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10.15252/msb.202110243 http://scihub22266oqcxt.onion/10.15252/msb.202110243 34487431!8420856!34487431     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\34487431.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Mol+Syst+Biol 2021 ; 17 (9): e10243 Nephropedia Template TP {{tp|p=34487431|t=2021. Tensor-structured decomposition improves systems serology analysis |pdf=|usr=}}{{34487431}} gab.com Text Tensor-structured decomposition improves systems serology analysis JO: Mol Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=34487431 #FOAvip Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease-relevant antigen targets, alongside additional measurements made for single antigens or in an antigen-generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix-tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV- and SARS-CoV-2-infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen-binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology. Twit Text FOAVip Tensor-structured decomposition improves systems serology analysis ????Mol Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=34487431 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34487431 #FOAvip English Wikipedia <ref>{{Cite pmid|34487431}}</ref> Tensor-structured decomposition improves systems serology analysis #MMPMID34487431 Tan ZC; Murphy MC; Alpay HS; Taylor SD; Meyer AS Mol Syst Biol 2021[Sep]; 17 (9): e10243 PMID34487431show ga Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease-relevant antigen targets, alongside additional measurements made for single antigens or in an antigen-generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix-tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV- and SARS-CoV-2-infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen-binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology. |*AIDS Serodiagnosis/methods/statistics & numerical data[MESH] |*COVID-19 Serological Testing/methods/statistics & numerical data[MESH] |*Data Interpretation, Statistical[MESH] |Antibodies, Viral/blood/metabolism[MESH] |COVID-19/*immunology[MESH] |HIV Infections/*immunology[MESH] |Humans[MESH] |Immunity, Humoral[MESH] |Killer Cells, Natural/immunology[MESH] |Logistic Models[MESH] |Receptors, Fc/immunology[MESH]Tensor-structured decomposition improves systems serology analysis (epmc).pdf DeepDyve Pubget Overpricing