MODIS: Multi-Omics Data Integration for small and unpaired datasets

Daniel Lepe-Soltero, Thierry Artières, Anaïs Baudot, Paul Villoutreix

Track: Full Paper Track

Keywords: Multi-omics data integration, transfer learning, class imbalance, cross-modal auto encoders, rare diseases

Abstract: A key challenge today lies in the ability to efficiently handle multi-omics data since such multimodal data may provide a more comprehensive overview of the underlying processes in a system. Yet it comes with challenges: multi-omics data are most often unpaired and only partially labeled, moreover only small amounts of data are available in some situation such as rare diseases. We propose MODIS which stands for Multi-Omics Data Integration for Small and unpaired datasets, a semi supervised approach to account for these particular settings. MODIS learns a probabilistic coupling of heterogeneous data modalities and learns a shared latent space where modalities are aligned. We rely on artificial data to build controlled experiments to explore how much supervision is needed for an accurate alignment of modalities, and how our approach enables dealing with new conditions for which few data are available. The code is available at https://github.com/VILLOUTREIXLab/MODIS.

Attendance: Daniel Lepe-Soltero

Submission Number: 95