Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, 17035, South Korea.
Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea.
Genes Genomics. 2019 May;41(5):547-555. doi: 10.1007/s13258-019-00789-8. Epub 2019 Feb 11.
Simultaneous measurement of gene expression level for thousands of genes contains the rich information about many different aspects of biological mechanisms. A major computational challenge is to find methods to extract new biological insights from this wealth of data. Complex biological processes are often regulated under the various conditions or circumstances and associated gene interactions are dynamically changed depending on different biological contexts. Thus, inference of such dynamic relationships between genes with consideration of biological conditions is very challenging.
In this study, we propose a comprehensive and integrated approach to infer the dynamic relationships between genes and evaluate this approach on three distinct gene networks.
This study demonstrates the advantage of integrating Markov chain Monte Carlo (MCMC) simulation into a Bayesian mixture model to overcome the high-dimension, low sample size (HDLSS) problem as well as to identify context-specific biological modules. Such biological modules were identified through the summarization of sampled network structures obtained from MCMC simulation.
This novel approach gives a comprehensive understanding of the dynamically regulated biological modules.
同时测量数千个基因的表达水平包含了许多不同方面的生物学机制的丰富信息。一个主要的计算挑战是找到从这些大量数据中提取新的生物学见解的方法。复杂的生物过程通常在各种条件或环境下受到调节，并且相关的基因相互作用根据不同的生物背景而动态变化。因此，考虑到生物条件，推断基因之间的这种动态关系是非常具有挑战性的。
在这项研究中，我们提出了一种综合的、集成的方法来推断基因之间的动态关系，并在三个不同的基因网络上评估了这种方法。
本研究证明了将马尔可夫链蒙特卡罗（MCMC）模拟集成到贝叶斯混合模型中以克服高维、小样本量（HDLSS）问题以及识别特定于上下文的生物模块的优势。这些生物模块是通过对从 MCMC 模拟中获得的采样网络结构进行总结来识别的。
这种新方法提供了对动态调节的生物模块的全面理解。
