Multi-omics analysis aims at extracting previously uncovered biological knowledge by integrating information across multiple single-omic sources. Past approaches have focused on the simultaneous analysis of a small number of omic data sets. Current challenges face the problem of integrating multiple omic sources into a unified complex model, or of combining already available tools for two-by-two omics analyses and merging their outcomes. By doing so and leveraging integrated system-level knowledge, multi-omic approaches ought to enable the development of better qualitative and quantitative models for descriptive and predictive analyses. To move this area forward, new statistical and algorithmic frameworks are needed, for example for generalizing classical graph theory results to heterogeneous networks and applying them to diverse problems such as drug repurposing or understanding the immune response to infections. Thus, in short, this workshop aims at investigating novel methodologies for providing crucial insights into multi-omics data management, integration, and analysis in order to enable biological discoveries.
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