LEADER 00968nam--2200349---450- 001 990001426700203316 005 20050923113758.0 035 $a000142670 035 $aUSA01000142670 035 $a(ALEPH)000142670USA01 035 $a000142670 100 $a20040216d1960----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> metodo educativo di Don Bosco$fMario Casotti 210 $aBrescia$cLa Scuola$d1960 215 $a135 p.$d21 cm 410 0$12001 454 1$12001 461 1$1001-------$12001 700 1$aCASOTTI,$bMario$0170372 801 0$aIT$bsalbc$gISBD 912 $a990001426700203316 951 $aII.4. 1716(VI C 243)$b11189 L.M.$cVI C 959 $aBK 969 $aUMA 979 $aSIAV7$b10$c20040216$lUSA01$h1509 979 $aPATRY$b90$c20040406$lUSA01$h1740 979 $aCOPAT3$b90$c20050923$lUSA01$h1137 996 $aMetodo educativo di don Bosco$9216907 997 $aUNISA LEADER 03485nam 2200385z- 450 001 9910136408203321 005 20210212 035 $a(CKB)3710000000612032 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/57438 035 $a(oapen)doab57438 035 $a(EXLCZ)993710000000612032 100 $a20202102d2015 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aQuantitative Assessment and Validation of Network Inference Methods in Bioinformatics 210 $cFrontiers Media SA$d2015 215 $a1 online resource (191 p.) 225 1 $aFrontiers Research Topics 311 08$a2-88919-478-7 330 $aScientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput "omics" technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the major challenges of the post-genomic era is to extract functional information by integrating such heterogeneous high-throughput genomic data. This is not a trivial task as we are increasingly coming to understand that it is not individual genes, but rather biological pathways and networks that drive an organism's response to environmental factors and the development of its particular phenotype. In order to fully understand the way in which these networks interact (or fail to do so) in specific states (disease for instance), we must learn both, the structure of the underlying networks and the rules that govern their behavior. In recent years there has been an increasing interest in methods that aim to infer biological networks. These methods enable the opportunity for better understanding the interactions between genomic features and the overall structure and behavior of the underlying networks. So far, such network models have been mainly used to identify and validate new interactions between genes of interest. But ultimately, one could use these networks to predict large-scale effects of perturbations, such as treatment by multiple targeted drugs. However, currently, we are still at an early stage of comprehending methods and approaches providing a robust statistical framework to quantitatively assess the quality of network inference and its predictive potential. The scope of this Research Topic in Bioinformatics and Computational Biology aims at addressing these issues by investigating the various, complementary approaches to quantify the quality of network models. These "validation" techniques could focus on assessing quality of specific interactions, global and local structures, and predictive ability of network models. These methods could rely exclusively on in silico evaluation procedures or they could be coupled with novel experimental designs to generate the biological data necessary to properly validate inferred networks. 606 $aGenetics (non-medical)$2bicssc 610 $abioinformatics 610 $aGene Expression 610 $aNetwork Inference 610 $aValidation 615 7$aGenetics (non-medical) 700 $aFrank Emmert-Streib$4auth$0867728 702 $aBenjamin Haibe-Kains$4auth 906 $aBOOK 912 $a9910136408203321 996 $aQuantitative Assessment and Validation of Network Inference Methods in Bioinformatics$93033438 997 $aUNINA