Biology subjects

Monsarrat, P.

Publications and source records attributed to Monsarrat, P..

3 recordsLinked to original sources

bioRxiv2025

Expert-guided multi-objective optimization: an efficient strategy for parameter estimation of biological systems with limited data.

Calibrating biological models is challenging due to high-dimensional parameter spaces and the limited availability of reliable experimental data. In this study, we propose a hybrid calibra- tion framework that integrates expert knowledge into a multi-objective optimization process using NSGA-II algorithm. Our approach combines hard constraints derived from biological measurements with soft constraints encoding qualitative domain expertise, such as expected curve shapes or event timing. This dual-constraint strategy guides the search toward biologi- cally plausible parameter sets while preserving flexibility and interpretability. We demonstrate the effectiveness of our method on a benchmark model of skin wound healing, comparing it to standard and unconstrained optimization strategies. Results show that incorporating expert guidance significantly improves the biological relevance of simulated dynamics and mitigates overfitting, especially in underdetermined or uncertain settings. The framework is flexible, it- erative, and generalizable, offering a principled way to leverage domain knowledge for model calibration in complex biological systems.

systems biology↗

bioRxiv2025

Discernibility in explanations: an approach to designing more acceptable and meaningful machine learning models for medicine

BackgroundAlthough the benefits of machine learning (ML) are undeniable in health-care, explainability plays a vital role in improving transparency and understanding the most decisive and persuasive variables for prediction. The challenge is to identify explanations that make sense to the biomedical expert. This work proposes discernibility as a new approach to faithfully reflect human cognition, with the users perception of a relationship between explanations and data for a given variable. MethodsA total of 50 participants (19 biomedical and 31 data scientists) evaluated their perception of the discernibility of explanations from both synthetic and human-based dataset (National Health and Nutrition Examination Survey). The inter-rater reliability was tested through the intraclass correlation coefficient (ICC). 13 statistical coefficients were considered to be able to capture for a given variable the relationship between its values and its explanations. A Passing-Bablok regression was performed for each user to highlight the consistency between user rating and each coefficient. FindingsThe low inter-rater reliability of discernibility (ICC{inverted exclamation}0.5) with no difference between areas of expertise or level of education underlines the need for an objective metric of discernibility. Among all evaluated metrics, dcor metric was found to be the most suitable to capture the intra-individual reliability of discernibility perceived by users (median slope closer to 1 and a narrower confidence interval width for the Passing-Bablok regression with the lowest differential bias between the most and least discernible values). Interpretationdcor was shown to be a reliable metric for assessing the discernibility of explanations, effectively capturing the clarity of the relationship between the data and their explanations, and providing clues to the underlying pathophysiological mechanisms that are not immediately apparent when examining individual predictors. Discernibility can also serve as an evaluation metric for model quality, used to prevent overfitting or aid in feature selection, providing medical practitioners with more accurate and persuasive results.

bioinformatics↗

bioRxiv2025

A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues

BackgroundIn the last decades, the utility of Machine Learning (ML) in the biomedical domain has been demonstrated repeatedly. Their inherent opacity need augmenting ML with explainability techniques. A common practice in model explainability however, is to focus solely on the explanatory values themselves without accounting for both the main and interaction effects. While this approach simplifies interpretation, it potentially overlooks critical medical information since the nature of the interactions may provide clues to the underlying biological mechanisms. ResultsThis article introduces a novel method for analyzing explanatory values of machine learning (ML) models, in the form of a comprehensive graphical visualization. The method not only emphasises the individual contributions of the features but also gives insights about the interactions they share with one another. Designed for local additive explanation methods, the proposed tool effectively translates the complex and multidimensional nature of these values into an intuitive single-graph format. It offers a clear window into how feature interactions contribute to the overall prediction of the ML model while aiding in the identification of various interaction types, such as mutual attenuation, positive/negative synergies or dominance of one feature over another. ConclusionsThis approach provides insights for generating hypotheses, improving the transparency of ML models, particularly in the context of biology and medicine since living organisms are characterised by a multitude of parameters in complex interactions, a complexity that ensures the "stability" and robustness of structures and functions.

systems biology↗