Integrative computational biology for cancer research.

Integrative computational biology for cancer research.
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DOI:
10.1007/s00439-011-0983-z
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发表时间:
2011-10
期刊:
影响因子:
5.3
通讯作者:
Jurisica I
Jurisica I
中科院分区:
生物学2区
文献类型:
--
作者:
Fortney K;Jurisica I

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在过去的二十年里,高通量(HTP)技术,如微阵列和质谱仪,从根本上改变了临床癌症研究。他们揭示了癌症亚型、转移、药物敏感性和耐药性的新分子标记。其中一些已被翻译成临床工具,用于早期疾病诊断、预后以及个体化治疗和反应监测。尽管取得了这些成功,但仍然存在许多挑战:HTP平台经常噪音大,存在假阳性和假阴性;最佳分析和成功验证需要复杂的工作流程;大量数据正在快速积累。在这里,我们讨论这些挑战,并展示综合计算生物学如何通过创建新的软件工具、分析方法和数据标准来帮助减少这些挑战。本文的在线版本(doi:10.1007/s00439-0110983-z)包含补充材料,授权用户可以使用。
Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards. The online version of this article (doi:10.1007/s00439-011-0983-z) contains supplementary material, which is available to authorized users.
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