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10.1186/s40880-015-0008-8 http://scihub22266oqcxt.onion/10.1186/s40880-015-0008-8 C4593335!4593335 !25963029     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 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 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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25963029 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Chin+J+Cancer 2015 ; 34 (10 ): 423-6 Nephropedia Template TP {{tp|p=25963029 |t=2015. Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling |pdf=|usr=}}{{25963029 }} gab.com Text Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling JO: Chin J Cancer http://www.kidney.de/pget.php?&tw=1&pmid=25963029 #FOAvip We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research. Twit Text FOAVip Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling ????Chin J Cancer http://www.kidney.de/pget.php?&tw=1&pmid=25963029 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25963029 #FOAvip English Wikipedia <ref>{{Cite pmid|25963029 }}</ref> Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling #MMPMID25963029 Yli-Hietanen J ; Ylipää A ; Yli-Harja O Chin J Cancer 2015[Apr]; 34 (10 ): 423-6 PMID25963029 show ga We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research. |*Computer Simulation [MESH] |*High-Throughput Nucleotide Sequencing [MESH] |*Neoplasms [MESH] |Genome [MESH]Cancer research in the era of next-generation sequencing and big data (epmc).pdf DeepDyve Pubget Overpricing