Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance ; volume:21 ; number:1 ; day:20 ; month:12 ; year:2023 ; pages:1-15 ; date:12.2023
BMC biology ; 21, Heft 1 (20.12.2023), 1-15, 12.2023

Creator: Zhu, Huan
Hao, Hongxia
Yu, Liang

Contributor: SpringerLink (Online service)

DOI: 10.1186/s12915-023-01796-8

URN: urn:nbn:de:101:1-2024030609084235883389

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:47 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Zhu, Huan
Hao, Hongxia
Yu, Liang
SpringerLink (Online service)

Other Objects (12)

Deep Embedding Clustering Based on Residual Autoencoder

Unsupervised Learning of Network Embedding with Variational Autoencoder Framework

Joint optimization of an autoencoder for clustering and embedding

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes

Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning

Brunnen | Ensemble | Freiplastik

Wasserstein

Archivale

Wasserstein, Bernhard

Karten und Pläne

Mittelhof: Wasserstein

Wasserstein dropout

Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means

Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks

Schleifstein mit Schlammstein (Abziehstein mit Aufreibstein)

WASSERSTEIN MIT AUFREIBSTEIN

Deep Embedding Clustering Based on Residual Autoencoder

Unsupervised Learning of Network Embedding with Variational Autoencoder Framework

Joint optimization of an autoencoder for clustering and embedding

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes

Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning

Brunnen | Ensemble | Freiplastik

Wasserstein

Archivale

Wasserstein, Bernhard

Karten und Pläne

Mittelhof: Wasserstein

Wasserstein dropout

Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means

Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks

Schleifstein mit Schlammstein (Abziehstein mit Aufreibstein)

WASSERSTEIN MIT AUFREIBSTEIN

Deep Embedding Clustering Based on Residual Autoencoder

Unsupervised Learning of Network Embedding with Variational Autoencoder Framework

Joint optimization of an autoencoder for clustering and embedding

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes

Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning

Brunnen | Ensemble | Freiplastik

Wasserstein

Archivale

Wasserstein, Bernhard

Karten und Pläne

Mittelhof: Wasserstein

Wasserstein dropout

Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means

Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks

Schleifstein mit Schlammstein (Abziehstein mit Aufreibstein)

WASSERSTEIN MIT AUFREIBSTEIN

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Deep Embedding Clustering Based on Residual Autoencoder

Unsupervised Learning of Network Embedding with Variational Autoencoder Framework

Joint optimization of an autoencoder for clustering and embedding

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes

Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning

Wasserstein

Wasserstein, Bernhard

Mittelhof: Wasserstein

Wasserstein dropout

Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means

Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks

WASSERSTEIN MIT AUFREIBSTEIN

Deep Embedding Clustering Based on Residual Autoencoder

Unsupervised Learning of Network Embedding with Variational Autoencoder Framework

Joint optimization of an autoencoder for clustering and embedding

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes

Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning

Wasserstein

Wasserstein, Bernhard

Mittelhof: Wasserstein

Wasserstein dropout

Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means

Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks

WASSERSTEIN MIT AUFREIBSTEIN

Deep Embedding Clustering Based on Residual Autoencoder

Unsupervised Learning of Network Embedding with Variational Autoencoder Framework

Joint optimization of an autoencoder for clustering and embedding

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes

Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning

Wasserstein

Wasserstein, Bernhard

Mittelhof: Wasserstein

Wasserstein dropout

Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means

Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks

WASSERSTEIN MIT AUFREIBSTEIN

Related objects

Reset password