Scientific research is shedding light on the interaction of the gut microbiome with the human host and on its role in human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. Most of them leverage shotgun metagenomic sequencing to extract gut microbial species-relative abundances or strain-level markers. Each of these gut microbial profiling modalities showed diagnostic potential when tested separately; however, no existing approach combines them in a single predictive framework. Here, we propose the Multimodal Variational Information Bottleneck (MVIB), a novel deep learning model capable of learning a joint representation of multiple heterogeneous data modalities. MVIB achieves competitive classification performance while being faster than existing methods. Additionally, MVIB offers interpretable results. Our model adopts an information theoretic interpretation of deep neural networks and computes a joint stochastic encoding of different input data modalities. We use MVIB to predict whether human hosts are affected by a certain disease by jointly analysing gut microbial species-relative abundances and strain-level markers. MVIB is evaluated on human gut metagenomic samples from 11 publicly available disease cohorts covering 6 different diseases. We achieve high performance (0.80 < ROC AUC < 0.95) on 5 cohorts and at least medium performance on the remaining ones. We adopt a saliency technique to interpret the output of MVIB and identify the most relevant microbial species and strain-level markers to the model’s predictions. We also perform cross-study generalisation experiments, where we train and test MVIB on different cohorts of the same disease, and overall we achieve comparable results to the baseline approach, i.e. the Random Forest. Further, we evaluate our model by adding metabolomic data derived from mass spectrometry as a third input modality. Our method is scalable with respect to input data modalities and has an average training time of < 1.4 seconds. The source code and the datasets used in this work are publicly available.

Scientific research is shedding more and more light on the interaction of the gut microbiome with the human body and its role in human health. Empirical evidence shows that the microbiome is strongly interconnected with the host immune system and can contribute to carcinogenesis. Furthermore, studies show that it can be used as a predictor for various diseases. In this work, we propose the Multimodal Variational Information Bottleneck (MVIB), a novel machine learning model capable of learning a joint representation of multiple heterogneous data modalities. Adopting an information theoretic interpretation of deep neural networks, MVIB allows to compute a joint stochastic encoding of the input data modalities, which acts as a minimal sufficient statistics of the inputs for the prediction of a target label. In a supervised learning setting, we use the MVIB to predict whether patients are affected by a certain disease by jointly analising the species-relative abundance and strain-level marker profiles extracted from shotgun metagenomic sequencing of microbial DNA. We propose various pre-processing methods for the marker profiles and validate our model on ten disease datasets. Additionaly, we perform transfer learning experiments by fine-tuning the model on the target disease dataset after a pre-training on all other source diseases. Furthermore, we adopt a saliency technique to interpret the outcome of the model classification and identify which microbial species and strain-level markers mostly contributed to a certain prediction. We show that MVIB achieves competitive results on the microbiome-based disease prediction task. The code for this work is publicly available at https://github.com/nec-research/microbiome-mvib.