The ongoing decrease in cost and processing time of omics-based methodologies has led to a significant increase in the volume of big data, shifting research methodologies from hypothesis-driven investigations to data-driven analyses.
To achieve a comprehensive understanding of human health and diseases, it is necessary to interpret the intricate molecular complexities and variations across multiple levels, including the genome, epigenome, transcriptome, proteome, and metabolome. The recollection of data at these various levels is referred to as multi-omics data.
In this work, we apply and compare different approaches to perform disease subtype discovery from multi-omics data, in the context of prostate cancer.
- Experiments can be reproduced from
bio_2023.Rmd - A complete description of the project can be found in
report.pdf
