GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis

• URL: http://arxiv.org/abs/2008.05903v3
• Date: Thu, 19 Nov 2020 12:06:13 GMT
• Title: GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
• Authors: Kateryna Melnyk, Stefan Klus, Gr\'egoire Montavon, Tim Conrad
• Abstract summary: 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.
• Score: 0.2752817022620644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both created synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes.

Related papers

• Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold [83.18058549195855]
  We argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities. In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depends on the microenvironment of cells specific to each patient. We propose Meta Flow Matching (MFM), a practical approach to integrating along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations.
  arXiv  Detail & Related papers  (2024-08-26T20:05:31Z)
• UMMAN: Unsupervised Multi-graph Merge Adversarial Network for Disease Prediction Based on Intestinal Flora [0.18641315013048299]
  We present a novel architecture, Unsupervised Multi-graph Adversarial Network (UMMAN) UMMAN can obtain the embeddings of nodes in the Multi-Graph in an unsupervised scenario, so that it helps learn the multiplex association. We employ complex relation-types to construct the Original-Graph and disrupt the relationships among nodes to generate corresponding Shuffled-Graph.
  arXiv  Detail & Related papers  (2024-07-31T16:06:43Z)
• 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)
• 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)
• A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression [48.97251676778599]
  In medical imaging, population graphs have demonstrated promising results, mostly for classification tasks. extracting population graphs is a non-trivial task and can significantly impact the performance of Graph Neural Networks (GNNs) In this work, we highlight the importance of a meaningful graph construction and experiment with different population-graph construction methods.
  arXiv  Detail & Related papers  (2023-09-26T10:30:45Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [55.42602325017405]
  We propose a novel method called GODE, which takes into account the two-level structure of individual molecules. By pre-training two graph neural networks (GNNs) on different graph structures, combined with contrastive learning, GODE fuses molecular structures with their corresponding knowledge graph substructures. When fine-tuned across 11 chemical property tasks, our model outperforms existing benchmarks, registering an average ROC-AUC uplift of 13.8% for classification tasks and an average RMSE/MAE enhancement of 35.1% for regression tasks.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• 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)
• Understanding microbiome dynamics via interpretable graph representation learning [0.0]
  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.
  arXiv  Detail & Related papers  (2022-03-02T18:53:14Z)
• Graph Representation Learning on Tissue-Specific Multi-Omics [0.0]
  We leverage a graph embedding model (i.e VGAE) to perform link prediction on tissue-specific Gene-Gene Interaction (GGI) networks. We prove that the combination of multiple biological modalities (i.e multi-omics) leads to powerful embeddings and better link prediction performances.
  arXiv  Detail & Related papers  (2021-07-25T17:38:45Z)
• A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: from Traditional Image Processing and Classical Machine Learning to Current Deep Convolutional Neural Networks and Potential Visual Transformers [19.612485200561455]
  Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. Traditional microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms.
  arXiv  Detail & Related papers  (2021-05-07T10:18:17Z)
• 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)

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.