NFT @ 09102026: A Graph Machine Learning Approach for Multi-Omics Representation Learning

#researchatnecstlab - #NECSTFridayTalk On Friday, October 9, 2026, we had a new talk for the series #NECSTFridayTalk. During this talk, we had, as speaker, Leonardo De Grandis, PhD student at Dipartimento di Elettronica, Informazione e Bioingegneria. In the following, you can find the details about the talk: 📌 Title: A Graph Machine Learning Approach for Multi-Omics Representation Learning 📌 Abstract: Graphs are a powerful way to represent heterogeneous biological entities and their interactions. As a result, Graph Machine Learning (GML) is increasingly used to study biological networks. This talk covers the key steps of applying GML in a multi-omics context, from raw data processing to model development. Since data aggregation and harmonization remain bottlenecks for large scale artificial intelligence applications, two methods for consistently collecting high-quality genomic variants and multi-omics data will be presented. Finally, two different approaches for multi-omics GML applications will be introduced: a tool for compound-protein interaction prediction leveraging graph matching networks and a framework for toxicity detection on RNA-seq data. #NECSTLab #Computerscience