Resolution-Adaptive Binning Enhances Machine Learning Modeling by Interbatch and Multiplatform Orbitrap-Based Shotgun Mass Spectrometry Data Integration.

ABSTRACT: Machine learning (ML) modeling on mass spectrometry (MS)-based shotgun data facilitates feature selection and disease modeling. However, batch-specific models often struggle with limited transferability and generalizability, necessitating data integration from multiple batches and platforms. Traditional binning methods can either disintegrate or aggregate m/z features, making data combination unreliable. In this study, we introduce a mass resolution-adaptive binning and integration strategy to overcome these challenges. This approach recovers 88-99% of ground truth features in a low mass region (70-434 m/z) from 49 mixed standard solutions at 250, 500, and 1000 ppb. Compared to conventional methods, it demonstrates stable binning and integration across low (100-

SUBMITTER: Ngan HL 

PROVIDER: S-EPMC12874219 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature