Statistical modelling of molecular descriptors in QSAR/QSPR [[electronic resource] /] / edited by Matthias Dehmer, Kurt Varmuza, and Danail Bonchev

• Record Nr.: UNINA9910130957503321
• Pubbl/distr/stampa: Weinheim, : Wiley-VCH; [Chichester, : John Wiley, distributor], c2012
• ISBN: 3-527-64501-2; 1-283-59696-2; 9786613909411; 3-527-64502-0; 3-527-64512-8
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (458 p.)
• Collana: Quantitative and network biology ; ; v. 2
• Altri autori (Persone): DehmerMatthias <1968->; VarmuzaKurt <1942->; BonchevDanail
• Disciplina: 572.80285
• Soggetti: Bioinformatics; Molecules - Models - Computer simulation
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Statistical Modelling of Molecular Descriptors in QSAR/QSPR; Contents; Preface; List of Contributors; 1 Current Modeling Methods Used in QSAR/QSPR; 1.1 Introduction; 1.2 Modeling Methods; 1.2.1 Methods for Regression Problems; 1.2.1.1 Multiple Linear Regression; 1.2.1.2 Partial Least Squares; 1.2.1.3 Feedforward Backpropagation Neural Network; 1.2.1.4 General Regression Neural Network; 1.2.1.5 Gaussian Processes; 1.2.2 Methods for Classification Problems; 1.2.2.1 Logistic Regression; 1.2.2.2 Linear Discriminant Analysis; 1.2.2.3 Decision Tree and Random Forest; 1.2.2.4 k-Nearest Neighbor; 1.2.2.5 Probabilistic Neural Network1.2.2.6 Support Vector Machine; 1.3 Software for QSAR Development; 1.3.1 Structure Drawing or File Conversion; 1.3.2 3D Structure Generation; 1.3.3 Descriptor Calculation; 1.3.4 Modeling; 1.3.5 General purpose; 1.4 Conclusion; References; 2 Developing Best Practices for Descriptor-Based Property ; Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations; 2.1 Introduction; 2.1.1 Posing the Question; 2.1.2 Validating the Models; 2.1.3 Interpreting the Models; 2.2 Leveraging Experimental Data and Understanding their Limitations; 2.3 Descriptors: The Lexicon of QSARs2.3.1 Classical QSAR Descriptors and Uses; 2.3.2 Experimentally Derived Descriptors; 2.3.2.1 Biodescriptors; 2.3.2.2 Descriptors from Spectroscopy/Spectrometry and Microscopy; 2.3.3 0D, 1D and 2D Computational Descriptors; 2.3.4 3D Descriptors and Beyond; 2.3.5 Local Molecular Surface Property Descriptors; 2.3.6 Quantum Chemical Descriptors; 2.4 Machine Learning Methods: The Grammar of QSARs; 2.4.1 Principal Component Analysis; 2.4.2 Factor Analysis; 2.4.3 Multidimensional Scaling, Stochastic Proximity Embedding, and Other Nonlinear Dimensionality Reduction Methods2.4.4 Clustering; 2.4.5 Partial Least Squares (PLS); 2.4.6 k-Nearest Neighbors (kNN); 2.4.7 Neural Networks; 2.4.8 Ensemble Models; 2.4.9 Decision Trees and Random Forests; 2.4.10 Kernel Methods; 2.4.11 Ranking Methods; 2.5 Defining Modeling Strategies: Putting It All Together; 2.6 Conclusions; References; 3 Mold2 Molecular Descriptors for QSAR; 3.1 Background; 3.1.1 History of QSAR; 3.1.2 Introduction to QSAR; 3.1.3 Molecular Descriptors: Bridge for QSAR; 3.1.3.1 Molecular Descriptors3.1.3.2 Role of Molecular Descriptors; 3.1.3.3 Types of Molecular Descriptors; 3.1.3.4 Calculation of Molecular Descriptors (Software Packages); 3.2 Mold2 Molecular Descriptors; 3.2.1 Description of Mold2 Descriptors; 3.2.1.1 Topological Descriptors; 3.2.1.2 Constitutional Descriptors; 3.2.1.3 Information Content-based Descriptors; 3.2.2 Calculation of Mold2 Descriptors; 3.2.3 Evaluation of Mold2 Descriptors; 3.2.3.1 Information Content by Shannon Entropy Analysis; 3.2.3.2 Correlations between Descriptors; 3.3 QSAR Using Mold2 Descriptors; 3.3.1 Classification Models based on Mold2 Descriptors
• Sommario/riassunto: This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR.The high-profile international author and editor team ensures excellent coverage of the topic, making th

Green, Pervasive, and Cloud Computing : 12th International Conference, GPC 2017, Cetara, Italy, May 11-14, 2017, Proceedings / / edited by Man Ho Allen Au, Arcangelo Castiglione, Kim-Kwang Raymond Choo, Francesco Palmieri, Kuan-Ching Li

• Record Nr.: UNINA9910483970603321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
• ISBN: 9783319571867; 3319571869
• Edizione: [1st ed. 2017.]
• Descrizione fisica: 1 online resource (XX, 815 p. 268 illus.)
• Collana: Theoretical Computer Science and General Issues, , 2512-2029 ; ; 10232
• Disciplina: 004.6782
• Soggetti: Computer networks; Application software; Software engineering; Information storage and retrieval systems; Algorithms; Artificial intelligence; Computer Communication Networks; Computer and Information Systems Applications; Software Engineering; Information Storage and Retrieval; Artificial Intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Cryptography, security and biometric techniques -- Advances network services, algorithms and optimization -- Mobile and pervasive computing -- Cybersecurity -- Parallel and distributed computing -- Ontologies and smart applications -- Healthcare support systems.
• Sommario/riassunto: This book constitutes the proceedings of the 12th International Conference on Green, Pervasive, and Cloud Computing, GPC 2017, held in Cetara, Italy, in May 2017 and the following colocated workshops: ; First International Workshop on Digital Knowledge Ecosystems 2017; and First Workshop on Cloud Security Modeling, Monitoring and Management, CS3M 2017. The 58 full papers included in this volume were carefully reviewed and selected from 169 initial submissions. They deal with cryptography, security and biometric techniques; advances network services, algorithms and optimization; mobile and pervasive computing; cybersecurity; parallel and distributed computing; ontologies and smart applications; and healthcare support systems.