Despite the fundamental role networks play in how scientists understand the dynamics and properties of complex systems, reconstructing networks from large-scale experimental data is a challenge.
In systems biology and microbial ecology -- the study of microbes in the environment and their interactions with each other -- the challenges of reconstructing these networks can be compounded by difficulty unraveling direct and indirect interactions, or the ability of one element in a system to impact another, either with or without direct interaction.
Researchers have devised a new conceptual framework for disentangling direct and indirect relationships in association networks. The framework is called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity).
The researchers tested on the framework on synthetic gene expression and microbial community data.
Specifically, the iDIRECT framework reduces mathematical challenges to network reconstruction, including ill-conditioning, self-looping and interaction strength overflow. Using simulation data to benchmark results, the researchers demonstrated high prediction accuracies using the iDIRECT framework.
See:
Naijia Xiao, Aifen Zhou, Megan L. Kempher, Benjamin Y. Zhou, Zhou Jason Shi, Mengting Yuan, Xue Guo, Linwei Wu, Daliang Ning, Joy Van Nostrand, Mary K. Firestone, Jizhong Zhou. Disentangling direct from indirect relationships in association networks. Proceedings of the National Academy of Sciences, 2022; 119 (2): e2109995119 DOI: 10.1073/pnas.2109995119
Posted by Dr. Tim Sandle, Pharmaceutical Microbiology Resources (http://www.pharmamicroresources.com/)
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