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Applications of multi‐omics analysis in human diseases
Multi‐omics usually refers to the crossover application of multiple high‐throughput screening
technologies represented by genomics, transcriptomics, single‐cell transcriptomics …
technologies represented by genomics, transcriptomics, single‐cell transcriptomics …
Using machine learning approaches for multi-omics data analysis: A review
With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …
become essential for biomedical studies to undertake an integrative (combined) approach to …
Multi-omics data integration, interpretation, and its application
I Subramanian, S Verma, S Kumar… - … and biology insights, 2020 - journals.sagepub.com
To study complex biological processes holistically, it is imperative to take an integrative
approach that combines multi-omics data to highlight the interrelationships of the involved …
approach that combines multi-omics data to highlight the interrelationships of the involved …
[HTML][HTML] Integration strategies of multi-omics data for machine learning analysis
Increased availability of high-throughput technologies has generated an ever-growing
number of omics data that seek to portray many different but complementary biological …
number of omics data that seek to portray many different but complementary biological …
DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays
Motivation In the continuously expanding omics era, novel computational and statistical
strategies are needed for data integration and identification of biomarkers and molecular …
strategies are needed for data integration and identification of biomarkers and molecular …
Missing data in multi-omics integration: Recent advances through artificial intelligence
JE Flores, DM Claborne, ZD Weller… - Frontiers in artificial …, 2023 - frontiersin.org
Biological systems function through complex interactions between various 'omics
(biomolecules), and a more complete understanding of these systems is only possible …
(biomolecules), and a more complete understanding of these systems is only possible …
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 …
Guidelines for the use of flow cytometry and cell sorting in immunological studies
These guidelines are a consensus work of a considerable number of members of the
immunology and flow cytometry community. They provide the theory and key practical …
immunology and flow cytometry community. They provide the theory and key practical …
Multi-omic and multi-view clustering algorithms: review and cancer benchmark
N Rappoport, R Shamir - Nucleic acids research, 2018 - academic.oup.com
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 …
Gene co-expression analysis for functional classification and gene–disease predictions
Gene co-expression networks can be used to associate genes of unknown function with
biological processes, to prioritize candidate disease genes or to discern transcriptional …
biological processes, to prioritize candidate disease genes or to discern transcriptional …