Leveraging Omics and Machine Learning: Predicting Disease Progression and Nutritional Interventions in Pets

Tuesday, April 29, 2025

10:55 AM – 11:15 AM PST

Room: A105

Presenting Author(s)

CJ

Chang Woon Jang

Sr. Bioinformatics Scientist
Nestle Purina Petcare Company
Saint Louis, Missouri, United States

Co-Author(s)

ES

Ebenezer Satyaraj

Director Nutrition Science
Nestle Purina, United States
PS

Pascal Steiner

Sr Director Nutrition Science
Nestle Purina, United States
RM

Rondo Middleton

Sr Principal Scientist
Nestle Purina, United States
PY

Pan Yuanlong

Fellow Scientist
Nestle Purina, United States
QL

Qinghong Li

Sr Principal Scientist
Nestle Purina, United States

Abstract:

Food plays a crucial role in influencing various omics fields of study, which are comprehensive approaches to studying biological systems. Metabolomics focuses on the study of metabolites, the small molecules involved in metabolism. Diet directly impacts the metabolome, which reflects the biochemical activities within an organism. Additionally, food intake can alter the protein composition in cells and tissues, as studied in proteomics. Proteins are crucial for various cellular functions, and their levels can be influenced by diet. Therefore, investigating various omics data could reveal new insights into the mechanisms underlying the metabolism of nutrients and health. In this presentation, we utilized machine learning models on large complex metabolomics and proteomics datasets to reduce their dimensionality within these datasets and predict disease progression in cats and dogs. These models may identify biomarkers from datasets to help predict disease progression and aging, potentially facilitating nutritional interventions for pet health.