Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang |
Pubbl/distr/stampa | Singapore ; ; Hong Kong, : World Scientific, c2007 |
Descrizione fisica | 1 online resource (352 p.) |
Disciplina | 570.28563 |
Altri autori (Persone) |
BandyopadhyaySanghamitra <1968->
MaulikUjjwal WangJason T. L |
Collana | Science, engineering, and biology informatics |
Soggetto topico |
Bioinformatics
Soft computing |
Soggetto genere / forma | Electronic books. |
ISBN |
1-281-91864-4
9786611918644 981-270-889-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
CONTENTS; Preface; Part I OVERVIEW; Chapter 1 Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments Haixu Tang and Sun Kim; 1 Introduction; 2 Recent Development of Classical Topics; 2.1 Sequence alignment; 2.2 Genome sequencing and fragment assembly; 2.3 Gene annotation; 2.4 RNA folding; 2.5 Motif finding; 2.6 Protein structure prediction; 3 Emerging Topics from New Genome Technologies; 3.1 Comparative genomics: beyond genome comparison; 3.2 Pathway reconstruction; 3.3 Microarray analysis; 3.4 Proteomics; 3.5 Protein-protein interaction; 4 Conclusion
AcknowledgementReferences; Chapter 2 An Introduction to Soft Computing Amit Konar and Swagatam Das; 1 Classical AI and its Pitfalls; 2 What is Soft Computing?; 3 Fundamental Components of Soft Computing; 3.1 Fuzzy sets and fuzzy logic; 3.2 Neural networks; 3.3 Genetic algorithms; 3.4 Belief networks; 4 Synergism in Soft Computing; 4.1 Neuro-fuzzy synergism; 4.2 Neuro-GA synergism; 4.3 Fuzzy-GA synergism; 4.4 Neuro-belief network synergism; 4.5 GA-belief network synergism; 4.6 Neuro-fuzzy-GA synergism; 5 Some Emerging Areas of Soft Computing; 5.1 Artificial life 5.2 Particle swarm optimization (PSO)5.3 Artificial immune system; 5.4 Rough sets and granular computing; 5.5 Chaos theory; 5.6 Ant colony systems (ACS); 6 Summary; References; Part II BIOLOGICAL SEQUENCE AND STRUCTURE ANALYSIS; Chapter 3 Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound José E. Gallardo, Carlos Cotta and Antonio J. Fernández; 1 Introduction; 2 A Crash Introduction to Phylogenetic Inference; 3 Evolutionary Algorithms for the Phylogeny Problem; 4 A BnB Algorithm for Phylogenetic Inference; 5 A Memetic Algorithm for Phylogenetic Inference 6 A Hybrid Algorithm7 Experimental Results; 7.1 Experimental setting; 7.2 Sensitivity analysis on the hybrid algorithm; 7.3 Analysis of results; 8 Conclusions; Acknowledgment; References; Chapter 4 Classification ofRNASequences with Support Vector Machines Jason T. L. Wang and Xiaoming Wu; 1 Introduction; 2 Count Kernels and Marginalized Count Kernels; 2.1 RNA sequences with known secondary structures; 2.2 RNA sequences with unknown secondary structures; 3 Kernel Based on Labeled Dual Graphs; 3.1 Labeled dual graphs; 3.2 Marginalized kernel for labeled dual graphs; 4 A New Kernel 4.1 Extracting features for global structural information4.2 Extracting features for local structural information; 5 Experiments and Results; 5.1 Data and parameters; 5.2 Results; 6 Conclusion; Acknowledgment; References; Chapter 5 Beyond String Algorithms: Protein Sequence Analysis using Wavelet Transforms Arun Krishnan and Kuo-Bin Li; 1 Introduction; 1.1 String algorithms; 1.2 Sequence analysis; 1.3 Wavelet transform; 2 Motif Searching; 2.1 Introduction; 2.2 Methods; 2.3 Results; 2.4 Allergenicity prediction; 3 Transmembrane Helix Region (HTM) Prediction; 4 Hydrophobic Cores 5 Protein Repeat Motifs |
Record Nr. | UNINA-9910451542303321 |
Singapore ; ; Hong Kong, : World Scientific, c2007 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang |
Pubbl/distr/stampa | Singapore ; ; Hong Kong, : World Scientific, c2007 |
Descrizione fisica | 1 online resource (352 p.) |
Disciplina | 570.28563 |
Altri autori (Persone) |
BandyopadhyaySanghamitra <1968->
MaulikUjjwal WangJason T. L |
Collana | Science, engineering, and biology informatics |
Soggetto topico |
Bioinformatics
Soft computing |
ISBN |
1-281-91864-4
9786611918644 981-270-889-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
CONTENTS; Preface; Part I OVERVIEW; Chapter 1 Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments Haixu Tang and Sun Kim; 1 Introduction; 2 Recent Development of Classical Topics; 2.1 Sequence alignment; 2.2 Genome sequencing and fragment assembly; 2.3 Gene annotation; 2.4 RNA folding; 2.5 Motif finding; 2.6 Protein structure prediction; 3 Emerging Topics from New Genome Technologies; 3.1 Comparative genomics: beyond genome comparison; 3.2 Pathway reconstruction; 3.3 Microarray analysis; 3.4 Proteomics; 3.5 Protein-protein interaction; 4 Conclusion
AcknowledgementReferences; Chapter 2 An Introduction to Soft Computing Amit Konar and Swagatam Das; 1 Classical AI and its Pitfalls; 2 What is Soft Computing?; 3 Fundamental Components of Soft Computing; 3.1 Fuzzy sets and fuzzy logic; 3.2 Neural networks; 3.3 Genetic algorithms; 3.4 Belief networks; 4 Synergism in Soft Computing; 4.1 Neuro-fuzzy synergism; 4.2 Neuro-GA synergism; 4.3 Fuzzy-GA synergism; 4.4 Neuro-belief network synergism; 4.5 GA-belief network synergism; 4.6 Neuro-fuzzy-GA synergism; 5 Some Emerging Areas of Soft Computing; 5.1 Artificial life 5.2 Particle swarm optimization (PSO)5.3 Artificial immune system; 5.4 Rough sets and granular computing; 5.5 Chaos theory; 5.6 Ant colony systems (ACS); 6 Summary; References; Part II BIOLOGICAL SEQUENCE AND STRUCTURE ANALYSIS; Chapter 3 Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound José E. Gallardo, Carlos Cotta and Antonio J. Fernández; 1 Introduction; 2 A Crash Introduction to Phylogenetic Inference; 3 Evolutionary Algorithms for the Phylogeny Problem; 4 A BnB Algorithm for Phylogenetic Inference; 5 A Memetic Algorithm for Phylogenetic Inference 6 A Hybrid Algorithm7 Experimental Results; 7.1 Experimental setting; 7.2 Sensitivity analysis on the hybrid algorithm; 7.3 Analysis of results; 8 Conclusions; Acknowledgment; References; Chapter 4 Classification ofRNASequences with Support Vector Machines Jason T. L. Wang and Xiaoming Wu; 1 Introduction; 2 Count Kernels and Marginalized Count Kernels; 2.1 RNA sequences with known secondary structures; 2.2 RNA sequences with unknown secondary structures; 3 Kernel Based on Labeled Dual Graphs; 3.1 Labeled dual graphs; 3.2 Marginalized kernel for labeled dual graphs; 4 A New Kernel 4.1 Extracting features for global structural information4.2 Extracting features for local structural information; 5 Experiments and Results; 5.1 Data and parameters; 5.2 Results; 6 Conclusion; Acknowledgment; References; Chapter 5 Beyond String Algorithms: Protein Sequence Analysis using Wavelet Transforms Arun Krishnan and Kuo-Bin Li; 1 Introduction; 1.1 String algorithms; 1.2 Sequence analysis; 1.3 Wavelet transform; 2 Motif Searching; 2.1 Introduction; 2.2 Methods; 2.3 Results; 2.4 Allergenicity prediction; 3 Transmembrane Helix Region (HTM) Prediction; 4 Hydrophobic Cores 5 Protein Repeat Motifs |
Record Nr. | UNINA-9910784816603321 |
Singapore ; ; Hong Kong, : World Scientific, c2007 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Analysis of biological data : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang |
Edizione | [1st ed.] |
Pubbl/distr/stampa | Singapore ; ; Hong Kong, : World Scientific, c2007 |
Descrizione fisica | 1 online resource (352 p.) |
Disciplina | 570.28563 |
Altri autori (Persone) |
BandyopadhyaySanghamitra <1968->
MaulikUjjwal WangJason T. L |
Collana | Science, engineering, and biology informatics |
Soggetto topico |
Bioinformatics
Soft computing |
ISBN |
1-281-91864-4
9786611918644 981-270-889-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
CONTENTS; Preface; Part I OVERVIEW; Chapter 1 Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments Haixu Tang and Sun Kim; 1 Introduction; 2 Recent Development of Classical Topics; 2.1 Sequence alignment; 2.2 Genome sequencing and fragment assembly; 2.3 Gene annotation; 2.4 RNA folding; 2.5 Motif finding; 2.6 Protein structure prediction; 3 Emerging Topics from New Genome Technologies; 3.1 Comparative genomics: beyond genome comparison; 3.2 Pathway reconstruction; 3.3 Microarray analysis; 3.4 Proteomics; 3.5 Protein-protein interaction; 4 Conclusion
AcknowledgementReferences; Chapter 2 An Introduction to Soft Computing Amit Konar and Swagatam Das; 1 Classical AI and its Pitfalls; 2 What is Soft Computing?; 3 Fundamental Components of Soft Computing; 3.1 Fuzzy sets and fuzzy logic; 3.2 Neural networks; 3.3 Genetic algorithms; 3.4 Belief networks; 4 Synergism in Soft Computing; 4.1 Neuro-fuzzy synergism; 4.2 Neuro-GA synergism; 4.3 Fuzzy-GA synergism; 4.4 Neuro-belief network synergism; 4.5 GA-belief network synergism; 4.6 Neuro-fuzzy-GA synergism; 5 Some Emerging Areas of Soft Computing; 5.1 Artificial life 5.2 Particle swarm optimization (PSO)5.3 Artificial immune system; 5.4 Rough sets and granular computing; 5.5 Chaos theory; 5.6 Ant colony systems (ACS); 6 Summary; References; Part II BIOLOGICAL SEQUENCE AND STRUCTURE ANALYSIS; Chapter 3 Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound José E. Gallardo, Carlos Cotta and Antonio J. Fernández; 1 Introduction; 2 A Crash Introduction to Phylogenetic Inference; 3 Evolutionary Algorithms for the Phylogeny Problem; 4 A BnB Algorithm for Phylogenetic Inference; 5 A Memetic Algorithm for Phylogenetic Inference 6 A Hybrid Algorithm7 Experimental Results; 7.1 Experimental setting; 7.2 Sensitivity analysis on the hybrid algorithm; 7.3 Analysis of results; 8 Conclusions; Acknowledgment; References; Chapter 4 Classification ofRNASequences with Support Vector Machines Jason T. L. Wang and Xiaoming Wu; 1 Introduction; 2 Count Kernels and Marginalized Count Kernels; 2.1 RNA sequences with known secondary structures; 2.2 RNA sequences with unknown secondary structures; 3 Kernel Based on Labeled Dual Graphs; 3.1 Labeled dual graphs; 3.2 Marginalized kernel for labeled dual graphs; 4 A New Kernel 4.1 Extracting features for global structural information4.2 Extracting features for local structural information; 5 Experiments and Results; 5.1 Data and parameters; 5.2 Results; 6 Conclusion; Acknowledgment; References; Chapter 5 Beyond String Algorithms: Protein Sequence Analysis using Wavelet Transforms Arun Krishnan and Kuo-Bin Li; 1 Introduction; 1.1 String algorithms; 1.2 Sequence analysis; 1.3 Wavelet transform; 2 Motif Searching; 2.1 Introduction; 2.2 Methods; 2.3 Results; 2.4 Allergenicity prediction; 3 Transmembrane Helix Region (HTM) Prediction; 4 Hydrophobic Cores 5 Protein Repeat Motifs |
Record Nr. | UNINA-9910827968903321 |
Singapore ; ; Hong Kong, : World Scientific, c2007 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Application of Computational Intelligence to Biology / / edited by Ravi Bhramaramba, Akula Chandra Sekhar |
Edizione | [1st ed. 2016.] |
Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2016 |
Descrizione fisica | 1 online resource (107 p.) |
Disciplina | 570.28563 |
Collana | SpringerBriefs in Forensic and Medical Bioinformatics |
Soggetto topico |
Computational intelligence
Bioinformatics Diabetes Proteins Biomedical engineering Health informatics Computational Intelligence Computational Biology/Bioinformatics Protein Structure Biomedical Engineering and Bioengineering Health Informatics |
ISBN | 981-10-0391-2 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Enhancing the performance of Multi-parameter Patient Monitors by Homogeneous Kernel Maps -- Augmenting the performance of Multi-patient Parameter Monitoring system -- An Efficient Classification Model based on Ensemble of Fuzzy-Rough Classifier for Analysis of Medical Data -- A Comparative Study of Various Minutiae Extraction Methods for Fingerprint Recognition Based on Score Level Fusion -- Hybrid Model for Analysis of Abnormalities in Diabetic Cardiomyopathy -- Computational Screening of DrugBank DataBase for Novel Cell Cycle Inhibitors -- Pathway analysis of highly conserved Mitogen Activated Protein Kinases (MAPKs) -- Identification of drug targets from integrated database of diabetes mellitus Genes using Protein-Protein Interactions -- Distributed Data Mining for modeling and prediction of skin condiction in Cosmetic Industry - A Rough Set Theory Approach. |
Record Nr. | UNINA-9910254239703321 |
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2016 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Intelligent bioinformatics [[electronic resource] ] : the application of artificial intelligence techniques to bioinformatics problems / / Edward Keedwell and Ajit Narayanan |
Autore | Keedwell Edward |
Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2005 |
Descrizione fisica | 1 online resource (294 p.) |
Disciplina |
570.28563
570/.285 |
Altri autori (Persone) | NarayananAjit <1952-> |
Soggetto topico |
Artificial intelligence - Biological applications
Bioinformatics |
Soggetto genere / forma | Electronic books. |
ISBN |
1-280-28753-5
9786610287536 0-470-01572-1 0-470-02176-4 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intelligent Bioinformatics; Contents; Preface; Acknowledgement; PART 1 INTRODUCTION; 1 Introduction to the Basics of Molecular Biology; 1.1 Basic cell architecture; 1.2 The structure, content and scale of deoxyribonucleic acid (DNA); 1.3 History of the human genome; 1.4 Genes and proteins; 1.5 Current knowledge and the 'central dogma'; 1.6 Why proteins are important; 1.7 Gene and cell regulation; 1.8 When cell regulation goes wrong; 1.9 So, what is bioinformatics?; 1.10 Summary of chapter; 1.11 Further reading; 2 Introduction to Problems and Challenges in Bioinformatics; 2.1 Introduction
2.2 Genome2.3 Transcriptome; 2.4 Proteome; 2.5 Interference technology, viruses and the immune system; 2.6 Summary of chapter; 2.7 Further reading; 3 Introduction to Artificial Intelligence and Computer Science; 3.1 Introduction to search; 3.2 Search algorithms; 3.3 Heuristic search methods; 3.4 Optimal search strategies; 3.5 Problems with search techniques; 3.6 Complexity of search; 3.7 Use of graphs in bioinformatics; 3.8 Grammars, languages and automata; 3.9 Classes of problems; 3.10 Summary of chapter; 3.11 Further reading; PART 2 CURRENT TECHNIQUES; 4 Probabilistic Approaches 4.1 Introduction to probability4.2 Bayes' Theorem; 4.3 Bayesian networks; 4.4 Markov networks; 4.5 Summary of chapter; 4.6 References; 5 Nearest Neighbour and Clustering Approaches; 5.1 Introduction; 5.2 Nearest neighbour method; 5.3 Nearest neighbour approach for secondary structure protein folding prediction; 5.4 Clustering; 5.5 Advanced clustering techniques; 5.6 Application guidelines; 5.7 Summary of chapter; 5.8 References; 6 Identification (Decision) Trees; 6.1 Method; 6.2 Gain criterion; 6.3 Over fitting and pruning; 6.4 Application guidelines; 6.5 Bioinformatics applications 6.6 Background6.7 Summary of chapter; 6.8 References; 7 Neural Networks; 7.1 Method; 7.2 Application guidelines; 7.3 Bioinformatics applications; 7.4 Background; 7.5 Summary of chapter; 7.6 References; 8 Genetic Algorithms; 8.1 Single-objective genetic algorithms - method; 8.2 Single-objective genetic algorithms - example; 8.3 Multi-objective genetic algorithms - method; 8.4 Application guidelines; 8.5 Genetic algorithms - bioinformatics applications; 8.6 Summary of chapter; 8.7 References and further reading; PART 3 FUTURE TECHNIQUES; 9 Genetic Programming; 9.1 Method 9.2 Application guidelines9.3 Bioinformatics applications; 9.4 Background; 9.5 Summary of chapter; 9.6 References; 10 Cellular Automata; 10.1 Method; 10.2 Application guidelines; 10.3 Bioinformatics applications; 10.4 Background; 10.5 Summary of chapter; 10.6 References and further reading; 11 Hybrid Methods; 11.1 Method; 11.2 Neural-genetic algorithm for analysing gene expression data; 11.3 Genetic algorithm and k nearest neighbour hybrid for biochemistry solvation; 11.4 Genetic programming neural networks for determining gene - gene interactions in epidemiology; 11.5 Application guidelines 11.6 Conclusions |
Record Nr. | UNINA-9910143742503321 |
Keedwell Edward | ||
Hoboken, NJ, : Wiley, c2005 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Intelligent bioinformatics [[electronic resource] ] : the application of artificial intelligence techniques to bioinformatics problems / / Edward Keedwell and Ajit Narayanan |
Autore | Keedwell Edward |
Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2005 |
Descrizione fisica | 1 online resource (294 p.) |
Disciplina |
570.28563
570/.285 |
Altri autori (Persone) | NarayananAjit <1952-> |
Soggetto topico |
Artificial intelligence - Biological applications
Bioinformatics |
ISBN |
1-280-28753-5
9786610287536 0-470-01572-1 0-470-02176-4 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intelligent Bioinformatics; Contents; Preface; Acknowledgement; PART 1 INTRODUCTION; 1 Introduction to the Basics of Molecular Biology; 1.1 Basic cell architecture; 1.2 The structure, content and scale of deoxyribonucleic acid (DNA); 1.3 History of the human genome; 1.4 Genes and proteins; 1.5 Current knowledge and the 'central dogma'; 1.6 Why proteins are important; 1.7 Gene and cell regulation; 1.8 When cell regulation goes wrong; 1.9 So, what is bioinformatics?; 1.10 Summary of chapter; 1.11 Further reading; 2 Introduction to Problems and Challenges in Bioinformatics; 2.1 Introduction
2.2 Genome2.3 Transcriptome; 2.4 Proteome; 2.5 Interference technology, viruses and the immune system; 2.6 Summary of chapter; 2.7 Further reading; 3 Introduction to Artificial Intelligence and Computer Science; 3.1 Introduction to search; 3.2 Search algorithms; 3.3 Heuristic search methods; 3.4 Optimal search strategies; 3.5 Problems with search techniques; 3.6 Complexity of search; 3.7 Use of graphs in bioinformatics; 3.8 Grammars, languages and automata; 3.9 Classes of problems; 3.10 Summary of chapter; 3.11 Further reading; PART 2 CURRENT TECHNIQUES; 4 Probabilistic Approaches 4.1 Introduction to probability4.2 Bayes' Theorem; 4.3 Bayesian networks; 4.4 Markov networks; 4.5 Summary of chapter; 4.6 References; 5 Nearest Neighbour and Clustering Approaches; 5.1 Introduction; 5.2 Nearest neighbour method; 5.3 Nearest neighbour approach for secondary structure protein folding prediction; 5.4 Clustering; 5.5 Advanced clustering techniques; 5.6 Application guidelines; 5.7 Summary of chapter; 5.8 References; 6 Identification (Decision) Trees; 6.1 Method; 6.2 Gain criterion; 6.3 Over fitting and pruning; 6.4 Application guidelines; 6.5 Bioinformatics applications 6.6 Background6.7 Summary of chapter; 6.8 References; 7 Neural Networks; 7.1 Method; 7.2 Application guidelines; 7.3 Bioinformatics applications; 7.4 Background; 7.5 Summary of chapter; 7.6 References; 8 Genetic Algorithms; 8.1 Single-objective genetic algorithms - method; 8.2 Single-objective genetic algorithms - example; 8.3 Multi-objective genetic algorithms - method; 8.4 Application guidelines; 8.5 Genetic algorithms - bioinformatics applications; 8.6 Summary of chapter; 8.7 References and further reading; PART 3 FUTURE TECHNIQUES; 9 Genetic Programming; 9.1 Method 9.2 Application guidelines9.3 Bioinformatics applications; 9.4 Background; 9.5 Summary of chapter; 9.6 References; 10 Cellular Automata; 10.1 Method; 10.2 Application guidelines; 10.3 Bioinformatics applications; 10.4 Background; 10.5 Summary of chapter; 10.6 References and further reading; 11 Hybrid Methods; 11.1 Method; 11.2 Neural-genetic algorithm for analysing gene expression data; 11.3 Genetic algorithm and k nearest neighbour hybrid for biochemistry solvation; 11.4 Genetic programming neural networks for determining gene - gene interactions in epidemiology; 11.5 Application guidelines 11.6 Conclusions |
Record Nr. | UNINA-9910830706603321 |
Keedwell Edward | ||
Hoboken, NJ, : Wiley, c2005 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Multimedia and Network Information Systems : Proceedings of the 11th International Conference MISSI 2018 / / edited by Kazimierz Choroś, Marek Kopel, Elżbieta Kukla, Andrzej Siemiński |
Edizione | [1st ed. 2019.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
Descrizione fisica | 1 online resource (604 pages) |
Disciplina | 570.28563 |
Collana | Advances in Intelligent Systems and Computing |
Soggetto topico |
Computational intelligence
Artificial intelligence Computers Computational Intelligence Artificial Intelligence Information Systems and Communication Service |
ISBN | 3-319-98678-3 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910484892203321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|