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10.1016/j.drudis.2020.10.002 http://scihub22266oqcxt.onion/10.1016/j.drudis.2020.10.002 33059075!7550123!33059075     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33059075&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       Drug+Discov+Today 2021 ; 26 (1): 173-180 Nephropedia Template TP {{tp|p=33059075|t=2021. Deep learning in next-generation sequencing |pdf=|usr=}}{{33059075}} gab.com Text Deep learning in next-generation sequencing JO: Drug Discov Today http://www.kidney.de/pget.php?&tw=1&pmid=33059075 #FOAvip Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods of the data sets produced, addressing questions such as variant calling, metagenomic classification and quantification, genomic feature detection, or downstream analysis in larger biological or medical contexts. In addition to classical algorithmic approaches, machine-learning (ML) techniques are often used for such tasks. In particular, deep learning (DL) methods that use multilayered artificial neural networks (ANNs) for supervised, semisupervised, and unsupervised learning have gained significant traction for such applications. Here, we highlight important network architectures, application areas, and DL frameworks in a NGS context. Twit Text FOAVip Deep learning in next-generation sequencing ????Drug Discov Today http://www.kidney.de/pget.php?&tw=1&pmid=33059075 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33059075 #FOAvip English Wikipedia <ref>{{Cite pmid|33059075}}</ref> Deep learning in next-generation sequencing #MMPMID33059075 Schmidt B; Hildebrandt A Drug Discov Today 2021[Jan]; 26 (1): 173-180 PMID33059075show ga Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods of the data sets produced, addressing questions such as variant calling, metagenomic classification and quantification, genomic feature detection, or downstream analysis in larger biological or medical contexts. In addition to classical algorithmic approaches, machine-learning (ML) techniques are often used for such tasks. In particular, deep learning (DL) methods that use multilayered artificial neural networks (ANNs) for supervised, semisupervised, and unsupervised learning have gained significant traction for such applications. Here, we highlight important network architectures, application areas, and DL frameworks in a NGS context. |*Deep Learning[MESH] |*Metagenomics/methods/trends[MESH] |*Neural Networks, Computer[MESH] |Biomedical Research/trends[MESH] |High-Throughput Nucleotide Sequencing/*methods[MESH]Deep learning in next-generation sequencing (epmc).pdf DeepDyve Pubget Overpricing