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Algebraic graph algorithms : a practical guide using Python / / K. Erciyes
| Algebraic graph algorithms : a practical guide using Python / / K. Erciyes |
| Autore | Erciyes Kayhan |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (229 pages) |
| Disciplina | 511.5 |
| Collana | Undergraduate Topics in Computer Science |
| Soggetto topico |
Graph algorithms
Python (Computer program language) |
| ISBN |
9783030878863
9783030878856 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464410103316 |
Erciyes Kayhan
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Algebraic Graph Algorithms : A Practical Guide Using Python / / by K. Erciyes
| Algebraic Graph Algorithms : A Practical Guide Using Python / / by K. Erciyes |
| Autore | Erciyes Kayhan |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (229 pages) |
| Disciplina | 511.5 |
| Collana | Undergraduate Topics in Computer Science |
| Soggetto topico |
Computer science
Computer science - Mathematics Discrete mathematics Theory of Computation Discrete Mathematics in Computer Science Mathematical Applications in Computer Science Python (Llenguatge de programació) Algorismes Àlgebra |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030878863
9783030878856 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Graphs, Matrices and Matroids -- 3. Parallel Matrix Algorithm Kernel -- 4. Basic Graph Algorithms -- 5. Connectivity, Matching and Matroids -- 6. Subgraph Search -- 7. Analysis of Large Graphs -- 8. Clustering in Complex Networks -- 9. Kronecker Graphs -- 10. Sample Algorithms for Complex Networks. |
| Record Nr. | UNINA-9910510574003321 |
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Erciyes Kayhan
|
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Distributed and Sequential Algorithms for Bioinformatics / / by Kayhan Erciyes
| Distributed and Sequential Algorithms for Bioinformatics / / by Kayhan Erciyes |
| Autore | Erciyes Kayhan |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XVII, 367 p. 157 illus. in color.) |
| Disciplina | 570.285 |
| Collana | Computational Biology |
| Soggetto topico |
Bioinformatics
Algorithms Systems biology Biological systems Computer science—Mathematics Computational Biology/Bioinformatics Algorithm Analysis and Problem Complexity Systems Biology Math Applications in Computer Science |
| ISBN | 3-319-24966-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Introduction -- 1.2 Biological Sequences -- 1.3 Biological Networks -- 1.4 The Need for Distributed Algorithms -- 1.5 Outline of the Book -- Part IBackground -- 2 Introduction to Molecular Biology -- 2.1 Introduction -- 2.2 The Cell -- 2.2.1 DNA -- 2.2.2 RNA -- 2.2.3 Genes -- 2.2.4 Proteins -- 2.3 Central Dogma of Life -- 2.3.1 Transcription -- 2.3.2 The Genetic Code -- 2.3.3 Translation -- 2.3.4 Mutations -- 2.4 Biotechnological Methods -- 2.4.1 Cloning -- 2.4.2 Polymerase Chain Reaction -- 2.4.3 DNA Sequencing -- 2.5 Databases -- 2.5.1 Nucleotide Databases -- 2.5.2 Protein Sequence Databases -- 2.6 Human Genome Project -- 2.7 Chapter Notes -- 3 Graphs, Algorithms, and Complexity -- 3.1 Introduction -- 3.2 Graphs -- 3.2.1 Types of Graphs -- 3.2.2 Graph Representations -- 3.2.3 Paths, Cycles, and Connectivity -- 3.2.4 Trees -- 3.2.5 Spectral Properties of Graphs -- 3.3 Algorithms -- 3.3.1 Time and Space Complexities -- 3.3.2 Recurrences -- 3.3.3 Fundamental Approaches -- 3.3.4 Dynamic Programming -- 3.3.5 Graph Algorithms -- 3.3.6 Special Subgraphs -- 3.4 NP-Completeness -- 3.4.1 Reductions -- 3.4.2 Coping with NP-Completeness -- 3.5 Chapter Notes -- 4 Parallel and Distributed Computing -- 4.1 Introduction -- 4.2 Architectures for Parallel and Distributed Computing -- 4.2.1 Interconnection Networks -- 4.2.2 Multiprocessors and Multicomputers -- 4.2.3 Flynn's Taxonomy -- 4.3 Parallel Computing -- 4.3.1 Complexity of Parallel Algorithms -- 4.3.2 Parallel Random Access Memory Model -- 4.3.3 Parallel Algorithm Design Methods -- 4.3.4 Shared Memory Programming -- 4.3.5 Multi-threaded Programming -- 4.3.6 Parallel Processing in UNIX -- 4.4 Distributed Computing -- 4.4.1 Distributed Algorithm Design -- 4.4.2 Threads Re-visited -- 4.4.3 Message Passing Interface.
4.4.4 Distributed Processing in UNIX -- 4.5 Chapter Notes -- Part IIBiological Sequences -- 5 String Algorithms -- 5.1 Introduction -- 5.2 Exact String Matching -- 5.2.1 Sequential Algorithms -- 5.2.2 Distributed String Matching -- 5.3 Approximate String Matching -- 5.4 Longest Subsequence Problems -- 5.4.1 Longest Common Subsequence -- 5.4.2 Longest Increasing Subsequence -- 5.5 Suffix Trees -- 5.5.1 Construction of Suffix Trees -- 5.5.2 Applications of Suffix Trees -- 5.5.3 Suffix Arrays -- 5.6 Chapter Notes -- 6 Sequence Alignment -- 6.1 Introduction -- 6.2 Problem Statement -- 6.2.1 The Objective Function -- 6.2.2 Scoring Matrices for Proteins -- 6.3 Pairwise Alignment -- 6.3.1 Global Alignment -- 6.3.2 Local Alignment -- 6.4 Multiple Sequence Alignment -- 6.4.1 Center Star Method -- 6.4.2 Progressive Alignment -- 6.5 Alignment with Suffix Trees -- 6.6 Database Search -- 6.6.1 FASTA -- 6.6.2 BLAST -- 6.7 Parallel and Distributed Sequence Alignment -- 6.7.1 Parallel and Distributed SW Algorithm -- 6.7.2 Distributed BLAST -- 6.7.3 Parallel/Distributed CLUSTALW -- 6.8 Chapter Notes -- 7 Clustering of Biological Sequences -- 7.1 Introduction -- 7.2 Analysis -- 7.2.1 Distance and Similarity Measures -- 7.2.2 Validation of Cluster Quality -- 7.3 Classical Methods -- 7.3.1 Hierarchical Algorithms -- 7.3.2 Partitional Algorithms -- 7.3.3 Other Methods -- 7.4 Clustering Algorithms Targeting Biological Sequences -- 7.4.1 Alignment-Based Clustering -- 7.4.2 Other Similarity-Based Methods -- 7.4.3 Graph-Based Clustering -- 7.5 Distributed Clustering -- 7.5.1 Hierarchical Clustering -- 7.5.2 k-means Clustering -- 7.5.3 Graph-Based Clustering -- 7.5.4 Review of Existing Algorithms -- 7.6 Chapter Notes -- 8 Sequence Repeats -- 8.1 Introduction -- 8.2 Tandem Repeats -- 8.2.1 Stoye and Gusfield Algorithm -- 8.2.2 Distributed Tandem Repeat Search. 8.3 Sequence Motifs -- 8.3.1 Probabilistic Approaches -- 8.3.2 Combinatorial Methods -- 8.3.3 Parallel and Distributed Motif Search -- 8.3.4 A Survey of Recent Distributed Algorithms -- 8.4 Chapter Notes -- 9 Genome Analysis -- 9.1 Introduction -- 9.2 Gene Finding -- 9.2.1 Fundamental Methods -- 9.2.2 Hidden Markov Models -- 9.2.3 Nature Inspired Methods -- 9.2.4 Distributed Gene Finding -- 9.3 Genome Rearrangement -- 9.3.1 Sorting by Reversals -- 9.3.2 Unsigned Reversals -- 9.3.3 Signed Reversals -- 9.3.4 Distributed Genome Rearrangement Algorithms -- 9.4 Haplotype Inference -- 9.4.1 Problem Statement -- 9.4.2 Clark's Algorithm -- 9.4.3 EM Algorithm -- 9.4.4 Distributed Haplotype Inference Algorithms -- 9.5 Chapter Notes -- Part IIIBiological Networks -- 10 Analysis of Biological Networks -- 10.1 Introduction -- 10.2 Networks in the Cell -- 10.2.1 Metabolic Networks -- 10.2.2 Gene Regulation Networks -- 10.2.3 Protein Interaction Networks -- 10.3 Networks Outside the Cell -- 10.3.1 Networks of the Brain -- 10.3.2 Phylogenetic Networks -- 10.3.3 The Food Web -- 10.4 Properties of Biological Networks -- 10.4.1 Distance -- 10.4.2 Vertex Degrees -- 10.4.3 Clustering Coefficient -- 10.4.4 Matching Index -- 10.5 Centrality -- 10.5.1 Degree Centrality -- 10.5.2 Closeness Centrality -- 10.5.3 Betweenness Centrality -- 10.5.4 Eigenvalue Centrality -- 10.6 Network Models -- 10.6.1 Random Networks -- 10.6.2 Small World Networks -- 10.6.3 Scale-Free Networks -- 10.6.4 Hierarchical Networks -- 10.7 Module Detection -- 10.8 Network Motifs -- 10.9 Network Alignment -- 10.10 Chapter Notes -- 11 Cluster Discovery in Biological Networks -- 11.1 Introduction -- 11.2 Analysis -- 11.2.1 Quality Metrics -- 11.2.2 Classification of Clustering Algorithms -- 11.3 Hierarchical Clustering -- 11.3.1 MST-Based Clustering -- 11.3.2 Edge-Betweenness-Based Clustering. 11.4 Density-Based Clustering -- 11.4.1 Clique Algorithms -- 11.4.2 k-core Decomposition -- 11.4.3 Highly Connected Subgraphs Algorithm -- 11.4.4 Modularity-Based Clustering -- 11.5 Flow Simulation-Based Approaches -- 11.5.1 Markov Clustering Algorithm -- 11.5.2 Distributed Markov Clustering Algorithm Proposal -- 11.6 Spectral Clustering -- 11.7 Chapter Notes -- 12 Network Motif Search -- 12.1 Introduction -- 12.2 Problem Statement -- 12.2.1 Methods of Motif Discovery -- 12.2.2 Relation to Graph Isomorphism -- 12.2.3 Frequency Concepts -- 12.2.4 Random Graph Generation -- 12.2.5 Statistical Significance -- 12.3 A Review of Sequential Motif Searching Algorithms -- 12.3.1 Network Centric Algorithms -- 12.3.2 Motif Centric Algorithms -- 12.4 Distributed Motif Discovery -- 12.4.1 A General Framework -- 12.4.2 Review of Distributed Motif Searching Algorithms -- 12.4.3 Wang et al.'s Algorithm -- 12.4.4 Schatz et al.'s Algorithm -- 12.4.5 Riberio et al.'s Algorithms -- 12.5 Chapter Notes -- 13 Network Alignment -- 13.1 Introduction -- 13.2 Problem Statement -- 13.2.1 Relation to Graph Isomorphism -- 13.2.2 Relation to Bipartite Graph Matching -- 13.2.3 Evaluation of Alignment Quality -- 13.2.4 Network Alignment Methods -- 13.3 Review of Sequential Network Alignment Algorithms -- 13.3.1 PathBlast -- 13.3.2 IsoRank -- 13.3.3 MaWIsh -- 13.3.4 GRAAL -- 13.3.5 Recent Algorithms -- 13.4 Distributed Network Alignment -- 13.4.1 A Distributed Greedy Approximation Algorithm Proposal -- 13.4.2 Distributed Hoepman's Algorithm -- 13.4.3 Distributed Auction Algorithms -- 13.5 Chapter Notes -- 14 Phylogenetics -- 14.1 Introduction -- 14.2 Terminology -- 14.3 Phylogenetic Trees -- 14.3.1 Distance-Based Algorithms -- 14.3.2 Maximum Parsimony -- 14.3.3 Maximum Likelihood -- 14.4 Phylogenetic Networks -- 14.4.1 Reconstruction Methods -- 14.5 Chapter Notes -- 15 Epilogue. 15.1 Introduction -- 15.2 Current Challenges -- 15.2.1 Big Data Analysis -- 15.2.2 Disease Analysis -- 15.2.3 Bioinformatics Education -- 15.3 Specific Challenges -- 15.3.1 Sequence Analysis -- 15.3.2 Network Analysis -- 15.4 Future Directions -- 15.4.1 Big Data Gets Bigger -- 15.4.2 New Paradigms on Disease Analysis -- 15.4.3 Personalized Medicine -- Index. |
| Record Nr. | UNINA-9910299257803321 |
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Erciyes Kayhan
|
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Distributed Graph Algorithms for Computer Networks / / by Kayhan Erciyes
| Distributed Graph Algorithms for Computer Networks / / by Kayhan Erciyes |
| Autore | Erciyes Kayhan |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | London : , : Springer London : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (327 p.) |
| Disciplina | 004.36 |
| Collana | Computer Communications and Networks |
| Soggetto topico |
Algorithms
Computer communication systems Computer science—Mathematics Algorithm Analysis and Problem Complexity Computer Communication Networks Math Applications in Computer Science |
| ISBN | 1-4471-5173-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Part I: Fundamental Algorithms -- Graphs -- The Computational Model -- Spanning Tree Construction -- Graph Traversals -- Minimal Spanning Trees -- Routing -- Self-Stabilization -- Part II: Graph Theoretical Algorithms -- Vertex Coloring -- Maximal Independent Sets -- Dominating Sets -- Matching -- Vertex Cover -- Part III: Ad Hoc Wireless Networks -- Introduction -- Topology Control -- Ad Hoc Routing -- Sensor Network Applications -- ASSIST: A Simulator to Develop Distributed Algorithms -- Pseudocode Conventions -- ASSIST Code -- Applications Using ASSIST. |
| Record Nr. | UNINA-9910741142203321 |
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Erciyes Kayhan
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| London : , : Springer London : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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