Integrated Network Analysis and Therapeutic Target Exploration in Complex Diseases
Lead Research Organisation:
University College London
Department Name: Neuroscience Physiology and Pharmacology
Abstract
Complex diseases present a multifaceted interplay between genes, proteins, and other molecular entities, necessitating a multi-scale, integrated analysis for understanding their underlying mechanisms. This research project takes a novel approach to investigate complex disease pathways by leveraging the recent advancements in graph neural networks and geometric deep learning.
The objectives include a comprehensive investigation of the pathways and fundamental mechanisms of complex diseases through a systems-biology approach, integration of disparate data sources including e.g. protein-protein interactions and differential gene expression, and application of advanced computational methods to elucidate the geometric and topological properties of these networks.
The methodology encompasses the curation and amalgamation of large-scale biological datasets across different omics scales to establish a consolidated framework for analysis. State-of-the-art computational techniques, specifically graph neural networks and geometric deep learning, will be employed to decipher the complex structures of the curated integrated biological networks. The derived insights will be utilized to identify potential disease pathways, emphasizing those amenable for drug targeting.
Expected outcomes encompass a nuanced understanding of complex disease mechanisms through comprehensive biological network analysis, identification of therapeutic targets, and discovery of drug intervention pathways. The alignment of advanced computational tools with omics data and biological network topology will present a pioneering approach to categorizing disease pathology.
The significance of this research lies in its integration of cutting-edge computational techniques with vast biological data repositories. By focusing on intrinsic network structures and their associations with disease, this study hopes to uncover therapeutic targets and make substantial contributions to the field of biomedical research, particularly in the realms of drug development and therapeutic interventions for complex diseases.
The objectives include a comprehensive investigation of the pathways and fundamental mechanisms of complex diseases through a systems-biology approach, integration of disparate data sources including e.g. protein-protein interactions and differential gene expression, and application of advanced computational methods to elucidate the geometric and topological properties of these networks.
The methodology encompasses the curation and amalgamation of large-scale biological datasets across different omics scales to establish a consolidated framework for analysis. State-of-the-art computational techniques, specifically graph neural networks and geometric deep learning, will be employed to decipher the complex structures of the curated integrated biological networks. The derived insights will be utilized to identify potential disease pathways, emphasizing those amenable for drug targeting.
Expected outcomes encompass a nuanced understanding of complex disease mechanisms through comprehensive biological network analysis, identification of therapeutic targets, and discovery of drug intervention pathways. The alignment of advanced computational tools with omics data and biological network topology will present a pioneering approach to categorizing disease pathology.
The significance of this research lies in its integration of cutting-edge computational techniques with vast biological data repositories. By focusing on intrinsic network structures and their associations with disease, this study hopes to uncover therapeutic targets and make substantial contributions to the field of biomedical research, particularly in the realms of drug development and therapeutic interventions for complex diseases.
Organisations
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
MR/W006774/1 | 01/10/2022 | 30/09/2028 | |||
2720655 | Studentship | MR/W006774/1 | 01/10/2022 | 31/12/2026 | David Miller |