GP Progression Model¶
The Gaussian Process Progression Model (GPPM) is a model of disease progression estimating long-term biomarkers’ trajectories across the evolution of a disease, from the analysis of short-term individual measurements. GPPM software has been first presented in the work Lorenzi, NeuroImage 2017, and subsequently extended in the GPPM-DS presented in Garbarino and Lorenzi, IPMI 2019 and Garbarino and Lorenzi, NeuroImage 2021.
GPPM and GPPM-DS enable the following analyses:
[GPPM] reoconstruct the profile of biomarkers evolution over time,
[GPPM] quantify the subject-specific disease severity associated with the measurements each individual (missing observations are allowed),
[GPPM] estimate the ordering of the biomarkers from normal to pathological stages,
[GPPM-DS] specify prior hypothesis about the causal interaction between biomarkers,
[GPPM-DS] data-driven estimation of the interaction parameters,
[GPPM-DS] data-driven comparison between different hypothesis to identify the most plausible interaction dynamics between biomarkers,
[GPPM-DS] model personalisation to simulate and predict subject-specific biomarker trajectories,
The software comes with a simple installation and an easy interface.
An example of the basic usage of GPPM on synthetic and real data is available here:
An example of GPPM-DS on synthetic and real data is available here:
The source code is available on GitLab. The software is freely distributed for academic purposes. All the commercial rights are owned by Inria.