Title: Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer,
Abstract: Convolutional Neural Networks (CNNs) are a popular deep learning architecture
widely 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 neighbourhood
function) in the input feature space has so far prevented its direct use on
data 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 a
convolutional based deep learning framework, e.g., for prediction. We propose a
generalized solution for CNNs on omics data, implemented through a dedicated
Keras layer. In particular, for metagenomics data, a metric can be derived from
the patristic distance on the phylogenetic tree. For transcriptomics data, we
combine Gene Ontology semantic similarity and gene co-expression to define a
distance; the function is defined through a multilayer network where 3 layers
are defined by the GO mutual semantic similarity while the fourth one by gene
co-expression. As a general tool, feature distance on omics data is enabled by
OmicsConv, a novel Keras layer, obtaining OmicsCNN, a dedicated deep learning
framework. Here we demonstrate OmicsCNN on gut microbiota sequencing data, for
Inflammatory Bowel Disease (IBD) 16S data, first on synthetic data and then a
metagenomics collection of gut microbiota of 222 IBD patients.