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Convolutional Neural Networks (CNNs) are a popular deep learning architecturewidely applied in different domains, in particular in classifying over images,for which the concept of convolution with a filter comes naturally.Unfortunately, the requirement of a distance (or, at least, of a neighbourhoodfunction) in the input feature space has so far prevented its direct use ondata types such as omics data. However, a number of omics data are metrizable,i.e., they can be endowed with a metric structure, enabling to adopt aconvolutional based deep learning framework, e.g., for prediction. We propose ageneralized solution for CNNs on omics data, implemented through a dedicatedKeras layer. In particular, for metagenomics data, a metric can be derived fromthe patristic distance on the phylogenetic tree. For transcriptomics data, wecombine Gene Ontology semantic similarity and gene co-expression to define adistance; the function is defined through a multilayer network where 3 layersare defined by the GO mutual semantic similarity while the fourth one by geneco-expression. As a general tool, feature distance on omics data is enabled byOmicsConv, a novel Keras layer, obtaining OmicsCNN, a dedicated deep learningframework. Here we demonstrate OmicsCNN on gut microbiota sequencing data, forInflammatory Bowel Disease (IBD) 16S data, first on synthetic data and then ametagenomics collection of gut microbiota of 222 IBD patients.

Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer