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10.1016/j.ymeth.2020.05.018 http://scihub22266oqcxt.onion/10.1016/j.ymeth.2020.05.018 32454212!7255349!32454212     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32454212&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Methods 2021 ; 189 (ä): 95-103 Nephropedia Template TP {{tp|p=32454212|t=2021. CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning |pdf=|usr=}}{{32454212}} gab.com Text CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning JO: Methods http://www.kidney.de/pget.php?&tw=1&pmid=32454212 #FOAvip The fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex composition, and large data size can all make taxonomic analysis difficult. In particular, commonly used alignment-based methods are not ideal choices for detecting new viral species. In this work, we present a novel hierarchical classification model named CHEER, which can conduct read-level taxonomic classification from order to genus for new species. By combining k-mer embedding-based encoding, hierarchically organized CNNs, and carefully trained rejection layer, CHEER is able to assign correct taxonomic labels for reads from new species. We tested CHEER on both simulated and real sequencing data. The results show that CHEER can achieve higher accuracy than popular alignment-based and alignment-free taxonomic assignment tools. The source code, scripts, and pre-trained parameters for CHEER are available via GitHub:https://github.com/KennthShang/CHEER. Twit Text FOAVip CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning ????Methods http://www.kidney.de/pget.php?&tw=1&pmid=32454212 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32454212 #FOAvip English Wikipedia <ref>{{Cite pmid|32454212}}</ref> CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning #MMPMID32454212 Shang J; Sun Y Methods 2021[May]; 189 (ä): 95-103 PMID32454212show ga The fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex composition, and large data size can all make taxonomic analysis difficult. In particular, commonly used alignment-based methods are not ideal choices for detecting new viral species. In this work, we present a novel hierarchical classification model named CHEER, which can conduct read-level taxonomic classification from order to genus for new species. By combining k-mer embedding-based encoding, hierarchically organized CNNs, and carefully trained rejection layer, CHEER is able to assign correct taxonomic labels for reads from new species. We tested CHEER on both simulated and real sequencing data. The results show that CHEER can achieve higher accuracy than popular alignment-based and alignment-free taxonomic assignment tools. The source code, scripts, and pre-trained parameters for CHEER are available via GitHub:https://github.com/KennthShang/CHEER. |*Deep Learning[MESH] |Classification[MESH] |Metagenomics/*methods[MESH]CHEER: HierarCHical taxonomic classification for viral mEtagEnomic dat(epmc).pdf DeepDyve Pubget Overpricing