Multiclass Disease Classification from Microbial Whole-Community Metagenomes using Graph Convolutional Neural Networks.
Khan, S.; Kelly, L.
Show abstract
There is a wealth of information contained within ones microbiome regarding their physiology and environment, and this is a promising avenue for developing non-invasive diagnostic tools. Here, we utilize 5643 aggregated, annotated whole-community metagenomes from 19 different diseases to implement the first multiclass microbiome disease classifier of this scale. We compared three different machine learning models: random forests, deep neural nets, and a novel graph convolutional architecture which exploits the graph structure of phylogenetic trees as its input. We show that the graph convolutional model outperforms deep neural nets in terms of accuracy (achieving 75% average test-set accuracy), receiver-operator-characteristics (92.1% average AUC), and precision-recall (50% average AUPR). Additionally, the convolutional nets performance complements that of the random forest, achieving similar accuracy but better receiver-operator-characteristics and lower area under precision-recall. Lastly, we are able to achieve over 90% average top-3 accuracy across all of our models. Together, these results indicate that there are predictive, disease specific signatures across microbiomes which could potentially be used for diagnostic purposes.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Decoding the Language of Microbiomes: Leveraging Patterns in 16S Public Data using Word-Embedding Techniques and Applications in Inflammatory Bowel Disease 94%
- PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration 93%
- MiMeNet: Exploring Microbiome-Metabolome Relationships using Neural Networks 93%
Similar papers in this journal
- CaLMPhosKAN: Prediction of General Phosphorylation Sites in Proteins via Fusion of Codon Aware Embeddings with Amino Acid Aware Embeddings and Wavelet-based Kolmogorov Arnold Network 93%
- Multi-Omic Graph Diagnosis (MOGDx) : A data integration tool to perform classification tasks for heterogeneous diseases 93%
- PiDeel: Pathway-informed deep learning model for survivalanalysis and pathological classification of gliomas 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.