LEADER 05357nam 22006254a 450 001 9910830706603321 005 20230617040711.0 010 $a1-280-28753-5 010 $a9786610287536 010 $a0-470-01572-1 010 $a0-470-02176-4 035 $a(CKB)1000000000357233 035 $a(EBL)242936 035 $a(OCoLC)475962221 035 $a(SSID)ssj0000180598 035 $a(PQKBManifestationID)11938928 035 $a(PQKBTitleCode)TC0000180598 035 $a(PQKBWorkID)10168113 035 $a(PQKB)11551266 035 $a(MiAaPQ)EBC242936 035 $a(EXLCZ)991000000000357233 100 $a20050325d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIntelligent bioinformatics$b[electronic resource] $ethe application of artificial intelligence techniques to bioinformatics problems /$fEdward Keedwell and Ajit Narayanan 210 $aHoboken, NJ $cWiley$dc2005 215 $a1 online resource (294 p.) 300 $aDescription based upon print version of record. 311 $a0-470-02175-6 320 $aIncludes bibliographical references and index. 327 $aIntelligent 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 327 $a2.2 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 327 $a4.1 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 327 $a6.6 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 327 $a9.2 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 327 $a11.6 Conclusions 330 $aBioinformatics 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 discipl 606 $aArtificial intelligence$xBiological applications 606 $aBioinformatics 615 0$aArtificial intelligence$xBiological applications. 615 0$aBioinformatics. 676 $a570.28563 676 $a570/.285 700 $aKeedwell$b Edward$01640821 701 $aNarayanan$b Ajit$f1952-$052289 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830706603321 996 $aIntelligent bioinformatics$93984556 997 $aUNINA