AI for Biology
Machine intelligence for life science — deep learning, transformers, graph neural networks and LLMs applied across biology.
What this area is.
AI is core to how we read biology, not a bolt-on. We apply deep learning to predict variant effects, transformers and language models to learn directly from biological sequences, and graph neural networks to reason over gene and pathway graphs.
We also use large language models to surface literature evidence and support clinical interpretation — and we explain how these models actually work, the recurring theme of our channel.
Tools & technologies
What we do.
Core methods we apply in ai for biology.
Variant effect prediction
Deep models (AlphaMissense-style) ranking pathogenicity at scale.
Sequence transformers
Language models over DNA, RNA and protein.
Graph neural networks
Learning over gene, protein and pathway graphs.
LLMs for genomics
Evidence retrieval, annotation and interpretation.
Variant prioritisation
Ranking causal and actionable variants.
Explainability
Making model decisions interpretable for biology.
From data to insight.
How a ai for biology project flows end to end.
Data
sequences · graphs · text
Represent
embeddings
Model
transformers · GNNs
Predict
effects · priorities
Explain
attributions
Clinic
interpretation support
Publication-grade figures.
Interactive, live-rendered visualisations used in ai for biology.
Where we go deep.
Variant effect prediction
From raw variants to ranked, interpretable pathogenicity.
LLMs in the clinic
Language models that surface and summarise evidence.
The science behind AI
How modern models work — explained, not black-boxed.
Questions we answer.
A few of the things people ask about ai for biology — and our short answers. Ask CGB-AI for more.
Is AI replacing analysis?
No — it augments it. Models prioritise and predict; scientists interpret and decide. We keep humans in the loop and models explainable.
Why graph neural networks?
Biology is networks. GNNs reason over gene, protein and pathway graphs the way sequence models reason over text.
Publications in AI for Biology.
Drawn from our full record of 173 papers, filtered to this area.
Start a ai for biology project.
Tell us the biological question and the data you have — we will map out an approach.