Bioinformatics.

Identifying interactions in omics data for clinical biomarker discovery using symbolic regression.

Niels Johan Christensen, Samuel Demharter, Meera Machado, Lykke Pedersen, Marco Salvatore, Valdemar Stentoft-Hansen, Miquel Triana Iglesias. Bioinformatics, 38, 15 (2022).

Abstract.

Motivation.

The identification of predictive biomarker signatures from omics and multi-omics data for clinical applications is an active area of research. Recent developments in assay technologies and machine learning (ML) methods have led to significant improvements in predictive performance. However, most high-performing ML methods suffer from complex architectures and lack interpretability.

Results.

We present the application of a novel symbolic-regression-based algorithm, the QLattice®, on a selection of clinical omics datasets. This approach generates parsimonious high-performing models that can both predict disease outcomes and reveal putative disease mechanisms, demonstrating the importance of selecting maximally relevant and minimally redundant features in omics-based machine-learning applications. The simplicity and high-predictive power of these biomarker signatures make them attractive tools for high-stakes applications in areas such as primary care, clinical decision-making and patient stratification.

Availability and implementation.

The QLattice is available as part of a python package (Feyn®), which is available at the Python Package Index and can be installed via pip. The documentation provides guides, tutorials and the API reference. All code and data used to generate the models and plots discussed in this work can be found in https://github.com/abzu-ai/QLattice-clinical-omics.

doi: https://doi.org/10.1093/bioinformatics/btac405

This publication is open access.

Anyone can read, download, distribute, or cite this paper.

You can opt out at any time. We’re cookieless, and our privacy policy is actually easy to read.

Try the QLattice.

Experience the future of AI, where accuracy meets simplicity and explainability.

Models developed by the QLattice have unparalleled accuracy, even with very little data, and are uniquely simple to understand.

The QLattice

Share this publication.

The QLattice accelerates discoveries with explainable insights.​

Researchers and and scientists cite Abzu’s QLattice symbolic AI in industry-leading journals for introducing a new standard of performance and explainability to data sets.

Subscribe for
notifications from Abzu.

You can opt out at any time. We’re cookieless, and our privacy policy is actually easy to read.