Nacher Jose C, Akutsu Tatsuya
Faculty of Science, Department of Information Science, Toho University, Chiba, Japan.
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.
Methods Mol Biol. 2019;1912:289-300. doi: 10.1007/978-1-4939-8982-9_11.
Human diseases are not only associated to mutations in protein-coding genes. Contrary to what was thought decades ago, the human genome is largely transcribed which generates a large amount of nonprotein-coding RNAs (ncRNAs). Interestingly, these ncRNAs are not only able to perform biological functions and interact with other molecules such as proteins, but also have been reported involved in human diseases. In this book chapter, we review the recent research done on controllability methods related to associations between ncRNAs and human diseases. First, we introduce the bipartite complex network resulting from the interactions of ncRNAs and proteins. We then explain the theoretical background of controllability algorithms and apply these methods to the problem of identifying ncRNAs with critical roles in network control. Then, by performing statistical analyses we can answer the question on whether the subset of critical control ncRNAs is also enriched by human diseases. In addition, we review three-layer network models for prediction of ncRNA-disease associations.
人类疾病不仅与蛋白质编码基因的突变有关。与几十年前的认知相反，人类基因组大部分区域都能转录，从而产生大量非蛋白质编码RNA（ncRNA）。有趣的是，这些ncRNA不仅能够执行生物学功能并与其他分子（如蛋白质）相互作用，而且据报道还参与了人类疾病。在本章中，我们回顾了最近关于ncRNA与人类疾病关联的可控性方法的研究。首先，我们介绍由ncRNA与蛋白质相互作用产生的二分复杂网络。然后，我们解释可控性算法的理论背景，并将这些方法应用于识别在网络控制中起关键作用的ncRNA的问题。接着，通过进行统计分析，我们可以回答关键控制ncRNA的子集是否也在人类疾病中富集的问题。此外，我们还回顾了用于预测ncRNA-疾病关联的三层网络模型。
