Research Outline:
The proposed research aims to develop and validate a Graph Neural Network (GNN)-based computational framework for the comparative analysis of subgraphs derived from transcriptomics data. With the increasing complexity and volume of biological datasets, traditional methods for analyzing transcriptomic data face significant limitations in capturing intricate relationships between genes and phenotypes. GNNs offer a promising solution by explicitly modeling complex biological interactions as graph structures, enabling richer and more accurate biological insights.
Research Questions:
How effectively can GNN methodologies enhance the analysis, interpretation, and predictive power of transcriptomics data compared to traditional analytical approaches?
Can the subgraph patterns identified through GNN analyses serve as robust and clinically relevant biomarkers for distinguishing various disease states or phenotypic traits?
Objectives:
To design and implement a comprehensive GNN-based analytical pipeline capable of systematically extracting and comparing subgraph features from large-scale transcriptomics datasets.
To utilize UK Biobank’s extensive, diverse, and high-quality transcriptomic and phenotypic data for rigorous methodological validation and benchmarking.
To demonstrate the clinical and biological utility of the developed approach through predictive modeling, biomarker identification, and elucidation of biological pathways associated with key diseases and phenotypes.