To characterize longitudinal protein features and their dynamics, we have tested the state-of-the-art methods for longitudinal omics data as well as developed novel approaches that take into account the interplay between multiple proteins. For assessment of the methods in well-defined samples, surrogate longitudinal data were generated using mass spectrometry-based shotgun proteomics. To ensure high-quality quantitative data for modelling, we have performed a comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation (Välikangas et al. 2017) as well as normalization (Välikangas et al. 2017), and implemented an R/ Bioconductor package for normalization of phosphoproteomics data (Saraei et al. 2017). Towards optimized marker detection, we have implemented an R/Bioconductor package for reproducibility-optimized statistical testing ROTS (Suomi et al. 2017) and its enhanced version for the increasingly popular data-independent acquisition (DIA) mass spectrometry technology (Suomi & Elo 2017). For individualized dynamic predictive modelling of longitudinal proteomics data, we have compared currently available dynamic predictive models using longitudinal clinical data. Further investigation of the models is ongoing as well as investigation of alternative modelling techniques.