首页> 外文期刊>Journal of Bioinformatics and Computational Biology >BRIEF INTRODUCTION TO SOME NEW RESULTS IN GENE EXPRESSION ANALYSIS, SYSTEMS BIOLOGY MODELING, MOTIF IDENTIFICATION, AND (NONCODING) RNA ANALYSIS
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BRIEF INTRODUCTION TO SOME NEW RESULTS IN GENE EXPRESSION ANALYSIS, SYSTEMS BIOLOGY MODELING, MOTIF IDENTIFICATION, AND (NONCODING) RNA ANALYSIS

机译:简要介绍基因表达分析,系统生物学建模,分子鉴定和(非编码)RNA分析的一些新结果

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This issue of the Journal of Bioinformatics and Computational Biology presents annumber of new approaches to several key problems in the analysis of data fromnbiological experiments. These approaches are briefly summarized below.nThe possibility of using gene expression profiling by microarrays for diagnosticnand prognostic purposes has generated much excitement and research for the last 10nyears.1,2 Nevertheless, a number of issues persist such as how to identify genes thatnare meaningful in explaining the difference in response and phenotypes, how to rectifynbatch effects, and how to deal with censoring when modeling survival outcome.nIn this issue, Zhao and Wang3 apply correlation principal component regressionnto handle censoring in survival data under a semi-parametric additive risk modelnto identify genes which are significantly related to patient survival of a disease.nTheir technique is shown to be effective on several datasets and is comparativelynconvenient to implement.
机译:本期《生物信息学与计算生物学杂志》为解决生物学实验数据分析中的几个关键问题提供了许多新方法。这些方法在下面简要概述。n在过去的10年中,使用微阵列基因表达谱进行诊断和预后的可能性引起了极大的兴奋和研究。1,2然而,仍然存在许多问题,例如如何鉴定对人类有意义的基因。解释了响应和表型的差异,如何纠正批处理效应以及在建模生存结果时如何处理审查。n在本期中,Zhao和Wang3应用相关主成分回归n在半参数加性风险模型下处理生存数据中的审查。这些基因与患者的疾病存活率显着相关。n他们的技术在多个数据集上均显示有效,并且实施起来相对不便。

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