Mohseni, N. | Search bioRxiv

bioRxiv2024

predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable app and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

neuroscience↗

bioRxiv2023

MORPHIX: Resolving sample identification bias in morphometrics analysis with a supervised machine learning package

Evolutionary biologists, primarily palaeoanthropologists, anatomists and ontogenists, employ modern geometric morphometrics to quantitatively analyse physical forms (e.g., skull morphology) and explore relationships, variations, and differences between samples and taxa using landmark coordinates. The standard approach comprises two steps: Generalised Procrustes Analysis (GPA) followed by Principal Component Analysis (PCA). PCA projects the superimposed data produced by GPA onto a set of uncorrelated variables, which can be visualised on scatterplots and used to draw phenetic, evolutionary, and ontogenetic conclusions. Recently, the use of PCA in genetic studies has been challenged. Due to PCAs central role in morphometrics, we sought to evaluate the standard approach and claims based on PCA outcomes. To test PCAs accuracy, robustness, and reproducibility using benchmark data of the crania of five papionin genera, we developed MORPHIX, a Python package for processing superimposed landmark data with classifier and outlier detection methods, which can be further visualised using various plots. Throughout this manuscript, we address the recent and contentious use of PCA in physical anthropology and phylogenetic inference, such as the case of Homo Nesher Ramla, an archaic hominin with a questionable taxonomy. We found that PCA outcomes are artefacts of the input data and are neither reliable, robust, nor reproducible as field members may assume. We also found that supervised machine learning classifiers are more accurate both for classification and detecting new taxa. Our findings raise concerns about PCA-based findings applied in 18,400 to 35,200 Physical anthropology studies. Our work can be used to evaluate prior and novel claims concerning the origins and relatedness of inter- and intra-species and improve phylogenetic and taxonomic reconstructions.

evolutionary biology↗