This project will address the question of how to leverage clinical data to improve our ability to predict which cancer patients will respond to a drug treatment. The CONSORE project within IPC has completed the structuration of electronic health records from clinical practice. As a result, we now count with a large volume of structured data from the IPC (currently 258,168 patient records). The successful candidate will investigate and implement predictive models exploiting these data resources.
We are looking for a highly motivated and diligent student to carry out her/his M2 master project in our group. The ideal candidate will be comfortable writing code in Python and/or R, with prior exposure to supervised learning algorithms. Additional background on tumour molecular profiling and/or biomarker discovery will be an advantage. The project will be building upon our recent work on predicting in vivo tumour response to cancer treatments (https://www.biorxiv.org/content/early/2018/12/04/277772).
We are looking for a highly motivated and diligent student to carry out her/his M2 master project in our group. The project requires some knowledge of python programming and machine learning. Additional background on chemical informatics and/or structural bioinformatics will be an advantage. The project will be building upon recent work on predicting the binding of small molecules to proteins with applications to structure-based drug design (https://www.nature.com/articles/srep46710).