Research team
Expertise
I work as a Postdoctoral researcher in the field of Bioinformatics, studying how to detect metabolites through Mass Spectrometry Data using Machine Learning Algorithms. For this purpose, I use public datasets in order to train Deep Learning Models that are able to predict the similarity between molecules.
Deep Learning for Comprehensive Small Molecule Discovery From Untargeted Mass Spectrometry Data.
Abstract
Although small molecule mass spectrometry (MS) is a vital tool in various life sciences domains, its potential is hindered by the low annotation rate of MS/MS spectra, limiting our ability to uncover critical biological insights. This research project aims to revolutionize small molecule MS by harnessing the power of deep learning and multimodal integration to overcome this challenge. I will develop several complementary deep learning strategies for small molecule identification. First, I will develop a learned spectrum similarity score for the discovery of structurally related analogs. Second, I will use generative AI techniques to simulate comprehensive spectral libraries. Third, I will develop a solution for de novo molecule identification directly from MS/MS spectra, reducing the reliance on spectral libraries and expanding the range of discoverable molecules. Furthermore, I will introduce a holistic approach to MS by integrating three disparate data sources—MS/MS spectra, molecular structures, and natural language descriptions—into a shared latent space using multimodal representation learning. This paradigm shift will allow for direct linking of MS/MS observations to molecular structures and expert knowledge, enabling semantic search and retrieval of molecular information. Moreover, I will employ explainable AI techniques to interpret model decisions and provide insights into MS experimentation patterns.Researcher(s)
- Promoter: Bittremieux Wout
- Co-promoter: Laukens Kris
- Fellow: Piedrahita Giraldo Juan Sebastian
Research team(s)
Project type(s)
- Research Project