CS-E4880 - Machine Learning in Bioinformatics D, Lecture, 3.3.2023-2.6.2023

Speaker: Elina Francovic-Fontaine, Laval University, Canada

Time and place: 15.5.2023 at 14:15, seminar room T3 Aalto CS Building.

Abstract:

Machine learning needs to be made accessible for metabolomics scientists to help with tasks like biomarkers discovery. This presentation will introduce the Metabolomics Dashboard for Interpretable Classification (MeDIC), aiming at facilitating the access to machine learning for non-domain experts. Our work focuses on providing the scientific community with a sound and straightforward tool to ease the analysis of untargeted liquid-chromatography-mass spectrometry metabolomic data. We propose the MeDIC to perform metabolomics data analyses relying on machine learning tools. It allows the extraction of relevant features from a pool of interpretable classifiers to perform biomarker discovery. Alongside the pure machine learning study, it provides an extensive result analysis pipeline, allowing non domain experts to understand the inner mechanisms of the algorithms. 

Bio :

Elina is a Ph.D. student from Laval University in Canada. She completed her Bachelor's degree in bioinformatics. As an undergraduate student, she developed a strong interest in machine learning through her internships. For her Master's degree, she focused on the application of machine learning to metabolomics data and the creation of a pipeline with a web-based interface to produce and analyse machine learning experiments on mass spectrometry metabolomics data. For her Ph.D, she furthers the development of the tool by working on developing multi-omics algorithms focusing on the integration of metabolomics with other omics.