Patient Subgrouping using Multiomics Data on Grassmann Manifold

Description:

A comprehensive approach to identifying patient subgroups with distinct survival rates by leveraging multiomics datasets, namely, microRNA, gene expression, and DNA methylation. This project showcases:

• Integration of heterogeneous big data into intrinsic structures.
• Dimensionality reduction via PCA.
• Graph construction for patient data.
• Data embedding using the Grassmann manifold for patient clustering.
• Extensive testing on datasets from The Cancer Genome Atlas (TCGA).

By integrating these varied datasets on a Grassmann manifold, our method outperforms conventional methods in clustering accuracy and survival rate prediction, paving the way for precise and personalized medical treatments.

Keywords:

Multi-omics, Cancer Subtype, Graphs, Grassmann Manifold, Patient Subgrouping, PCA, Data Integration, The Cancer Genome Atlas, Survival Rates, Clustering, Dimensionality Reduction.

Cite Our Work:

If you find this work beneficial and utilize it in your research, please cite our original article: Patient subgrouping with distinct survival rates via integration of multiomics data on a Grassmann manifold.