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 …

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 …

With the rapid adoption of high-throughput omic approaches to analyze biological samples
such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can …

Through the developments of Omics technologies and dissemination of large-scale
datasets, such as those from The Cancer Genome Atlas, Alzheimer's Disease Neuroimaging …

Background Methods for the integrative analysis of multi-omics data are required to draw a
more complete and accurate picture of the dynamics of molecular systems. The complexity …

Background In high-dimensional data (HDD) settings, the number of variables associated
with each observation is very large. Prominent examples of HDD in biomedical research …

Computational integrative analysis has become a significant approach in the data-driven
exploration of biological problems. Many integration methods for cancer subty** have …

Metabolomics deals with multiple and complex chemical reactions within living organisms
and how these are influenced by external or internal perturbations. It lies at the heart of …

Motivation: In biomedical research a growing number of platforms and technologies are
used to measure diverse but related information, and the task of clustering a set of objects …

Motivation: The integration of multiple datasets remains a key challenge in systems biology
and genomic medicine. Modern high-throughput technologies generate a broad array of …