Disease understanding: Dealing with complex and unstructured big data in biomedical domain

Outline

Big Data is a paradigm where large amounts of diverse data can be available to help and improve the decision-making process. In the biomedical field, this can be applied to very different scenarios, from the analytical predictive models to diagnose diseases to the ones that are more focused on helping with administrative tasks or reducing the number of visits to the hospital. In biology, machine learning has led to many successful cases and areas, such as the AlphaFold project for protein structure prediction.

In this seminar, a brief introduction to medical informatics and why it is important and relevant to apply computer science to medicine will be provided. We will navigate to the area of knowledge representation, and deep into the question about how big data can be used, specifically, to improve our understanding of diseases, and more concretely, how this knowledge can be used to create potential hypotheses for drug repurposing using a computational perspective.

Assessment Method

Attendance and participation. Short assignment

Lective hours

Timetable

• 10 February, 15:00-18:00

Lecture Theatre

• A-6305

Tuition Language

English.