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10.1093/nar/gkac010 http://scihub22266oqcxt.onion/10.1093/nar/gkac010 35100418!9071488!35100418     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nucleic+Acids+Res 2022 ; 50 (8): e45 Nephropedia Template TP {{tp|p=35100418|t=2022. AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks |pdf=|usr=}}{{35100418}} gab.com Text AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks JO: Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=35100418 #FOAvip 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. Twit Text FOAVip AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks ????Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=35100418 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35100418 #FOAvip English Wikipedia <ref>{{Cite pmid|35100418}}</ref> 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]AggMapNet: enhanced and explainable low-sample omics deep learning wit(epmc).pdf DeepDyve Pubget Overpricing