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Data Efficiency Semi-Supervised Meta-Learning Elucidates Understudied Interspecies Molecular Interactions

The power of deep learning compromises when applied to biological problems with sparsely labeled data and a data distribution shift. We developed a highly data-efficient model-agnostic semi-supervised meta-learning framework DESSML to address these challenges, and applied it to investigate understud...

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Detalles Bibliográficos
Autores principales: Wu, You, Xie, Li, Liu, Yang, Xie, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245663/
https://www.ncbi.nlm.nih.gov/pubmed/37292680
http://dx.doi.org/10.1101/2023.05.17.541172
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author Wu, You
Xie, Li
Liu, Yang
Xie, Lei
author_facet Wu, You
Xie, Li
Liu, Yang
Xie, Lei
author_sort Wu, You
collection PubMed
description The power of deep learning compromises when applied to biological problems with sparsely labeled data and a data distribution shift. We developed a highly data-efficient model-agnostic semi-supervised meta-learning framework DESSML to address these challenges, and applied it to investigate understudied interspecies metabolite-protein interactions (MPI). Knowledge of interspecies MPIs is crucial to understand microbiome-host interactions. However, our understanding of interspecies MPIs is extremely poor due to experimental limitations. The paucity of experimental data also hampers the application of machine learning. DESSML successfully explores unlabeled data and transfers the information of intraspecies chemical-protein interactions to the interspecies MPI predictions. It achieves three times improvement in the prediction-recall over the baseline model. Using DESSML, we reveal novel MPIs that are validated by bioactivity assays and fill in missing links in microbiome-human interactions. DESSML is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental techniques.
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spelling pubmed-102456632023-06-08 Data Efficiency Semi-Supervised Meta-Learning Elucidates Understudied Interspecies Molecular Interactions Wu, You Xie, Li Liu, Yang Xie, Lei bioRxiv Article The power of deep learning compromises when applied to biological problems with sparsely labeled data and a data distribution shift. We developed a highly data-efficient model-agnostic semi-supervised meta-learning framework DESSML to address these challenges, and applied it to investigate understudied interspecies metabolite-protein interactions (MPI). Knowledge of interspecies MPIs is crucial to understand microbiome-host interactions. However, our understanding of interspecies MPIs is extremely poor due to experimental limitations. The paucity of experimental data also hampers the application of machine learning. DESSML successfully explores unlabeled data and transfers the information of intraspecies chemical-protein interactions to the interspecies MPI predictions. It achieves three times improvement in the prediction-recall over the baseline model. Using DESSML, we reveal novel MPIs that are validated by bioactivity assays and fill in missing links in microbiome-human interactions. DESSML is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental techniques. Cold Spring Harbor Laboratory 2023-05-20 /pmc/articles/PMC10245663/ /pubmed/37292680 http://dx.doi.org/10.1101/2023.05.17.541172 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Wu, You
Xie, Li
Liu, Yang
Xie, Lei
Data Efficiency Semi-Supervised Meta-Learning Elucidates Understudied Interspecies Molecular Interactions
title Data Efficiency Semi-Supervised Meta-Learning Elucidates Understudied Interspecies Molecular Interactions
title_full Data Efficiency Semi-Supervised Meta-Learning Elucidates Understudied Interspecies Molecular Interactions
title_fullStr Data Efficiency Semi-Supervised Meta-Learning Elucidates Understudied Interspecies Molecular Interactions
title_full_unstemmed Data Efficiency Semi-Supervised Meta-Learning Elucidates Understudied Interspecies Molecular Interactions
title_short Data Efficiency Semi-Supervised Meta-Learning Elucidates Understudied Interspecies Molecular Interactions
title_sort data efficiency semi-supervised meta-learning elucidates understudied interspecies molecular interactions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245663/
https://www.ncbi.nlm.nih.gov/pubmed/37292680
http://dx.doi.org/10.1101/2023.05.17.541172
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