Learning gene functional classifications from multiple data types
Learning gene functional classifications from multiple data types
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DOI：
10.1089/10665270252935539
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发表时间：
2002-01-01
影响因子：
1.7
通讯作者：
Noble, WS
中科院分区：
文献类型：
--
作者：
Pavlidis, P;Weston, J;Noble, WS
在我们试图从分子水平理解细胞功能时，我们必须能够从不同类型的基因组数据中综合信息。我们考虑的问题推断基因功能分类从一个异质性的数据集组成的DNA微阵列表达测量和系统发育概况从全基因组序列比较。我们展示了支持向量机（SVM）学习算法的应用程序，这个功能推理任务。我们的结果表明利用有关数据异质性的先验信息的重要性。特别是，我们提出了一个SVM核函数，是显式异构。此外，我们描述了特征缩放方法，通过给每个数据类型不同的权重，进一步利用异质性的先验知识。
In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene functional classifications from a heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence comparisons. We demonstrate the application of the support vector machine (SVM) learning algorithm to this functional inference task. Our results suggest the importance of exploiting prior information about the heterogeneity of the data. In particular, we propose an SVM kernel function that is explicitly heterogeneous. In addition, we describe feature scaling methods for further exploiting prior knowledge of heterogeneity by giving each data type different weights.