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 …
[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 …
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 …
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 …
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 …
Guidelines for the use of flow cytometry and cell sorting in immunological studies
A Cossarizza, HD Chang, A Radbruch… - European journal of …, 2019 - Wiley Online Library
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 …
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 …
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
S Van Dam, U Vosa, A van der Graaf… - Briefings in …, 2018 - academic.oup.com
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 …
Multi-omics integration in biomedical research–A metabolomics-centric review
Recent advances in high-throughput technologies have enabled the profiling of multiple
layers of a biological system, including DNA sequence data (genomics), RNA expression …
layers of a biological system, including DNA sequence data (genomics), RNA expression …