PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype From Metagenomic Data

Reiman, Derek; Metwally, Ahmed A.; Sun, Jun; Dai, Yang

doi:10.1109/jbhi.2020.2993761

Cited by 63 publications

(77 citation statements)

References 36 publications

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“…PopPhy-CNN achieves a 2% higher ROC AUC on Cirrhosis, but is outperformed by MVIB on C-T2D. In Table 2 the standard error for the PopPhy-CNN is omitted as not provided in [18].…”

Section: Mvib Achieves Competitive Results On the Multimodal Microbiome-based Disease Prediction Taskmentioning

confidence: 99%

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Microbiome-based disease prediction with multimodal variational information bottlenecks

Siarheyeu¹,

G²,

A³

2021

Preprint

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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.

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“…PopPhy-CNN achieves a 2% higher ROC AUC on Cirrhosis, but is outperformed by MVIB on C-T2D. In Table 2 the standard error for the PopPhy-CNN is omitted as not provided in [18].…”

Section: Mvib Achieves Competitive Results On the Multimodal Microbiome-based Disease Prediction Taskmentioning

confidence: 99%

“…[17] targets the prediction of cardiovascular disease by using supervised learning on taxonomic features (microbial taxa). PopPhy-CNN [18] represents the microbial phylogenetic tree in a matrix together with the relative abundance of the microbial taxa and operates disease prediction with a convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 99%

Microbiome-based disease prediction with multimodal variational information bottlenecks

Siarheyeu¹,

G²,

A³

2021

Preprint

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show abstract

“…Therefore, as limited with the sample size and the interpretability of deep learning models, only few studies in intestinal disease diagnosis by cfDNA 36 or microbiome 37 adopted those methods. However, the recent successes in deep learning do shed light on its promising ability 38–40 . Therefore, we are still optimistic with its involvement in intestinal disease diagnosis in the future.…”

Section: Machine Learning Algorithms Of Diagnosis In Intestinal Diseasesmentioning

confidence: 99%

Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome

Zhang

Chen

Wong

2021

J of Gastro and Hepatol

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The maturing development in artificial intelligence (AI) and genomics has propelled the advances in intestinal diseases including intestinal cancer, inflammatory bowel disease (IBD), and irritable bowel syndrome (IBS). On the other hand, colorectal cancer is the second most deadly and the third most common type of cancer in the world according to GLOBOCAN 2020 data. The mechanisms behind IBD and IBS are still speculative. The conventional methods to identify colorectal cancer, IBD, and IBS are based on endoscopy or colonoscopy to identify lesions. However, it is invasive, demanding, and time‐consuming for early‐stage intestinal diseases. To address those problems, new strategies based on blood and/or human microbiome in gut, colon, or even feces were developed; those methods took advantage of high‐throughput sequencing and machine learning approaches. In this review, we summarize the recent research and methods to diagnose intestinal diseases with machine learning technologies based on cell‐free DNA and microbiome data generated by amplicon sequencing or whole‐genome sequencing. Those methods play an important role in not only intestinal disease diagnosis but also therapy development in the near future.

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“…This is motivated by the findings that a microbial signature for the host phenotype may be complex, involving simultaneous over-and under-representations of multiple microbial taxa potentially interacting with each other. Classical ML models, such as Random Forest (RF), Logistic Regression and Support Vector Machines (SVMs), and deep neural networks (DNNs) have been applied to host phenotype prediction using microbial abundance features [16][17][18][19][20][21][22] .…”

Section: Introductionmentioning

confidence: 99%

“…While the ML approaches demonstrated promising results on host phenotype prediction 23,24 , it is a challenging task for users to determine what is the best ML model and how many features are needed in order to achieve robust prediction, especially on external validation datasets. In addition, each ML algorithm may generate different feature importance rankings 12,22,25 , complicating the decision on a consistent and informative signature for the host phenotype of interest.…”

Section: Introductionmentioning

confidence: 99%

Meta-Signer: Metagenomic Signature Identifier based onrank aggregation of features

2021

Self Cite

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The advance of metagenomic studies provides the opportunity to identify microbial taxa that are associated with human diseases. Multiple methods exist for the association analysis. However, the results could be inconsistent, presenting challenges in interpreting the host-microbiome interactions. To address this issue, we develop Meta-Signer, a novel Metagenomic Signature Identifier tool based on rank aggregation of features identified from multiple machine learning models including Random Forest, Support Vector Machines, Logistic Regression, and Multi-Layer Perceptron Neural Networks. Meta-Signer generates ranked taxa lists by training individual machine learning models over multiple training partitions and aggregates the ranked lists into a single list by an optimization procedure to represent the most informative and robust microbial features. A User will receive speedy assessment on the predictive performance of each ma-chine learning model using different numbers of the ranked features and determine the final models to be used for evaluation on external datasets. Meta-Signer is user-friendly and customizable, allowing users to explore their datasets quickly and efficiently.

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PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype From Metagenomic Data

Cited by 63 publications

References 36 publications

Microbiome-based disease prediction with multimodal variational information bottlenecks

Microbiome-based disease prediction with multimodal variational information bottlenecks

Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome

Meta-Signer: Metagenomic Signature Identifier based onrank aggregation of features

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