A unified computational model for revealing and predicting subtle subtypes of cancers.
A unified computational model for revealing and predicting subtle subtypes of cancers.
复制标题
用于揭示和预测癌症的微妙亚型的统一计算模型
DOI：
10.1186/1471-2105-13-70
复制
发表时间：
2012-05-01
影响因子：
3
通讯作者：
Zhang XS
中科院分区：
文献类型：
--
作者：
Ren X;Wang Y;Wang J;Zhang XS
背景基因表达谱技术已逐渐成为临床应用的标准工具。例如，基因表达数据已被分析以揭示新的疾病亚型（类别发现）并将特定样本分配给明确定义的类别（类别预测）。在过去的十年中，已经提出了许多有效的方法，为个人的应用。然而，仍然迫切需要一个统一的框架，可以揭示样本之间的复杂关系。ResultsWe提出了一种新的凸优化模型进行类发现和类预测在一个统一的框架。设计了一种有效的聚类算法，并开发了聚类和分类优化工具OTCC（Optimization Tool for Clustering and Classification）。在模拟数据集上的比较表明，我们的方法优于现有的方法。然后，我们将OTCC应用于急性白血病和乳腺癌数据集。结果表明，我们的方法不仅可以揭示这些癌症基因表达数据背后的细微结构，而且可以准确地预测未知癌症样本的类别标签。因此，我们的方法持有的承诺，以确定新的癌症亚型，提高diagnosis.ConclusionsWe提出了一个统一的计算框架类发现和类预测，以促进发现和预测的微妙亚型的癌症。我们的方法通常可以应用于多种类型的测量，例如，基因表达谱分析、蛋白质组学测量和最近的下一代测序，因为它只需要样本之间的相似性作为输入。
BackgroundGene expression profiling technologies have gradually become a community standard tool for clinical applications. For example, gene expression data has been analyzed to reveal novel disease subtypes (class discovery) and assign particular samples to well-defined classes (class prediction). In the past decade, many effective methods have been proposed for individual applications. However, there is still a pressing need for a unified framework that can reveal the complicated relationships between samples.ResultsWe propose a novel convex optimization model to perform class discovery and class prediction in a unified framework. An efficient algorithm is designed and software named OTCC (Optimization Tool for Clustering and Classification) is developed. Comparison in a simulated dataset shows that our method outperforms the existing methods. We then applied OTCC to acute leukemia and breast cancer datasets. The results demonstrate that our method not only can reveal the subtle structures underlying those cancer gene expression data but also can accurately predict the class labels of unknown cancer samples. Therefore, our method holds the promise to identify novel cancer subtypes and improve diagnosis.ConclusionsWe propose a unified computational framework for class discovery and class prediction to facilitate the discovery and prediction of subtle subtypes of cancers. Our method can be generally applied to multiple types of measurements, e.g., gene expression profiling, proteomic measuring, and recent next-generation sequencing, since it only requires the similarities among samples as input.
登录
查看更多内容
DOI：
10.1073/pnas.97.1.262
发表时间：
2000-01-04
影响因子：
11.1
作者：
Brown, MPS;Grundy, WN;Haussler, D
通讯作者：
Haussler, D
影响因子：
5.8
作者：
Hwang, TaeHyun;Sicotte, Hugues;Kuang, Rui
通讯作者：
Kuang, Rui
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
5.8
作者：
Kim, Hyunsoo;Park, Haesun
通讯作者：
Park, Haesun
DOI：
10.1073/pnas.0708598105
发表时间：
2008-03-18
影响因子：
11.1
作者：
Kim, Choongrak;Cheon, Mookyung;Chang, Iksoo
通讯作者：
Chang, Iksoo