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10.1039/c4mb00677a http://scihub22266oqcxt.onion/10.1039/c4mb00677a C4333019!4333019 !25609570     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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25609570 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Mol+Biosyst 2015 ; 11 (3 ): 714-22 Nephropedia Template TP {{tp|p=25609570 |t=2015. Compound signature detection on LINCS L1000 big data |pdf=|usr=}}{{25609570 }} gab.com Text Compound signature detection on LINCS L1000 big data JO: Mol Biosyst http://www.kidney.de/pget.php?&tw=1&pmid=25609570 #FOAvip The Library of Integrated Network-based Cellular Signatures (LINCS) L1000 big data provide gene expression profiles induced by over 10?000 compounds, shRNAs, and kinase inhibitors using the L1000 platform. We developed csNMF, a systematic compound signature discovery pipeline covering from raw L1000 data processing to drug screening and mechanism generation. The csNMF pipeline demonstrated better performance than the original L1000 pipeline. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. The csNMF pipeline provided a novel and complete tool to expedite signature-based drug discovery leveraging the LINCS L1000 resources. Twit Text FOAVip Compound signature detection on LINCS L1000 big data ????Mol Biosyst http://www.kidney.de/pget.php?&tw=1&pmid=25609570 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25609570 #FOAvip English Wikipedia <ref>{{Cite pmid|25609570 }}</ref> Compound signature detection on LINCS L1000 big data #MMPMID25609570 Liu C ; Su J ; Yang F ; Wei K ; Ma J ; Zhou X Mol Biosyst 2015[Mar]; 11 (3 ): 714-22 PMID25609570 show ga The Library of Integrated Network-based Cellular Signatures (LINCS) L1000 big data provide gene expression profiles induced by over 10?000 compounds, shRNAs, and kinase inhibitors using the L1000 platform. We developed csNMF, a systematic compound signature discovery pipeline covering from raw L1000 data processing to drug screening and mechanism generation. The csNMF pipeline demonstrated better performance than the original L1000 pipeline. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. The csNMF pipeline provided a novel and complete tool to expedite signature-based drug discovery leveraging the LINCS L1000 resources. |*Datasets as Topic [MESH] |*Transcriptome [MESH] |Breast Neoplasms/drug therapy/genetics [MESH] |Cell Line, Tumor [MESH] |Computational Biology/methods [MESH] |Drug Discovery/methods [MESH] |Drug Screening Assays, Antitumor [MESH] |Gene Expression Profiling/methods [MESH] |Gene Expression Regulation/*drug effects [MESH] |Humans [MESH] |Molecular Sequence Annotation [MESH] |Protein Kinase Inhibitors/pharmacology [MESH] |RNA, Small Interfering/genetics [MESH] |Reproducibility of Results [MESH] |Small Molecule Libraries [MESH]Compound signature detection on LINCS L1000 big data (epmc).pdf DeepDyve Pubget Overpricing