Network reconstruction and machine learning approaches to model tumour relapse in breast cancer

PhD opportunity in the CDT AI for Healthcare, under the supervision of Dr Barbara Bravi (Department of Mathematics) and Prof Luca Magnani (Surgery and Cancer).

Short project description:

Motivation:

Estrogen receptor positive breast cancer has an immense social and economic cost. Despite short-term survival has remarkably improved since the introduction of adjuvant endocrine therapies, patients with this type of cancer can relapse for many years after the initial diagnosis. Actually, endocrine therapies induce a state of cancer cell dormancy, which invariably ends in awakening, hence tumour relapse. The underlying mechanisms are far from being understood. Our aim is to develop machine learning tools to elucidate such mechanisms and infer cell state trajectories along the dormancy-awakening transition from longitudinal, lineage-specific transcriptional datasets of breast cancer cells.

Project Content:

The project involves the development of statistical and machine learning approaches to reconstruct the phenotypical trajectories of cells during relapse from high-throughput transcriptomic datasets. The tools we will employ include generative machine learning and belief propagation algorithms.

For more information on the project, write to [Email Address Removed]

To apply: visit the webpage https://ai4health.io/apply/ and follow the instructions.

Only students who are eligible for Home/UK fees can apply. Applications are open until the place is filled, so, if you are interested, apply as soon as possible.