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10.1093/bioinformatics/btx818 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btx818 C5946842!5946842!29280997     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       Bioinformatics 2018 ; 34 (10): 1690-6 Nephropedia Template TP {{tp|p=29280997|t=2018. DeepSig: deep learning improves signal peptide detection in proteins |pdf=|usr=}}{{29280997}} gab.com Text DeepSig: deep learning improves signal peptide detection in proteins JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29280997 #FOAvip Motivation: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results: Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation: DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. Supplementary information: Supplementary data are available at Bioinformatics online. Twit Text FOAVip DeepSig: deep learning improves signal peptide detection in proteins ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29280997 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29280997 #FOAvip English Wikipedia <ref>{{Cite pmid|29280997}}</ref> DeepSig: deep learning improves signal peptide detection in proteins #MMPMID29280997 Savojardo C; Martelli PL; Fariselli P; Casadio R Bioinformatics 2018[May]; 34 (10): 1690-6 PMID29280997show ga Motivation: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results: Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation: DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. Supplementary information: Supplementary data are available at Bioinformatics online. äDeepSig: deep learning improves signal peptide detection in proteins (epmc).pdf DeepDyve Pubget Overpricing