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Multi-omic and multi-view clustering algorithms: review and cancer benchmark
Recent high throughput experimental methods have been used to collect large biomedical
omics datasets. Clustering of single omic datasets has proven invaluable for biological and …
omics datasets. Clustering of single omic datasets has proven invaluable for biological and …
More is better: recent progress in multi-omics data integration methods
Multi-omics data integration is one of the major challenges in the era of precision medicine.
Considerable work has been done with the advent of high-throughput studies, which have …
Considerable work has been done with the advent of high-throughput studies, which have …
Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry
To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we
applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing …
applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing …
Integrated omics: tools, advances and future approaches
With the rapid adoption of high-throughput omic approaches to analyze biological samples
such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can …
such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can …
Correlation and association analyses in microbiome study integrating multiomics in health and disease
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Computational integrative analysis has become a significant approach in the data-driven
exploration of biological problems. Many integration methods for cancer subty** have …
exploration of biological problems. Many integration methods for cancer subty** have …