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• W3136353561 abstract "For network-assisted analysis, which has become a popular method of data mining, network construction is a crucial task. Network construction relies on the accurate quantification of direct associations among variables. The existence of multiscale associations among variables presents several quantification challenges, especially when quantifying nonlinear direct interactions.In this study, the multiscale part mutual information (MPMI), based on part mutual information (PMI) and nonlinear partial association (NPA), was developed for effectively quantifying nonlinear direct associations among variables in networks with multiscale associations. First, we defined the MPMI in theory and derived its five important properties. Second, an experiment in a three-node network was carried out to numerically estimate its quantification ability under two cases of strong associations. Third, experiments of the MPMI and comparisons with the PMI, NPA and conditional mutual information were performed on simulated datasets and on datasets from DREAM challenge project. Finally, the MPMI was applied to real datasets of glioblastoma and lung adenocarcinoma to validate its effectiveness. Results showed that the MPMI is an effective alternative measure for quantifying nonlinear direct associations in networks, especially those with multiscale associations.The source code of MPMI is available online at https://github.com/CDMB-lab/MPMI.Supplementary data are available at Bioinformatics online." @default.
• W3136353561 created "2021-03-29" @default.
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• W3136353561 date "2021-03-17" @default.
• W3136353561 modified "2023-10-17" @default.
• W3136353561 title "Multiscale part mutual information for quantifying nonlinear direct associations in networks" @default.
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• W3136353561 doi "https://doi.org/10.1093/bioinformatics/btab182" @default.
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• W3136353561 hasPublicationYear "2021" @default.
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