Unraveling the Molecular Puzzle: Exploring Gene Networks across Diverse EMT Status of Cell Lines
-
Published:2023-08-14
Issue:16
Volume:24
Page:12784
-
ISSN:1422-0067
-
Container-title:International Journal of Molecular Sciences
-
language:en
-
Short-container-title:IJMS
Affiliation:
1. School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul 02844, Republic of Korea
Abstract
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.
Subject
Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis
Reference61 articles.
1. A survey of neural network-based cancer prediction models from microarray data;Daoud;Artif. Intell. Med.,2019 2. Network-based prediction of drug combinations;Cheng;Nat. Commun.,2019 3. Fout, A., Byrd, J., Shariat, B., and Ben-Hur, A. Proceedings of the NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, 4–9 December 2017. 4. Shimamura, T., Imoto, S., Shimada, Y., Hosono, Y., Niida, A., Nagasaki, M., Yamaguchi, R., Takahashi, T., and Miyano, S. (2011). A novel network profiling analysis reveals system changes in epithelial-mesenchymal transition. PLoS ONE, 6. 5. Gene regulatory networks and their applications: Understanding biological and medical problems in terms of networks;Dehmer;Front. Cell Dev. Biol.,2014
|
|