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10.1038/ncomms14825 http://scihub22266oqcxt.onion/10.1038/ncomms14825 C5384229!5384229!28382969     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2017 ; 8 (ä): ä Nephropedia Template TP {{tp|p=28382969|t=2017. Sensitive detection of rare disease-associated cell subsets via representation learning |pdf=|usr=}}{{28382969}} gab.com Text Sensitive detection of rare disease-associated cell subsets via representation learning JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=28382969 #FOAvip Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%. Twit Text FOAVip Sensitive detection of rare disease-associated cell subsets via representation learning ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=28382969 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28382969 #FOAvip English Wikipedia <ref>{{Cite pmid|28382969}}</ref> Sensitive detection of rare disease-associated cell subsets via representation learning #MMPMID28382969 Arvaniti E; Claassen M Nat Commun 2017[]; 8 (ä): ä PMID28382969show ga Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%. äSensitive detection of rare disease-associated cell subsets via repres(epmc).pdf DeepDyve Pubget Overpricing