Titlebook: Methodologies of Multi-Omics Data Integration and Data Mining; Techniques and Appli Kang Ning Book 2023 The Editor(s) (if applicable) and T

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Methodologies of Multi-Omics Data Integration and Data Mining978-981-19-8210-1Series ISSN 2213-2775 Series E-ISSN 2213-2783

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Introduction to Multi-Omics, paradigm shift in how we approach biomedical challenges (Manzoni et al. 2018). Large data sets are typically generated during genomics, transcriptomics, proteomics, microbiomics, and metabolomics research (Osier et al. 2017). With the advancement of these omics investigations, multi-omics research

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-Omics Technologies and Big Datae exists a discrepancy between the relatively mature -omics analysis techniques and the still developing standards and methods for handling their data, even more for the data integration of different -omics levels. This chapter presents common ?omics methods for biomedicine and important aspects per

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Multi-Omics Data Mining Techniques: Algorithms and Software have been dramatically accumulated. Because of the limitation of application individual omics, multi-omics efforts have been playing the lead in bioinformatics and biomedical research—from simple computation to data mining. As multi-omics, a merger of biology, informatics, data science and computat

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Multi-Omics Data Analysis for Inflammation Disease Research: Correlation Analysis, Causal Analysis ainvolved in the occurrence and development of a variety of diseases, including inflammation diseases. However, current research has concentrated on the relationship between disease and the human gut microbiota, as well as the interactions between microorganisms. The absence of a more detailed unders