LEADER 00837nam0-22002771i-450- 001 990005601430403321 005 20050907142011.0 035 $a000560143 035 $aFED01000560143 035 $a(Aleph)000560143FED01 035 $a000560143 100 $a19990604d1984----km-y0itay50------ba 101 0 $aita 105 $aa-------00--- 200 1 $aCome eravamo negli anni di guerra$eCronaca e costume 1940, 1945$fArrigo Petacco 210 $aNovara$cIstituto geografico De Agostini$dc1984 215 $a238 p.$cill.$d25 cm 676 $a945.0915$v21$zita 700 1$aPetacco,$bArrigo$f<1929- >$0151914 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005601430403321 952 $a945.091 PET 2$bBIBL. 59572$fFLFBC 959 $aFLFBC 996 $aCome eravamo negli anni di guerra$9609142 997 $aUNINA LEADER 01031cam0-22003731i-450 001 990000437310403321 005 20210603115726.0 035 $a000043731 035 $aFED01000043731 035 $a(Aleph)000043731FED01 035 $a000043731 100 $a20001010d1969----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $a<>alfabeto nella storia della civiltà$fDavid Diringer 205 $a2° ed. 210 $aFirenze$cGiunti$d1969 215 $a662 p.$d24 cm 610 0 $aScrittura$aStoria 610 0 $aAlfabeto$aStoria 610 0 $aScrittura$aTrattamento storico e geografico 676 $a411.09$v17$zita 700 1$aDiringer,$bDavid$024642 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000437310403321 952 $a08 DD 176$b2884$fDINED 952 $a411.09 DIR 1$bIST.GLOTT. 1317$fFLFBC 959 $aDINED 959 $aFLFBC 996 $aAlfabeto nella storia della civiltà$9136877 997 $aUNINA DB $aING01 LEADER 06700nam 22009255 450 001 9910768441203321 005 20251226202242.0 010 $a1-280-38605-3 010 $a9786613563972 010 $a3-642-12211-6 024 7 $a10.1007/978-3-642-12211-8 035 $a(CKB)2550000000011152 035 $a(SSID)ssj0000399479 035 $a(PQKBManifestationID)11275097 035 $a(PQKBTitleCode)TC0000399479 035 $a(PQKBWorkID)10376311 035 $a(PQKB)11783577 035 $a(DE-He213)978-3-642-12211-8 035 $a(MiAaPQ)EBC3065170 035 $a(PPN)149073488 035 $a(BIP)29212784 035 $a(EXLCZ)992550000000011152 100 $a20100402d2010 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics $e8th European Conference, EvoBIO 2010, Istanbul, Turkey, April 7-9, 2010, Proceedings /$fedited by Clara Pizzuti, Marylyn D. Ritchie, Mario Giacobini 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (XII, 249 p. 63 illus.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v6023 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-642-12210-8 320 $aIncludes bibliographical references and index. 327 $aVariable Genetic Operator Search for the Molecular Docking Problem -- Variable Genetic Operator Search for the Molecular Docking Problem -- Role of Centrality in Network-Based Prioritization of Disease Genes -- Parallel Multi-Objective Approaches for Inferring Phylogenies -- An Evolutionary Model Based on Hill-Climbing Search Operators for Protein Structure Prediction -- Finding Gapped Motifs by a Novel Evolutionary Algorithm -- Top-Down Induction of Phylogenetic Trees -- A Model Free Method to Generate Human Genetics Datasets with Complex Gene-Disease Relationships -- Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci -- Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions -- Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques -- Correlation?Based Scatter Search for Discovering Biclusters from Gene Expression Data -- A Local Search Appproach for Transmembrane Segment and Signal Peptide Discrimination -- A Replica Exchange Monte Carlo Algorithm for the Optimization of Secondary Structure Packing in Proteins -- Improving Multi-Relief for Detecting Specificity Residues from Multiple Sequence Alignments -- Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models -- Posters -- The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics -- Artificial Immune Systems for Epistasis Analysis in Human Genetics -- Metaheuristics for Strain Optimization Using Transcriptional Information Enriched Metabolic Models -- Using Rotation Forest for Protein Fold Prediction Problem: An Empirical Study -- Towards Automatic Detecting ofOverlapping Genes - Clustered BLAST Analysis of Viral Genomes -- Investigating Populational Evolutionary Algorithms to Add Vertical Meaning in Phylogenetic Trees. 330 $aThe ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction,leadingtonewdrugs andtherapiesforhumandisease. Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci'c information in a given dataset in order to generate new interesting knowledge. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o'er the ?eld of bioinformatics. The goal of the 8th - ropean Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2010) was to bring together experts in these ?elds in order to discuss new and novel methods for tackling complex biological problems. The 8th EvoBIO conference was held in Istanbul, Turkey during April 7-9, 2010attheIstanbulTechnicalUniversity. EvoBIO2010washeldjointlywiththe 13th European Conference on Genetic Programming (EuroGP 2010), the 10th European Conference on Evolutionary Computation in Combinatorial Opti- sation (EvoCOP 2010), and the conference on the applications of evolutionary computation,EvoApplications. Collectively,the conferences areorganizedunder the name Evo* (www. evostar. org). EvoBIO, held annually as a workshop since 2003, became a conference in 2007 and it is now the premiere European event for those interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v6023 606 $aBioinformatics 606 $aAlgorithms 606 $aDatabase management 606 $aArtificial intelligence 606 $aComputer science 606 $aArtificial intelligence$xData processing 606 $aComputational and Systems Biology 606 $aAlgorithms 606 $aDatabase Management 606 $aArtificial Intelligence 606 $aTheory of Computation 606 $aData Science 615 0$aBioinformatics. 615 0$aAlgorithms. 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 0$aComputer science. 615 0$aArtificial intelligence$xData processing. 615 14$aComputational and Systems Biology. 615 24$aAlgorithms. 615 24$aDatabase Management. 615 24$aArtificial Intelligence. 615 24$aTheory of Computation. 615 24$aData Science. 676 $a006.3 686 $aBIO 110f$2stub 686 $aDAT 708f$2stub 686 $aMAT 919f$2stub 686 $aSS 4800$2rvk 686 $aWC 7700$2rvk 701 $aPizzuti$b Clara$01295533 701 $aRitchie$b Marylyn DeRiggi$01756208 701 $aGiacobini$b Mario$01734761 712 12$aEvoBIO 2010 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910768441203321 996 $aEvolutionary computation, machine learning and data mining in bioinformatics$94193334 997 $aUNINA