Bayesian Networks in R: with Applications in Systems Biology
Bayesian Networks in R: with Applications in Systems Biology
复制标题
DOI：
--
复制
发表时间：
2013-04
期刊：
影响因子：
--
通讯作者：
R. Nagarajan;M. Scutari;Sophie Lbre
中科院分区：
文献类型：
--
作者：
R. Nagarajan;M. Scutari;Sophie Lbre
Bayesian Networks in R with Applications in Systems Biology是独一无二的，因为它结合开源统计环境R中的示例，向读者介绍了贝叶斯网络建模和推理的基本概念。复杂的水平也逐渐增加了跨章节的练习和解决方案，以增强对理论和概念的动手实验的理解。该应用程序侧重于系统生物学，重点是从高通量分子数据建模途径和信号机制。贝叶斯网络已被证明是在这方面特别有用的抽象。它们的有用性特别体现在它们除了验证感兴趣的分子中的已知关联之外还能够发现新的关联。人们还预计，公开可用的高通量生物数据集的流行可能会鼓励观众使用书中提出的方法探索调查新的范式。
Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.