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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >An Optimization-Based Framework for the Transformation of Incomplete Biological Knowledge into a Probabilistic Structure and Its Application to the Utilization of Gene/Protein Signaling Pathways in Discrete Phenotype Classification
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An Optimization-Based Framework for the Transformation of Incomplete Biological Knowledge into a Probabilistic Structure and Its Application to the Utilization of Gene/Protein Signaling Pathways in Discrete Phenotype Classification

机译：基于优化的不完全生物学知识转化为概率结构的框架及其在离散表型分类中利用基因/蛋白质信号通路的应用

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摘要

Phenotype classification via genomic data is hampered by small sample sizes that negatively impact classifier design. Utilization of prior biological knowledge in conjunction with training data can improve both classifier design and error estimation via the construction of the optimal Bayesian classifier. In the genomic setting, gene/protein signaling pathways provide a key source of biological knowledge. Although these pathways are neither complete, nor regulatory, with no timing associated with them, they are capable of constraining the set of possible models representing the underlying interaction between molecules. The aim of this paper is to provide a framework and the mathematical tools to transform signaling pathways to prior probabilities governing uncertainty classes of feature-label distributions used in classifier design. Structural motifs extracted from the signaling pathways are mapped to a set of constraints on a prior probability on a Multinomial distribution. Being the conjugate prior for the Multinomial distribution, we propose optimization paradigms to estimate the parameters of a Dirichlet distribution in the Bayesian setting. The performance of the proposed methods is tested on two widely studied pathways: mammalian cell cycle and a p53 pathway model.
机译：通过基因组数据进行的表型分类受到小样本量的限制，这不利于分类器的设计。通过构造最佳贝叶斯分类器，将先验生物学知识与训练数据结合使用可以改善分类器设计和错误估计。在基因组环境中，基因/蛋白质信号通路提供了生物学知识的关键来源。尽管这些途径既不完整，也不具有调节性，没有时序相关联，但它们能够约束代表分子间潜在相互作用的一组可能模型。本文的目的是提供一个框架和数学工具，将信号通路转化为控制分类器设计中使用的特征标签分布的不确定性类别的先验概率。从信号通路提取的结构基序被映射到多项式分布上对先验概率的一组约束。作为多项式分布的共轭先验，我们提出了优化范例来估计贝叶斯环境中Dirichlet分布的参数。在两种广泛研究的途径上测试了所提出方法的性能：哺乳动物细胞周期和p53途径模型。

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