Health and life sciences
Machine learning for life sciences
Machine learning in the life sciences classifies cells in microscopy images, finds structure in gene and protein expression data, and predicts experimental outcomes from assay measurements. The strongest projects tie each prediction to a measured endpoint and test it on new biological samples.
How machine learning is used in the life sciences
Segmenting cells in microscopy images
A trained network outlines every cell or nucleus in an image so that shape, count and intensity become measurements. This replaces hand-tuned thresholds that break when imaging conditions change.
Real exampleCellpose (Stringer et al., 2021)A generalist deep learning segmentation method trained on a varied dataset of over 70,000 segmented objects that segments cells across many image types without retraining or parameter adjustment.
Finding structure in expression data
Unsupervised models compress thousands of measured genes per cell into a few dimensions, revealing groups and gradients. Downstream analysis then compares those groups between conditions.
Real examplescVI (Lopez et al., 2018)A deep generative framework for single-cell transcriptomics that models technical noise and batch effects and supports clustering, visualisation and differential expression.
Tracking animal behaviour in video
Pose estimation networks mark body parts in each video frame after a researcher labels a few hundred frames. The resulting coordinates turn hours of video into quantitative behaviour data.
Real exampleDeepLabCut (Mathis et al., 2018)A markerless pose estimation toolbox using transfer learning that tracked user-defined body parts across species and behaviours, reaching tracking performance comparable to human accuracy with around 200 labelled frames.
Predicting protein structures at scale
Structure prediction models give a three-dimensional model for almost any sequence, which biologists use to form hypotheses about function, binding sites and the effect of mutations.
Real exampleAlphaFold Protein Structure DatabaseAn open database from EMBL-EBI and Google DeepMind that provides predicted protein structures with per-residue confidence for browsing and bulk download.
Can you learn machine learning for the life sciences?
Yes, and most life scientists already have the hardest part, which is knowing what the data means. Learn Python and data handling, then experimental statistics, then classical machine learning, adding deep learning for images, video or omics. Expect the first gains to come from better validation rather than fancier models.
- Python and data handlingMove analyses out of spreadsheets into pandas so plates, animals and conditions stay linked to every measurement.Learn it in Python
- Statistics and experimental designLearn replicates, grouped validation and effect sizes, because repeated measurements of the same animal or plate are not independent.Learn it in Statistics
- Classical machine learningFit regression and classification models on assay and image features, and check them against a sensible baseline.Learn it in Machine Learning
- Deep learning for images and videoUse pretrained segmentation and pose models before training your own, since both work well on small labelled sets.Learn it in Deep Learning
- Reproducible analysisKeep code, environment and data versions together so a result can be rerun months later by someone else.
Moving into machine learning in the life sciences
If you already work in the field
Biologists already know the experiment, the controls and the failure modes of their instruments. Add Python, statistics and model evaluation, then automate one measurement you currently do by hand and validate it against your manual counts.
If you come from data science or ML
Data scientists should learn how experiments are designed, what a replicate is and why sample sizes are small. Partner with a laboratory, read their protocols, and build validation that respects animals, plates and donors rather than shuffling rows.
Roles that do this workComputational biologistBioimage analystData scientist, life sciencesBioinformatics scientist
Tools you will use
| Tool | What you use it for |
|---|---|
| scikit-learn | Fit classifiers with grouped cross-validation so repeated measurements stay in one fold. |
| statsmodels | Fit mixed-effects and regression models that account for replicates and batches. |
| CellProfiler | Segment cells in microscopy images and measure features for downstream classification. |
| Scanpy | Analyse single-cell expression data, from quality control to clustering. |
Public data to practise on
- Mice Protein Expression (UCI): Protein expression levels from control and Down syndrome model mice, with repeated measurements per mouse and class labels.
- Broad Bioimage Benchmark Collection: Annotated microscopy image sets for practising cell counting, segmentation and phenotype classification.
Your first project
Download the UCI Mice Protein Expression dataset, predict the experimental class with a random forest, and compare a random split with a split that holds out whole mice using accuracy.
Common questions
How much data do you need for a machine learning project in biology?
Less than people expect for image tasks, because pretrained models need only tens to hundreds of labelled examples. Tabular experiments need enough independent samples, not just repeated measurements of the same subject.
Should a biologist learn Python or R first?
Python if the goal is machine learning, image analysis or pipelines; R if the work is mainly statistics and expression analysis. Many laboratories use both and the concepts transfer.
Why does a model that works in one laboratory fail in another?
Different instruments, protocols and operators shift the data. Testing on samples collected separately, ideally by another laboratory, is the only way to see whether the model learned biology.
Do you need a GPU?
Not for tabular work. Image, video and omics deep learning benefit from one, and free or institutional cloud GPUs are usually enough for a first project.
Courses for these skills
- Practical Machine Learning in PythonStudy regularisation and grouped classification.51 published lessons · more lessons in production
- Deep Learning with PyTorchBuild neural representation and classification foundations.Syllabus available · lessons open as produced
- Data Tools: NumPy, pandas, Matplotlib and scikit-learnPreserve sample identifiers and pipeline metadata.23 published lessons · all planned lessons open
- Statistics and Probability for Machine LearningAssess dependence and the limits of an experimental result.41 published lessons · all planned lessons open
New to programming or maths? Start with Python, or follow the machine learning path in order. Published lessons are free to read; compare Free and Pro.