01083nam0 22002893i 450 MIL029852220231121125544.0013536698420160705d1974 ||||0itac50 baengusz01i xxxe z01nLinear circuits for electronics technologyGary M. MillerEnglewood CliffsPrentice-Hallc1974XVI, 332 p.24 cm.Prentice-Hall series in electronic technology001MIL00397802001 Prentice-Hall series in electronic technologyMiller, Gary M.MILV169132070175862ITIT-0120160705IT-FR0099 Biblioteca Area IngegneristicaFR0099 MIL0298522Biblioteca Area Ingegneristica 54DAE Fondo Ch.MIL/3 54VM 0000698975 VM barcode:BAIN003606. - Inventario:809AVMA 2007071320121204 54Linear circuits for electronics technology3610199UNICAS05446nam 22006734a 450 991102017990332120200520144314.09786610287536978128028753412802875359780470015728047001572197804700217670470021764(CKB)1000000000357233(EBL)242936(OCoLC)475962221(SSID)ssj0000180598(PQKBManifestationID)11938928(PQKBTitleCode)TC0000180598(PQKBWorkID)10168113(PQKB)11551266(MiAaPQ)EBC242936(Perlego)2761996(EXLCZ)99100000000035723320050325d2005 uy 0engur|n|---|||||txtccrIntelligent bioinformatics the application of artificial intelligence techniques to bioinformatics problems /Edward Keedwell and Ajit NarayananHoboken, NJ Wileyc20051 online resource (294 p.)Description based upon print version of record.9780470021750 0470021756 Includes bibliographical references and index.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 Introduction2.2 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 Approaches4.1 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 applications6.6 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 Method9.2 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 guidelines11.6 ConclusionsBioinformatics is contributing to some of the most important advances in medicine and biology. At the forefront of this exciting new subject are techniques known as artificial intelligence which are inspired by the way in which nature solves the problems it faces. This book provides a unique insight into the complex problems of bioinformatics and the innovative solutions which make up 'intelligent bioinformatics'. Intelligent Bioinformatics requires only rudimentary knowledge of biology, bioinformatics or computer science and is aimed at interested readers regardless of disciplArtificial intelligenceBiological applicationsBioinformaticsArtificial intelligenceBiological applications.Bioinformatics.570/.285Keedwell Edward1841262Narayanan Ajit1952-52289MiAaPQMiAaPQMiAaPQBOOK9911020179903321Intelligent bioinformatics4420925UNINA