文章：

癌症基因功能的计算预测

Computational prediction of cancer-gene function

原文发布日期：2006-12-14

DOI: 10.1038/nrc2036

类型: Review Article

开放获取: 否

要点：

1. Many cancer genes remain functionally uncharacterized. Experimental methods to characterize their functions are inefficient, time consuming and expensive.
2. The increasing availability of diverse molecular profiles and functional-interaction data make the prediction of cancer-gene functions possible.
3. New computational prediction methods now enable the automated assessment of cancer-gene function.
4. The main difficulties are how to simultaneously integrate different high-throughput data sources and dependably assign multiple functions to a cancer gene.
5. Trustworthy gene annotations are crucial to achieving the best possible functional predictions for newly discovered or uncharacterized cancer genes.
6. Rigorous evaluation of the accuracy of functional predictions generated by computational methods is vital for formulating biologically relevant hypotheses to direct further rounds of experimentation.

要点翻译：

1. 许多癌症基因在功能上仍未得到明确表征。实验方法在表征其功能方面效率低下、耗时且昂贵。
2. 日益多样化的分子谱和功能相互作用数据的可获得性使得预测癌症基因功能成为可能。
3. 新的计算预测方法如今能够实现对癌症基因功能的自动评估。
4. 主要困难在于如何同时整合不同的高通量数据源，并可靠地为癌症基因分配多种功能。
5. 可靠的基因注释对于为新发现或未表征的癌症基因实现最佳功能预测至关重要。
6. 严格评估计算方法生成的功能预测准确性对于制定具有生物学相关性的假设以指导后续实验至关重要。

英文摘要：

Most cancer genes remain functionally uncharacterized in the physiological context of disease development. High-throughput molecular profiling and interaction studies are increasingly being used to identify clusters of functionally linked gene products related to neoplastic cell processes. However, in vivo determination of cancer-gene function is laborious and inefficient, so accurately predicting cancer-gene function is a significant challenge for oncologists and computational biologists alike. How can modern computational and statistical methods be used to reliably deduce the function(s) of poorly characterized cancer genes from the newly available genomic and proteomic datasets? We explore plausible solutions to this important challenge.

摘要翻译： 

大多数癌症基因在疾病发展的生理环境中仍未被功能表征。高通量分子分析和相互作用研究正越来越多地被用于识别与肿瘤细胞过程相关的功能关联基因产物簇。然而，在活体内确定癌症基因的功能既耗时又低效，因此准确预测癌症基因的功能对肿瘤学家和计算生物学家来说都是一个重大挑战。如何利用现代计算和统计方法，从最新可获得的基因组和蛋白质组数据集中可靠地推断出特征不明确的癌症基因的功能？我们探讨了这一重要挑战的可行解决方案。

原文链接：

Computational prediction of cancer-gene function