Identifying interactions in omics data for clinical biomarker discovery using symbolic regression.
Bioinformatics
; 38(15): 3749-3758, 2022 08 02.
Article
em En
| MEDLINE
| ID: mdl-35731214
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 (https//pypi.org/project/feyn/) and can be installed via pip. The documentation provides guides, tutorials and the API reference (https//docs.abzu.ai/). 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. SUPPLEMENTARY INFORMATION Supplementary material is available at Bioinformatics online.
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Software
/
Pesquisa Biomédica
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article