LEADER 01180nam0 22002771i 450 001 UON00348861 005 20231205104332.668 100 $a20091203d1954 |0itac50 ba 101 $aspa 102 $aES 105 $a|||| 1|||| 200 1 $aArte de la lengua española castellana$fGonzalo Correas$eedición y prólogo de Emilio Alarcos García 210 $aMadrid$c[S.n.]$d1954 215 $a500 p.$d25 cm. 410 1$1001UON00174972$12001 $aRevista de Filologia Española. Anejo$1210 $aMadrid$cJosé Molina impresor$v56 606 $aLingua Spagnola$xGrammatica$3UONC061435$2FI 620 $aES$dMadrid$3UONL000218 676 $a465$cSistemi strutturali (Grammatica) della lingua spagnola$v21 700 1$aCORREAS$bGonzalo$3UONV194477$0220440 702 1$aALARCOS GARCIA$bEmilio$3UONV194478 801 $aIT$bSOL$c20250516$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00348861 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI SPA V 0079 $eSI MR 54035 5 0079 996 $aArte de la lengua española castellana$91360039 997 $aUNIOR LEADER 04026nam 22007695 450 001 9910437857303321 005 20200703174719.0 010 $a3-642-45161-6 024 7 $a10.1007/978-3-642-45161-4 035 $a(CKB)3710000000083721 035 $a(EBL)1636655 035 $a(OCoLC)871224049 035 $a(SSID)ssj0001158996 035 $a(PQKBManifestationID)11636158 035 $a(PQKBTitleCode)TC0001158996 035 $a(PQKBWorkID)11106482 035 $a(PQKB)11374811 035 $a(DE-He213)978-3-642-45161-4 035 $a(MiAaPQ)EBC1636655 035 $a(PPN)176117822 035 $a(EXLCZ)993710000000083721 100 $a20140103d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aGene Network Inference $eVerification of Methods for Systems Genetics Data /$fedited by Alberto Fuente 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (135 p.) 300 $aDescription based upon print version of record. 311 $a3-642-45160-8 320 $aIncludes bibliographical references at the end of each chapters. 327 $aSimulation of the Benchmark Datasets -- A Panel of Learning Methods for the Reconstruction of Gene Regulatory Networks in a Systems Genetics Context -- Benchmarking a simple yet effective approach for inferring gene regulatory networks from systems genetics data -- Differential Equation based reverse-engineering algorithms: pros and cons -- Gene regulatory network inference from systems genetics data using tree-based methods -- Extending partially known networks -- Integration of genetic variation as external perturbation to reverse engineer regulatory networks from gene expression data -- Using Simulated Data to Evaluate Bayesian Network Approach for Integrating Diverse Data. 330 $aThis book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians. 606 $aSystems biology 606 $aBioinformatics 606 $aBiological systems 606 $aBioinformatics 606 $aComputational biology 606 $aGene expression 606 $aSystems Biology$3https://scigraph.springernature.com/ontologies/product-market-codes/L15010 606 $aBioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15001 606 $aSystems Biology$3https://scigraph.springernature.com/ontologies/product-market-codes/P27050 606 $aComputer Appl. in Life Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/L17004 606 $aGene Expression$3https://scigraph.springernature.com/ontologies/product-market-codes/B12010 615 0$aSystems biology. 615 0$aBioinformatics. 615 0$aBiological systems. 615 0$aBioinformatics. 615 0$aComputational biology. 615 0$aGene expression. 615 14$aSystems Biology. 615 24$aBioinformatics. 615 24$aSystems Biology. 615 24$aComputer Appl. in Life Sciences. 615 24$aGene Expression. 676 $a570 676 $a570285 676 $a571.4 676 $a572.8 702 $aFuente$b Alberto$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910437857303321 996 $aGene Network Inference$92513111 997 $aUNINA