Research Questions:
1) How do biomarker candidates vary in stability when data from different omics layers are analyzed across distribution shifts, such as those caused by environmental factors, disease progression, or technical variations?
2) Can we develop a computational framework that identifies biomarkers robust to distribution shifts, ensuring their reliability in diverse populations or under varying conditions?
3) How can we assess the impact of distribution shifts on the performance of biomarkers for diagnostic, prognostic, or predictive purposes?
4) What statistical and machine learning methods are most effective in identifying stable biomarkers that maintain their significance across different data distributions?
Research Objectives:
Objective 1, Biomarker Stability Assessment: To establish a framework for assessing the stability of potential biomarkers across distribution shifts, including those due to environmental changes, disease progression, and technical variations.
Objective 2, Identification of Stable Biomarkers: To identify stable biomarkers that maintain their predictive power and significance across different data distributions using advanced statistical and machine learning techniques.
Objective 3, Development of Predictive Models: To develop predictive models that incorporate stable biomarkers for accurate classification, prognosis, or treatment response prediction across distribution shifts.
Scientific Rationale: The scientific rationale for the project lies in the critical need for reliable biomarkers that can persistently indicate disease states despite changes in data distribution. By leveraging the complexity of multi-omics data, which encompasses genomic, proteomic, and metabolomic information, the project aims to identify biomarkers that are robust across various conditions, thereby enhancing the accuracy of early detection, prognosis, and therapeutic monitoring.