphocytic leukemia

机译：知识数据库辅助基因标记选择慢性淋巴细胞性白血病

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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.
机译：目的探讨先前治愈的慢性淋巴细胞白血病（CLL）危险基因是否可用于选择CLL的基因标志物，以诊断和预测CLL。方法通过全面的CLL基因相关性数据分析，建立了CLL基因数据库（CLL_042017），其中选择了753个CLL目标基因。这些基因的表达值用于对四个CLL数据集进行病例对照分类，并使用基于稀疏表示的变量选择（SRVS）方法进行特征（基因）选择。将结果与通过使用基于方差分析（ANOVA）的基因选择方法获得的结果进行比较。结果对于四个数据集，SRVS从753个CLL目标基因中选择了一个基因子集，与随机选择的基因（100％，100％，93.94％，89.39％）相比，分类准确性显着提高。在分类准确性方面，SRVS方法优于ANOVA。结论从753个CLL基因中选择的基因标记可以大大提高CLL预测的准确性。 SRVS提供了一种有效的基因标记选择方法。

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