Background: Large Foundation models (LFMs) are deep learning models that are trained on broad collections of text, images or other data and act as the basis behind many current artificial intelligence applications, such as ChatGPT. Recently, LFMs have also shown promising results in biology, drawing parallels between language (word sequences) and cells (gene and protein sequences). LFMs can also capture and make predictions on gene regulatory relationships in a context (e.g. cell type, tissue, development stage, etc). In addition, transfer learning allows LFMs to be repurposed for different tasks through finetuning with only minimum computational effort and training data required. Using LFMs to decipher gene regulations in human has shown great potential in perturbation studies, including gene deletions1-4. This project will leverage this powerful cutting-edge technology by generating LFMs for fungal gene regulatory networks using public -omics data and through transfer learning, predicting essential genes.
Objectives: (1) Develop computational models that capture the complex gene regulations of fungal species; (2) Use public condition-dependent gene essentiality data to finetune models to identify genes crucial for pathogen control; (3) Validate the model predictions using unseen Syngenta gene essentiality data.
This 4 year studentship opportunity is open to UK students and provides funding to cover stipend, UK tuition fees and consumable/travel costs.
Students must meet the eligibility criteria as outlined in the UKRI guidance on UK and international candidates. Applicants will have a first-class honours degree in a relevant subject or a 2.1 honours degree plus Masters (or equivalent).
This project is based at the Dundee site of the James Hutton Institute, UK.