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process optimization » model optimization (Expand Search)
where optimization » whale optimization (Expand Search), phase optimization (Expand Search), other optimization (Expand Search)
basic process » based process (Expand Search), basic protein (Expand Search)
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Supplementary information for Efficient distributed edge computing for dependent delay-sensitive tasks in multi-operator multi-access networks
Published 2024“…Tasks are executed by MEC servers with the assistance of nearby edge devices, so that the MEC network can be viewed as a distributed “primary-secondary node” system where each MEC server acts as a primary node (PN) deciding on sub-tasks assigned to its secondary nodes (SNs), i.e., nearby edge devices. …”
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Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX
Published 2019“…Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. …”
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Table_2_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX
Published 2019“…Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. …”
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Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx
Published 2019“…Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. …”
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Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS
Published 2019“…Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. …”
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Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX
Published 2019“…Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. …”