CRS4 Bioinformatics Laboratory, Polaris, Pula (CA), Italy.
Gene. 2013 Nov 10;530(2):257-65. doi: 10.1016/j.gene.2013.07.020. Epub 2013 Aug 6.
Translational cancer genomics research aims to ensure that experimental knowledge is subject to computational analysis, and integrated with a variety of records from omics and clinical sources. The data retrieval from such sources is not trivial, due to their redundancy and heterogeneity, and the presence of false evidence. In silico marker identification, therefore, remains a complex task that is mainly motivated by the impact that target identification from the elucidation of gene co-expression dynamics and regulation mechanisms, combined with the discovery of genotype-phenotype associations, may have for clinical validation. Based on the reuse of publicly available gene expression data, our aim is to propose cancer marker classification by integrating the prediction power of multiple annotation sources. In particular, with reference to the functional annotation for colorectal markers, we indicate a classification of markers into diagnostic and prognostic classes combined with susceptibility and risk factors.
转化癌症基因组学研究旨在确保实验知识受到计算分析的约束，并与来自组学和临床来源的各种记录进行整合。由于这些来源的数据冗余和异质性，以及虚假证据的存在，从这些来源中检索数据并非易事。因此，在计算机上识别标记仍然是一项复杂的任务，主要是因为从阐明基因共表达动力学和调控机制，以及发现基因型-表型关联中确定目标，可能对临床验证产生影响。基于对公开可用基因表达数据的重复使用，我们的目标是通过整合多个注释源的预测能力来提出癌症标记物分类。特别是，参考结直肠癌标记物的功能注释，我们将标记物分为诊断和预后类，并结合易感性和风险因素。
