Understanding microbiome dynamics via interpretable graph representation learning

• URL: http://arxiv.org/abs/2203.01830v1
• Date: Wed, 2 Mar 2022 18:53:14 GMT
• Title: Understanding microbiome dynamics via interpretable graph representation learning
• Authors: Kateryna Melnyk, Kuba Weimann, Tim O.F. Conrad
• Abstract summary: Large-scale perturbations in the microbiome constitution are strongly correlated with the health and functioning of human physiology. We propose to model these interactions as a time-evolving graph whose nodes are microbes and edges are interactions among them. Motivated by the need to analyse such complex interactions, we develop a method that learns a low-dimensional representation of the time-evolving graph.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale perturbations in the microbiome constitution are strongly correlated, whether as a driver or a consequence, with the health and functioning of human physiology. However, understanding the difference in the microbiome profiles of healthy and ill individuals can be complicated due to the large number of complex interactions among microbes. We propose to model these interactions as a time-evolving graph whose nodes are microbes and edges are interactions among them. Motivated by the need to analyse such complex interactions, we develop a method that learns a low-dimensional representation of the time-evolving graph and maintains the dynamics occurring in the high-dimensional space. Through our experiments, we show that we can extract graph features such as clusters of nodes or edges that have the highest impact on the model to learn the low-dimensional representation. This information can be crucial to identify microbes and interactions among them that are strongly correlated with clinical diseases. We conduct our experiments on both synthetic and real-world microbiome datasets.

Related papers

• Causal Representation Learning from Multimodal Biological Observations [57.00712157758845]
  We aim to develop flexible identification conditions for multimodal data. We establish identifiability guarantees for each latent component, extending the subspace identification results from prior work. Our key theoretical ingredient is the structural sparsity of the causal connections among distinct modalities.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary work [0.47248250311484113]
  We aim to derive meaningful representations of individual gut microbiomes using graph neural networks (GNNs) The representation learnt from the encoder are then used to train a model for phenotype prediction such as Inflammatory Bowel Disease (IBD)
  arXiv  Detail & Related papers  (2024-06-28T15:53:36Z)
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction [50.7901190642594]
  We propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner. It maintains consistent and robust semantics by smoothing relations around the target interaction.
  arXiv  Detail & Related papers  (2023-12-09T07:08:00Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Predicting Molecule-Target Interaction by Learning Biomedical Network and Molecule Representations [10.128856077021625]
  We propose a pseudo-siamese Graph Neural Network method, namely MTINet+, which learns both biomedical network topological and molecule structural/chemical information as representations to predict potential interaction of given molecule and target pair. In the experiments of different molecule-target interaction tasks, MTINet+ significantly outperforms over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-02-02T10:00:46Z)
• Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks [5.156812030122437]
  Current biological networks are noisy, sparse, and incomplete. Experimental identification of such interactions is both time-consuming and expensive. Deep graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction. Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.
  arXiv  Detail & Related papers  (2022-11-22T20:44:28Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Graph Neural Networks for Microbial Genome Recovery [64.91162205624848]
  We propose to use Graph Neural Networks (GNNs) to leverage the assembly graph when learning contig representations for metagenomic binning. Our method, VaeG-Bin, combines variational autoencoders for learning latent representations of the individual contigs, with GNNs for refining these representations by taking into account the neighborhood structure of the contigs in the assembly graph.
  arXiv  Detail & Related papers  (2022-04-26T12:49:51Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• PhD dissertation to infer multiple networks from microbial data [0.0]
  A microbial network is a weighted graph that is constructed from a sample-taxa count matrix. The nodes in this graph represent microbial taxa and the edges represent pairwise associations amongst these taxa.
  arXiv  Detail & Related papers  (2020-10-12T19:16:26Z)
• GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis [0.2752817022620644]
  We propose a method for learning the embedding of the time-evolving graph. We demonstrate that our method can capture temporary changes in the time-evolving graph on both created synthetic data and real-world data.
  arXiv  Detail & Related papers  (2020-08-12T10:57:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.