School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul 02844, Republic of Korea.
Int J Mol Sci. 2023 Aug 14;24(16):12784. doi: 10.3390/ijms241612784.
Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in single genes. Specifically, the exploration of cell line-specific gene networks can provide essential clues for precision medicine, as this methodology can uncover molecular interplays specific to particular cell line statuses, such as drug sensitivity, cancer progression, etc. In this article, we provide a comprehensive review of computational strategies for cell line-specific gene network analysis: (1) cell line-specific gene regulatory network estimation and analysis of gene networks under varying epithelial-mesenchymal transition (EMT) statuses of cell lines; and (2) an explainable artificial intelligence approach for interpreting the estimated massive multiple EMT-status-specific gene networks. The objective of this review is to help readers grasp the concept of computational network biology, which holds significant implications for precision medicine by offering crucial clues.
理解复杂疾病机制需要全面了解基因调控网络，因为复杂疾病通常表现为分子网络的失调和功能障碍，而不是单个基因的异常。具体来说，探索细胞系特异性基因网络可以为精准医学提供重要线索，因为这种方法可以揭示特定于特定细胞系状态的分子相互作用，例如药物敏感性、癌症进展等。在本文中，我们提供了细胞系特异性基因网络分析的计算策略的全面综述：（1）细胞系特异性基因调控网络估计和分析细胞系中不同上皮-间充质转化（EMT）状态下的基因网络；（2）用于解释估计的大量 EMT 状态特异性基因网络的可解释人工智能方法。本综述的目的是帮助读者掌握计算网络生物学的概念，这通过提供关键线索对精准医学具有重要意义。
