Efficient Mining of Discriminative Co-clusters from Gene Expression Data.
Efficient Mining of Discriminative Co-clusters from Gene Expression Data.
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DOI：
10.1007/s10115-013-0684-0
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发表时间：
2014-12
影响因子：
2.7
通讯作者：
Reddy, Chandan K.
中科院分区：
文献类型：
--
作者：
Odibat, Omar;Reddy, Chandan K.
判别模型用于分析两个类之间的差异，并识别类特定的模式。大多数现有的判别模型依赖于使用整个特征空间来计算每个类别的判别模式。联合聚类被提出来捕获特征子集中相关的模式，但它不能处理标记数据集中的判别模式。在某些生物学应用中，如基因表达分析，考虑仅在特征空间的子集中相关的判别模式是至关重要的。本文的目标是双重的：首先，它提出了一种算法，有效地找到任意位置的协同聚类从复杂的数据。其次，它扩展了这种共聚类算法，发现歧视性的共聚类，将类信息的共聚类搜索过程。此外，我们还描述了歧视性的协聚类，并提出了三个新的措施，可用于评估任何歧视性的子空间模式挖掘算法的性能。我们在几个合成的和真实的基因表达数据集上对所提出的算法进行了评估，我们的实验结果表明，所提出的算法优于文献中现有的几种算法。
Discriminative models are used to analyze the differences between two classes and to identify class-specific patterns. Most of the existing discriminative models depend on using the entire feature space to compute the discriminative patterns for each class. Co-clustering has been proposed to capture the patterns that are correlated in a subset of features, but it cannot handle discriminative patterns in labeled datasets. In certain biological applications such as gene expression analysis, it is critical to consider the discriminative patterns that are correlated only in a subset of the feature space. The objective of this paper is two-fold: first, it presents an algorithm to efficiently find arbitrarily positioned co-clusters from complex data. Second, it extends this co-clustering algorithm to discover discriminative co-clusters by incorporating the class information into the co-cluster search process. In addition, we also characterize the discriminative co-clusters and propose three novel measures that can be used to evaluate the performance of any discriminative subspace pattern mining algorithm. We evaluated the proposed algorithms on several synthetic and real gene expression datasets, and our experimental results showed that the proposed algorithms outperformed several existing algorithms available in the literature.
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