Overview
Sound statistics make results credible. We design the analysis around your question and report assumptions openly.
Salient features
- Appropriate tests with effect sizes
- Machine learning with honest validation
- ODE and rule-based dynamic models
Analyses we offer
Predictive Modeling
Builds and validates machine learning models for classification and regression on biological or chemical data, with honest cross-validation.
Methods: scikit-learn, xgboost, deepchem.
Statistical tests
Selects and applies appropriate parametric and non-parametric tests, with assumption checks, effect sizes and corrected p-values.
Methods: scipy, statsmodels, pingouin.
Mathematical modeling
Builds ODE and rule-based models of biochemical systems, fits them to data and simulates perturbations.
Methods: pysb, COPASI, tellurium.
What you receive
- A written report with methods, parameters and interpretation
- Processed data tables and publication-quality figures
- Scripts and settings so every result can be reproduced
Not sure which analysis fits your data? Contact us and we will advise.