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deep learning autoencoders  (Omics Data Automation)

 
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    Omics Data Automation deep learning autoencoders
    Deep Learning Autoencoders, supplied by Omics Data Automation, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/deep+learning+autoencoders/learning+machine/pm41945244-268-29-37
    Average 86 stars, based on 1 article reviews
    deep learning autoencoders - by Bioz Stars, 2026-10
    86/100 stars

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    Image Search Results


    Number of MAGs (percentage of the total observed within workflow) from different assembly-binning workflows categorized by initial quality evaluation.

    Journal: Frontiers in Microbiology

    Article Title: Recovery of High Quality Metagenome-Assembled Genomes From Full-Scale Activated Sludge Microbial Communities in a Tropical Climate Using Longitudinal Metagenome Sampling

    doi: 10.3389/fmicb.2022.869135

    Figure Lengend Snippet: Number of MAGs (percentage of the total observed within workflow) from different assembly-binning workflows categorized by initial quality evaluation.

    Article Snippet: Additionally, we compared these results to those obtained from the use of a recently released deep learning variational autoencoder called VAMB (Nissen et al., ), which appears to convey some advantages in relation to control of MAG contamination.

    Techniques:

    Number of MAGs categorized by genome quality assignments before and after decontamination with RefineM for (A) MAGs obtained from MetaBAT2 workflows and (B) MAGs from VAMB workflow.

    Journal: Frontiers in Microbiology

    Article Title: Recovery of High Quality Metagenome-Assembled Genomes From Full-Scale Activated Sludge Microbial Communities in a Tropical Climate Using Longitudinal Metagenome Sampling

    doi: 10.3389/fmicb.2022.869135

    Figure Lengend Snippet: Number of MAGs categorized by genome quality assignments before and after decontamination with RefineM for (A) MAGs obtained from MetaBAT2 workflows and (B) MAGs from VAMB workflow.

    Article Snippet: Additionally, we compared these results to those obtained from the use of a recently released deep learning variational autoencoder called VAMB (Nissen et al., ), which appears to convey some advantages in relation to control of MAG contamination.

    Techniques:

    Analysis of the microscopy video of lymph flow in a mouse brain (A–D) (A) Raw video frame, (B) results obtained by calculating the mean from the deep learning denoising autoencoder, (C) analysis by means of the GMM entropy clearly revealing surface lymph capillaries, (D) analysis by means of the latent entropy revealing even bulk lymph capillaries.

    Journal: iScience

    Article Title: A deeper look into natural sciences with physics-based and data-driven measures

    doi: 10.1016/j.isci.2021.102171

    Figure Lengend Snippet: Analysis of the microscopy video of lymph flow in a mouse brain (A–D) (A) Raw video frame, (B) results obtained by calculating the mean from the deep learning denoising autoencoder, (C) analysis by means of the GMM entropy clearly revealing surface lymph capillaries, (D) analysis by means of the latent entropy revealing even bulk lymph capillaries.

    Article Snippet: The capillaries of the glymphatic system are not directly visible either in the raw video data, see A, or in the mean obtained by using a commercial deep learning denoising autoencoder from the “Deep Learning Toolbox” of MATLAB ( ; , ), see B.

    Techniques: Microscopy