Project description

Finding personalized treatment for High Grade Serous Ovarian Carcinoma (HGSOC) patients is a very challenging task. It requires patient data (clinical data, mutational profile, gene expression ) along with biological knowledge concerning drugs, actionable variants, and proteins or biological pathways targeted by drugs. However, the large amounts of data and knowledge require scalable approaches to providing actionable information to support clinicians in decision-making [1, 2].

To propose personalized actionable drugs, our group is developing a semantic knowledge graph (SKG) database to integrate all this heterogeneous data (https://github.com/oncodash/oncodashkb). This type of database represents medical and biological data in the form of objects and relationships [3,4]. With this type of structure, it is then possible to connect HGSOC patients to potential drugs into an explainable directed graph linking previously unconnected information. Next we aim to apply AI algorithms to the graph-structured data to extract paths of interest and to suggest drugs to clinicians that they might not have otherwise considered [5, 6].

This project is ideal for students interested in the intersection of systems biology, programming and graph-based learning algorithms. This project will provide hands-on experience working with knowledge graphs, a rapidly evolving technology, and evaluating various machine learning methods on graph structures.

Internship objectives

• ●  Integration of new data sources from cancer databases into the SKG database.

• ●  Contribute with researchers to evaluate different learning algorithms applied to the SKG and

  assess the contribution to prediction made by the integration of these different data sources.

Profile

• ●  Currently enrolled in a master’s program in Computer Science, Statistics, Computational Biology, or a related field.

• ●  Programming skills in python (cypher is a plus).

• ●  Knowledge in Machine Learning and Deep Learning and their frameworks (PyTorch).

• ●  Proficient in working with a Unix environment.

• ●  Knowledge of graph databases, such a Neo4j, is a plus.