Sparse Independence Component Analysis for Competitive Endogenous RNA Co-Module Identification in Liver Hepatocellular Carcinoma
نویسندگان
چکیده
Objective: Long non-coding RNAs (lncRNAs) have been shown to be associated with the pathogenesis of different kinds diseases and play important roles in various biological processes. Although numerous lncRNAs found, functions most physiological/pathological significance are still its infancy. Meanwhile, their expression patterns regulation mechanisms also far from being fully understood. Methods: In order reveal functional identify key lncRNAs, we develop a new sparse independence component analysis (ICA) method lncRNA-mRNA-miRNA co-modules based on competitive endogenous RNA (ceRNA) theory using sample-matched lncRNA, mRNA miRNA profiles. The data three combined together is approximated sparsely obtain corresponding sparsity coefficient, then it decomposed by ICA constraint optimization common basis modules. Subsequently, affine propagation clustering used perform cluster under multiple running conditions for selection elements. Results: We applied Liver Hepatocellular Carcinoma (LIHC) dataset experiment results demonstrate that proposed can effectively discover biologically Conclusion: It may provide insights into function molecular mechanism LIHC. Clinical Translational Impact Statement–The LIHC modules, which IncRNAs
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ژورنال
عنوان ژورنال: IEEE Journal of Translational Engineering in Health and Medicine
سال: 2023
ISSN: ['2168-2372']
DOI: https://doi.org/10.1109/jtehm.2023.3283519