Multi-omics integration: a multi-view learning problem

Due to the advent of high-throughput technologies, high-dimensional "omics" data are produced at an increasing pace. In cancer biology, national and international consortia have profiled thousands of tumors at multiple molecular levels ("multi-omics") allowing to gather a comprehensive molecular picture of this disease. Moreover, multi-omics profiling approaches are currently being transposed at single-cell resolution, further increasing the information accessible from cancer samples. The current main challenge is to design appropriate methods to integrate this wealth of information and translate it into actionable biological knowledge.

In this talk, I will discuss two maincomputational directions, both grounded on multi-view learning: (i) multilayer networks to integrate a large range of interactions and (ii) dimensionality reduction to extract biological knowledge simultaneously from multiple omics. First, I will present the development done by my team for  bulk data and then our ongoing research in single-cell.