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Improved Bayesian Graphical Models for Omics Data

Abstract Number:

2539 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Lisa Bramer (1), David Degnan (1), Erik VonKaenel (2), Moses Obiri (1), Daniel Adrian (3)

Institutions:

(1) Pacific Northwest National Laboratory, N/A, (2) N/A, N/A, (3) Grand Valley State University, N/A

Co-Author(s):

David Degnan  
Pacific Northwest National Laboratory
Erik VonKaenel  
N/A
Moses Obiri  
Pacific Northwest National Laboratory
Daniel Adrian  
Grand Valley State University

First Author:

Lisa Bramer  
Pacific Northwest National Laboratory

Presenting Author:

Lisa Bramer  
Pacific Northwest National Laboratory

Abstract Text:

The study of protein–protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. Graphical models are useful tools for understanding complex biological relationships between biomolecules in high-dimensional data. Nevertheless, their current usability is limited, particularly in a Bayesian estimation paradigm when handling multiclass large datasets, particularly in the field of biology, due to computational limitations. Here, we introduce a clustering-focused iterative (CFI) methodology designed to enhance the scalability and accuracy of multiple Gaussian Graphical Model (GGM) estimation in high-dimensional spaces. Further, we present a framework for a Bayesian graphical model which allows for group-specific prior distribution specification leading to improved model accuracy. We present results from simulation studies as well as a real-world application to data from host-response mass spectrometry studies.

Keywords:

graphical model|Bayesian|omics data| | |

Sponsors:

Biometrics Section

Tracks:

Longitudinal/Correlated Data

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