LEADER 01171nam0 22003013i 450 001 SUN0114614 005 20180207120921.806 010 $a88-14-14140-1$d0.00 100 $a20180207d2008 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aI *gruppi come strumenti di governo delle aziende$fAndrea Cammareri ... [et al.]$ga cura di Carlo Sorci e Guglielmo Faldetta 210 $aMilano$cGiuffrè$d2008 215 $aXIV, 438 p.$d24 cm. 410 1$1001SUN0114619$12001 $a*Serie didattica$v6$1210 $aMilano$cGiuffrè 606 $aGruppi industriali$xGestione$2EC$3SUNC030482 620 $dMilano$3SUNL000284 676 $a338.7$v21 702 1$aSorci$b, Carlo$3SUNV021649 702 1$aCammareri$b, Andrea$3SUNV088681 702 1$aFaldetta$b, Guglielmo$3SUNV088682 712 $aGiuffrè$3SUNV001757$4650 801 $aIT$bSOL$c20181231$gRICA 912 $aSUN0114614 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI ECONOMIA$d03PREST IIBb109 $e03BDE1507 20180207 996 $aGruppi come strumenti di governo delle aziende$91016910 997 $aUNICAMPANIA LEADER 04909nam 22006615 450 001 9910299779803321 005 20250717140310.0 010 $a94-6239-097-5 024 7 $a10.2991/978-94-6239-097-3 035 $a(CKB)3710000000379768 035 $a(EBL)3108725 035 $a(SSID)ssj0001465355 035 $a(PQKBManifestationID)11896953 035 $a(PQKBTitleCode)TC0001465355 035 $a(PQKBWorkID)11471541 035 $a(PQKB)10656877 035 $a(DE-He213)978-94-6239-097-3 035 $a(MiAaPQ)EBC3108725 035 $a(PPN)184893534 035 $a(EXLCZ)993710000000379768 100 $a20150323d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAnalysis and Enumeration $eAlgorithms for Biological Graphs /$fby Andrea Marino 205 $a1st ed. 2015. 210 1$aParis :$cAtlantis Press :$cImprint: Atlantis Press,$d2015. 215 $a1 online resource (158 p.) 225 1 $aAtlantis Studies in Computing,$x2212-8565 ;$v6 300 $aDescription based upon print version of record. 311 08$a94-6239-096-7 320 $aIncludes bibliographical references. 327 $aIntroduction -- Enumeration Algorithms -- An Application: Biological Graph Analysis -- Telling Stories: Enumerating maximal directed acyclic graphs with constrained set of sources and targets -- Enumerating bubbles: listing pairs of vertex disjoint paths -- Enumerating Cycles and (s,t)-Paths in Undirected Graphs -- Enumerating Diametral and Radial vertices and computing Diameter and Radius of a graph -- Conclusions. 330 $aIn this work we plan to revise the main techniques for enumeration algorithms and to show four examples of enumeration algorithms that can be applied to efficiently deal with some biological problems modelled by using biological networks: enumerating central and peripheral nodes of a network, enumerating stories, enumerating paths or cycles, and enumerating bubbles. Notice that the corresponding computational problems we define are of more general interest and our results hold in the case of arbitrary graphs. Enumerating all the most and less central vertices in a network according to their eccentricity is an example of an enumeration problem whose solutions are polynomial and can be listed in polynomial time, very often in linear or almost linear time in practice. Enumerating stories, i.e. all maximal directed acyclic subgraphs of a graph G whose sources and targets belong to a predefined subset of the vertices, is on the other hand an example of an enumeration problem with an exponential number of solutions, that can be solved by using a non trivial brute-force approach. Given a metabolic network, each individual story should explain how some interesting metabolites are derived from some others through a chain of reactions, by keeping all alternative pathways between sources and targets. Enumerating cycles or paths in an undirected graph, such as a protein-protein interaction undirected network, is an example of an enumeration problem in which all the solutions can be listed through an optimal algorithm, i.e. the time required to list all the solutions is dominated by the time to read the graph plus the time required to print all of them. By extending this result to directed graphs, it would be possible to deal more efficiently with feedback loops and signed paths analysis in signed or interaction directed graphs, such as gene regulatory networks. Finally, enumerating mouths or bubbles with a source s in a directed graph, that is enumerating all the two vertex-disjoint directed paths between the source s and all the possible targets, is an example of an enumeration problem in which all the solutions can be listed through a linear delay algorithm, meaning that the delay between any two consecutive solutions is linear, by turning the problem into a constrained cycle enumeration problem. Such patterns, in a de Bruijn graph representation of the reads obtained by sequencing, are related to polymorphisms in DNA- or RNA-seq data. 410 0$aAtlantis Studies in Computing,$x2212-8565 ;$v6 606 $aAlgorithms 606 $aData mining 606 $aBioinformatics 606 $aAlgorithms 606 $aData Mining and Knowledge Discovery 606 $aComputational and Systems Biology 615 0$aAlgorithms. 615 0$aData mining. 615 0$aBioinformatics. 615 14$aAlgorithms. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputational and Systems Biology. 676 $a570.15118 700 $aMarino$b Andrea$4aut$4http://id.loc.gov/vocabulary/relators/aut$0222755 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299779803321 996 $aAnalysis and enumeration$91522919 997 $aUNINA