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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >BAYESIAN NETWORK LEARNING WITH FEATURE ABSTRACTION FOR GENE-DRUG DEPENDENCY ANALYSIS
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BAYESIAN NETWORK LEARNING WITH FEATURE ABSTRACTION FOR GENE-DRUG DEPENDENCY ANALYSIS

机译：特征提取的贝叶斯网络学习与特征抽象

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摘要

Combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activities in the malignant cell. In this paper, we apply Bayesian networks, a tool for compact representation of the joint probability distribution, to such analysis. For the alleviation of data dimensionality problem, the huge datasets were condensed using a feature abstraction technique. The proposed analysis method was applied to the NCI60 dataset (http://discover.nci.nih.gov) consisting of gene expression profiles and drug activity patterns on human cancer cell lines. The Bayesian networks, learned from the condensed dataset, identified most of the salient pairwise correlations and some known relationships among several features in the original dataset, confirming the effectiveness of the proposed feature abstraction method. Also, a survey of the recent literature confirms the several relationships appearing in the learned Bayesian network to be biologically meaningful.
机译：对微阵列和药物活性数据集的组合分析具有揭示关于恶性细胞中基因表达和药物活性之间各种关系的有价值的知识的潜力。在本文中，我们将贝叶斯网络（一种用于联合概率分布的紧凑表示的工具）应用于此类分析。为了缓解数据维数问题，使用功能抽象技术将庞大的数据集进行了压缩。拟议的分析方法已应用于NCI60数据集（http://discover.nci.nih.gov），该数据集由人类癌细胞系上的基因表达谱和药物活性模式组成。从压缩数据集中获悉的贝叶斯网络确定了大多数显着的成对相关性以及原始数据集中多个特征之间的某些已知关系，从而证实了所提出的特征抽象方法的有效性。同样，对最近文献的调查证实了在学习的贝叶斯网络中出现的几种关系具有生物学意义。

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