Data science & engineering
Machine Learning for Biological Data
We develop and evaluate machine-learning models on biological data, with the validation discipline the data require: splits that respect patients, batches and homology, baselines the model must beat, and documentation of where it should not be used.
Who it is for
- Research groups with labelled omics, imaging-derived or molecular datasets
- Biotech teams evaluating whether ML adds value to a decision
- Groups who need an independent evaluation of an existing model
What you send us
- Labelled datasetFeatures or raw data with outcomes, and the grouping structure (patients, batches, families).
- Problem statementWhat decision the model supports and what an error costs.
What you receive
- Trained model and inference script
- Evaluation report with baselines and ablations
- Model card stating intended use and limits
- Reproducible training code and environment
Methodology
How the work runs
Each step is agreed with you and documented. Typical tools: scikit-learn · PyTorch · XGBoost · SHAP · pandas · RDKit
- Problem framingDefine the prediction target, the unit of independence and the metric that reflects the real decision.
- BaselinesSimple models and domain heuristics establish the performance any proposal must beat.
- ModellingFeature engineering and model selection under a nested, grouped validation scheme.
- EvaluationHeld-out testing, calibration, subgroup performance and ablations.
- Hand-overModel, inference code, evaluation report and a model card.
Quality controls
Checks applied to every project
- Splits respect patients, batches or sequence homology to prevent leakage
- Every model compared with a simple baseline
- Calibration and subgroup performance reported, not only overall accuracy
- Hyperparameters tuned inside the training folds only
Scope and limits
What the work can and cannot show
- If no model beats the baseline under a fair split, that is the result we report.
- Models are not validated for clinical use.
- Research use only: results are not a clinical or diagnostic report.
Free quote
Start a Machine project.
Describe your data and questions. We reply with questions or a written quote.
It depends on the data type, the number of samples and the depth of interpretation you need. Every project receives a written quote with scope, deliverables, price and delivery date after a short exchange about your data and questions. The quote is free and carries no obligation.
Yes. An independent evaluation checks the splits for leakage, compares the model with baselines and tests it on held-out data, and often costs less than building a new model.
Data are used only for your project, transferred through a channel you approve and deleted at the end of the engagement on request. We will sign your institution’s confidentiality agreement before any data are shared.
Yes. You receive a methods section with software versions and parameters, and every figure can be regenerated from the delivered code. Authorship or acknowledgement follows your field’s norms and is agreed at the start.
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