SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.

ABSTRACT: Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple mo

SUBMITTER: Huang J 

PROVIDER: S-EPMC10982946 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature