  Background: Convolutional Neural Networks can be effectively used only when
data are endowed with an intrinsic concept of neighbourhood in the input space,
as is the case of pixels in images. We introduce here Ph-CNN, a novel deep
learning architecture for the classification of metagenomics data based on the
Convolutional Neural Networks, with the patristic distance defined on the
phylogenetic tree being used as the proximity measure. The patristic distance
between variables is used together with a sparsified version of
MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space.
Results: Ph-CNN is tested with a domain adaptation approach on synthetic data
and on a metagenomics collection of gut microbiota of 38 healthy subjects and
222 Inflammatory Bowel Disease patients, divided in 6 subclasses.
Classification performance is promising when compared to classical algorithms
like Support Vector Machines and Random Forest and a baseline fully connected
neural network, e.g. the Multi-Layer Perceptron. Conclusion: Ph-CNN represents
a novel deep learning approach for the classification of metagenomics data.
Operatively, the algorithm has been implemented as a custom Keras layer taking
care of passing to the following convolutional layer not only the data but also
the ranked list of neighbourhood of each sample, thus mimicking the case of
image data, transparently to the user. Keywords: Metagenomics; Deep learning;
Convolutional Neural Networks; Phylogenetic trees
