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10.1093/nar/gkac010

http://scihub22266oqcxt.onion/10.1093/nar/gkac010
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35100418!9071488!35100418
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suck abstract from ncbi

pmid35100418
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  • AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks #MMPMID35100418
  • Shen WX; Liu Y; Chen Y; Zeng X; Tan Y; Jiang YY; Chen YZ
  • Nucleic Acids Res 2022[May]; 50 (8): e45 PMID35100418show ga
  • Omics-based biomedical learning frequently relies on data of high-dimensions (up to thousands) and low-sample sizes (dozens to hundreds), which challenges efficient deep learning (DL) algorithms, particularly for low-sample omics investigations. Here, an unsupervised novel feature aggregation tool AggMap was developed to Aggregate and Map omics features into multi-channel 2D spatial-correlated image-like feature maps (Fmaps) based on their intrinsic correlations. AggMap exhibits strong feature reconstruction capabilities on a randomized benchmark dataset, outperforming existing methods. With AggMap multi-channel Fmaps as inputs, newly-developed multi-channel DL AggMapNet models outperformed the state-of-the-art machine learning models on 18 low-sample omics benchmark tasks. AggMapNet exhibited better robustness in learning noisy data and disease classification. The AggMapNet explainable module Simply-explainer identified key metabolites and proteins for COVID-19 detections and severity predictions. The unsupervised AggMap algorithm of good feature restructuring abilities combined with supervised explainable AggMapNet architecture establish a pipeline for enhanced learning and interpretability of low-sample omics data.
  • |*COVID-19[MESH]
  • |*Deep Learning[MESH]
  • |Algorithms[MESH]
  • |Humans[MESH]
  • |Machine Learning[MESH]
  • |Proteins[MESH]


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  • suck abstract from ncbi

    e45 8.50 2022