About her PhD project

Mitochondria are essential organelles, which produce cellular energy in the form of ATP. They are also involved in many other cellular processes (1) and functionally adapt to cellular needs. In this project, we will combine deep learning with mitochondrial computational biology to develop "mito-learner", a deep learning based algorithm for predicting mitochondrial functional states of tumors (taken from the TCGA database (2)) based on mito-gene expression states. We will test different visual representations of mito-gene expression data for classification of samples with image-based deep learning algorithms. In parallel, we explore the possibility to use graph neural network methods (3) with the aim of encoding pairwise gene interactions in the most general manner. We will focus on 1200 mito-genes, using functional information as provided by the annotated mitoXplorer mito-interactomes (4), for classifying mitochondrial states of tumor samples. Ultimately, mito-learner will be implemented in the mitoXplorer platform for classifying mitochondrial states of user-provided data.