Large Deep Generative Graph Models for Biomedicine
Duration: Six months.
Host organisation: Epistemic AI
Location: Remote
Point of contact: Stefano Pacifico, Co-founder & CEO
linkedin.com/in/stefanopacifico
Biomedical knowledge is vast, interconnected and growing each day and advances in the Science are driven by drawing upon this plethora of information. For example, we might be able to infer that a drug that was originally designed to tackle depression is also effective at treating neuralgia.
Here at Epistemic AI we encode this information as a Knowledge Graph (KG), containing billions of entries. This project addresses the task of taking our KG (or a part of it) and using deep learning to represent it as graph embeddings. Once we have the right representation, we can go on to make discoveries, uncovering connections that were previously hidden to the community or are even novel. We are agnostic regarding programming languages and frameworks (Python and PyTorch would be good candidates). No biomedical knowledge is required. Instead, some machine learning and data handling skills would be useful, within a Linux environment. The project will be heavily hands-on and practical.
The idea of the project is to create embeddings over time series data, where the data is appropriately released clinical records for HIV patients. With these embeddings in hand, we will then be in a position to start reasoning about patients, both within and across the data sets. For a given person, with a record of treatments and symptoms, their time series (temporal embeddings) could be inspected and clinical events might be discovered. By repeating this for cohort of patients, it becomes possible to look into more generalised patterns, allowing clinicians to draw medical inferences about patients. Here the IP would be the model itself, but the problem setting and exploration would potentially lead to a paper.
There is considerable flexibility with this project and we expect that it will evolve over time. Outcomes of the project will be research and development within a startup environment, software engineering skills, presentation of ideas and discussions and collaborations with subject matter experts here at EAI. We might even create new products and make Scientific discoveries!
Further, we would welcome any candidate with independent project proposals for Epistemic AI, in addition to the current description.
Core competences
- Python
- PyTorch (desirable)
- Machine learning and data handling skills
Personal attributes and qualities
- Excellent communication skills
- Motivated team player
- Practical and hands-on approach to problem solving and projects
Beneficial but not essential
- Understanding of the pharmaceutical industry
About Epistemic AI (EAI)
Epistemic AI was founded in 2018 to provide efficient and easily accessible AI solutions for the life sciences. The state-of-the-art AI platform eliminates common technology barriers in research and enables rapid knowledge discovery. The Epistemic AI platform effectively unlocks silos of information that can potentially deliver better cures. Epistemic AI works with academia, foundations, and biopharma companies to help advance their efforts in R&D, drug discovery, clinical trials, and commercialization. To learn more, visit epistemic.ai, or contact at info@epistemic.ai.
Deadline: 5pm on Friday 14th July
