PAMOGK

PAMOGK: Pathway-Based Multi-Omic Clustering

PAMOGK enhances classification of cancer subgroups by integrating multi-omics data with biological pathways.

Key Features:

• Multi-Omics Integration: Combines genomics, transcriptomics with pathway data for multi-dimensional tumor analysis.
• Graph Kernel Evaluation: Evaluates patient similarities via molecular alterations within pathways.
• Multi-View Clustering: Uses multi-view kernel clustering to integrate multiple pathway perspectives for robust subgroup identification.

Scientific Applications:

• Cancer Subgroup Identification: Identifies cancer subgroups with distinct survival outcomes and clinical parameters; applied to KIRC, revealing four clusters with varying survival times.

Methodology:

• Data Integration: Combines multi-omics data with pathway information.
• Graph Kernel Evaluation: Assesses patient similarities based on molecular alterations within pathways.
• Clustering Approach: Utilizes multi-view kernel clustering to integrate multiple data perspectives.

Research Impact:

Demonstrates superior performance in partitioning KIRC patients into survival-distinct groups; identifies cancer-specific pathways to inform personalized therapeutic strategies.