Biomedical AI
Machine learning, deep learning and AI applied to biological and clinical data, drug property prediction, molecular AI, medical image analysis, biomedical NLP and clinical outcome modelling.
AI for Life Sciences
We apply AI methods that are validated and appropriate to the biological question, not general-purpose ML applied indiscriminately to biological data.
Molecular Property Prediction
ML models for predicting molecular properties, ADMET parameters, binding affinity, toxicity, solubility, and bioactivity. Graph neural networks for molecular representation and property prediction using DeepChem and PyTorch Geometric.
Drug Response Prediction
Predicting drug sensitivity and resistance from genomic profiles, integrating gene expression, mutation data and drug-target interaction networks to build predictive models for treatment response.
Biomarker Discovery AI
Feature selection and ML-based biomarker discovery from high-dimensional omics datasets, identifying minimal feature sets with predictive power for disease classification, progression or treatment outcome.
Biomedical NLP
Natural language processing for biomedical literature, information extraction, entity recognition (genes, diseases, drugs), relation extraction and clinical note analysis using transformer-based models fine-tuned on biomedical corpora.
Medical Image Analysis
Convolutional neural networks and vision transformers for medical image classification, segmentation and feature extraction, histopathology slides, radiology images (X-ray, CT, MRI) and microscopy data.
Clinical Outcome Modelling
Predictive models for clinical outcomes, disease progression, treatment response, readmission risk, survival prediction, built from structured clinical data with appropriate handling of censored observations and class imbalance.
AI Done Right for Biology
Biology presents unique challenges for AI. Our approach addresses them directly.
Domain-Appropriate Features
We choose molecular representations and feature engineering based on the biology of the prediction task, not generic tabular features. Molecular fingerprints, graph representations and structured biological features.
Small Data Strategies
Biomedical datasets are often small. We apply transfer learning, data augmentation, regularisation and Bayesian approaches to build reliable models even with limited labelled examples.
Interpretable AI
Black-box predictions have limited scientific value. We use SHAP, LIME, attention weights and feature importance to make model decisions interpretable in biological terms.
Have a biological prediction problem?
Tell us about your data, your biological question and the output you need, we'll propose an appropriate ML approach.