  Recent advances in high-throughput sequencing technologies have enabled the
extraction of multiple features that depict patient samples at diverse and
complementary molecular levels. The generation of such data has led to new
challenges in computational biology regarding the integration of
high-dimensional and heterogeneous datasets that capture the interrelationships
between multiple genes and their functions. Thanks to their versatility and
ability to learn synthetic latent representations of complex data, deep
learning methods offer promising perspectives for integrating multi-omics data.
These methods have led to the conception of many original architectures that
are primarily based on autoencoder models. However, due to the difficulty of
the task, the integration strategy is fundamental to take full advantage of the
sources' particularities without losing the global trends. This paper presents
a novel strategy to build a customizable autoencoder model that adapts to the
dataset used in the case of high-dimensional multi-source integration. We will
assess the impact of integration strategies on the latent representation and
combine the best strategies to propose a new method, CustOmics
(https://github.com/HakimBenkirane/CustOmics). We focus here on the integration
of data from multiple omics sources and demonstrate the performance of the
proposed method on test cases for several tasks such as classification and
survival analysis.
