Abstract
BackgroundOne of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients.ResultsWe propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models.ConclusionWe demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data.ReviewersThis article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.
Highlights
One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced
We propose a network-based method to integrate omics data, which relies on the topological properties of networks generated from the omics data
The topological strategy performs significantly better than the classical strategy for five of the six comparisons, and the average gain in balanced accuracy ranges from 5% to 12%
Summary
One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. High-throughput technologies have been massively used to study various diseases in order to decipher the underlying biological mechanisms and to propose novel therapeutic strategies Initiatives such as The Cancer Genome Atlas have produced and made publicly available a huge amount of omics data from thousands of human samples. These data often correspond to measurements of different biological entities (e.g., transcripts, proteins), represent various views on the same entity (e.g., genetic, epigenetic) and are obtained through different technologies (e.g., microarray, RNA-sequencing). As an example, such network-based approaches have been used to build brain region-specific
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