Integration of multi-omics data for cancer subtype identification

Identifier:  TFG:7139

Handle: https://hdl.handle.net/20.500.11797/TFG7139

Authors:  Olomí Farré, Adrià

Abstract:
Advances in high-throughput technologies allow detailed measurement of various omics data, enabling the development of new methods to address biological questions such as cancer patient classification. State-of-the-art methods like Similarity Fusion Network (SNFtool), iClusterPlus, Perturbation Clustering for Data Integration and Disease Subtyping Plus (PINS+), and Consensus Clustering Plus (CC+) are powerful but not always able to correctly subtype data. A workflow that integrates these existing subtyping methods improves the classification of cancer patients. Future efforts should focus on standardizing omics databases and enhancing integration methods by optimizing parameters or developing new approaches.