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10.1371/journal.pcbi.1005661 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1005661 C5600403!5600403!28873403     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       PLoS+Comput+Biol 2017 ; 13 (9): ä Nephropedia Template TP {{tp|p=28873403|t=2017. DeepPep: Deep proteome inference from peptide profiles |pdf=|usr=}}{{28873403}} gab.com Text DeepPep: Deep proteome inference from peptide profiles JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=28873403 #FOAvip Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability (AUC of 0.80±0.18, AUPR of 0.84±0.28) in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/. Twit Text FOAVip DeepPep: Deep proteome inference from peptide profiles ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=28873403 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28873403 #FOAvip English Wikipedia <ref>{{Cite pmid|28873403}}</ref> DeepPep: Deep proteome inference from peptide profiles #MMPMID28873403 Kim M; Eetemadi A; Tagkopoulos I PLoS Comput Biol 2017[Sep]; 13 (9): ä PMID28873403show ga Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability (AUC of 0.80±0.18, AUPR of 0.84±0.28) in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/. äDeepPep: Deep proteome inference from peptide profiles (epmc).pdf DeepDyve Pubget Overpricing