CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration.

ABSTRACT: Molecular analytics increasingly utilize machine learning (ML) for predictive modeling based on data acquired through molecular profiling technologies. However, developing robust models that accurately capture physiological phenotypes is challenged by the dynamics inherent to biological systems, variability stemming from analytical procedures, and the resource-intensive nature of obtaining sufficiently representative datasets. Here, we propose and evaluate a new method: Contextual Out-of-Distribution Integration (CODI). Based on experimental observations, CODI generates synthetic data that integrate unrepresented sources of variation encountered in real-world applications into a given molecular fingerprint dataset. By augmenting a dataset with out-of-distribution variance, CODI enables an

SUBMITTER: Eissa T 

PROVIDER: S-EPMC11495219 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature