ageml 0.0.1

AgeML is a Python package for Age Modelling with Machine Learning made easy.

AgeModelling

Description

BrainAge models (Franke et al. 2010, Neuroimage) have had success in exploring the relationship between healthy and pathological ageing of the brain. Furthermore, this type of age modelling can be extended to multiple body systems and modelling of the interactions between them (Tian et al 2023, Nature Medicine). However, there is no standard for age modelling. There have been works attempting to describe proper procedures, especially for age-bias correction (de Lange and Cole 2020, Neuroimage: Clinical). In this work we developed an Open-Source software that allows anyone to do age modelling following well-established and tested methodologies for any type of clinical data. Age modelling with machine learning made easy.

The objective of AgeML is to standardise procedures, lower the barrier to entry into age modelling and ensure reproducibility. The project is Open-Source to create a welcoming environment and a community to work together to improve and validate existing methodologies. We are actively seeking new developers who want to contribute to growing and expanding the package.

References: De Lange, A.-M. G., & Cole, J. H. (2020). Commentary: Correction procedures in brain-age prediction. NeuroImage: Clinical, 26, 102229. https://doi.org/10.1016/j.nicl.2020.102229 Franke, K., Ziegler, G., Klöppel, S., & Gaser, C. (2010). Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters. NeuroImage, 50(3), 883–892. https://doi.org/10.1016/j.neuroimage.2010.01.005 Tian, Y. E., Cropley, V., Maier, A. B., Lautenschlager, N. T., Breakspear, M., & Zalesky, A. (2023). Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nature Medicine, 29(5), 1221–1231. https://doi.org/10.1038/s41591-023-02296-6

CI/CD

License

Licensed under the Apache 2.0