ArticleBMC bioinformatics2018
Phylogenetic convolutional neural networks in metagenomics.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- From trees to graphs: rethinking phylogeny in microbiome prediction.Applied and environmental microbiology · 2026Article
- Adaptive graph learning of microbial phylogeny enables accurate and interpretable microbiome-based host phenotype prediction.Applied and environmental microbiology · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Deconvolving Phylogenetic Distance Mixtures.bioRxiv : the preprint server for biology · 2026Article
- Review
- Phylo-Spec: a phylogeny-fusion deep learning model advances microbiome status identification.mSystems · 2025Article
- Zero-shot deep learning for the annotation of unknown eDNA sequences using co-occurrences and phylogenetic embeddings.PLoS computational biology · 2025Article
- Deep learning decodes species-specific codon usage signatures in Brassica from coding sequences.Scientific reports · 2025Article
- Emerging multifaceted roles of the microbiome in cancer susceptibility.World journal of clinical oncology · 2025Review
- Article
- Bioinformatic approaches to blood and tissue microbiome analyses: challenges and perspectives.Briefings in bioinformatics · 2025Review
- A Knowledge-Guided Large Language Model Framework for Microbiome-Based Disease Diagnosis.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.PloS one · 2025Article
- Multitask knowledge-primed neural network for predicting missing metadata and host phenotype based on human microbiome.Bioinformatics advances · 2025Article
- DeepPhylo: Phylogeny-Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Deep learning for predicting 16S rRNA gene copy number.Scientific reports · 2024Article
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Gut microbiome-metabolome interactions predict host condition.Microbiome · 2024Article
- PM-CNN: microbiome status recognition and disease detection model based on phylogeny and multi-path neural network.Bioinformatics advances · 2024Article
- A comprehensive analysis and performance evaluation for osteoporosis prediction models.PeerJ. Computer science · 2024Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundConvolutional 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.
resultsPh-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.
conclusionPh-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.
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