Phylogenetic convolutional neural networks in metagenomics

• Published in: BMC bioinformatics Vol. 19; no. S2; pp. 49 - 13
• Main Authors: Fioravanti, Diego, Giarratano, Ylenia, Maggio, Valerio, Agostinelli, Claudio, Chierici, Marco, Jurman, Giuseppe, Furlanello, Cesare
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.03.2018; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Computational biology; Convolutional neural networks; Data Analysis; Databases, Genetic; Deep learning; Genomics; Humans; Inflammatory Bowel Diseases - genetics; Innovations; Metagenomics; Neural Networks (Computer); Phylogenetic trees; Phylogeny; Principal Component Analysis; Reproducibility of Results; Support Vector Machine; Deep learning; Metagenomics; Phylogenetic trees; Convolutional neural networks
• DOI: 10.1186/s12859-018-2033-5
• ISSN: 1471-2105

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. 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. 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.