Garcia Condado, J. | bioRxiv Science

bioRxiv2024

AgeML: Age modelling with Machine Learning

An approach to age modeling involves the supervised prediction of age using machine learning from subject features. The derived age metrics are used to study the relationship between healthy and pathological aging in multiple body systems, as well as the interactions between them. We lack a standard for this type of age modeling. In this work we developed AgeML, an OpenSource software for age-prediction from any type of tabular clinical data following well-established and tested methodologies. The objective is to set standards for reproducibility and standardization of reporting in supervised age modeling tasks. AgeML does age modeling, calculates age deltas, the difference between predicted and chronological age, measures correlations between age deltas and factors, visualizes differences in age deltas of different clinical populations and classifies clinical populations based on age deltas. With this software we are able to reproduce published work and unveil novel relationships between body organs and polygenetic risk scores. AgeML is age modeling made easy for standardization and reproducibility.

bioinformatics↗

bioRxiv2022

Automatic determination of the handedness of Single-Particle maps of macromolecules solved by CryoEM

Single-Particle Analysis by Cryo-Electron Microscopy is a well-established technique to elucidate the three-dimensional (3D) structure of biological macromolecules. The orientation of the acquired projection images must be initially estimated without any reference to the final structure. In this step, algorithms may find a mirrored version of all the orientations resulting in a mirrored 3D map. It is as compatible with the acquired images as its unmirrored version from the image processing point of view, only that it is not biologically plausible. In this article, we introduce HaPi (Handedness Pipeline), the first method to automatically determine the hand of electron density maps of macromolecules solved by CryoEM. HaPi is built by training two 3D convolutional neural networks. The first determines -helices in a map, and the second determines whether the -helix is left-handed or right-handed. A consensus strategy defines the overall map hand. The pipeline is trained on simulated and experimental data. The handedness can be detected only for maps whose resolution is better than 5[A]. HaPi can identify the hand in 89% of new simulated maps correctly. Moreover, we evaluated all the maps deposited at the Electron Microscopy Data Bank and 11 structures uploaded with the incorrect hand were identified.

bioinformatics↗