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Bioinformatics: An Introduction Softcover reprint of the original 3rd ed. 2015 [Pehme köide]

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  • Formaat: Paperback / softback, 308 pages, kõrgus x laius: 235x155 mm, kaal: 4978 g, 34 Illustrations, black and white; XIX, 308 p. 34 illus., 1 Paperback / softback
  • Sari: Computational Biology 21
  • Ilmumisaeg: 12-Oct-2016
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1447168658
  • ISBN-13: 9781447168652
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Bioinformatics: An Introduction Softcover reprint of the original 3rd ed. 2015Suurem pilt
  • Formaat: Paperback / softback, 308 pages, kõrgus x laius: 235x155 mm, kaal: 4978 g, 34 Illustrations, black and white; XIX, 308 p. 34 illus., 1 Paperback / softback
  • Sari: Computational Biology 21
  • Ilmumisaeg: 12-Oct-2016
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1447168658
  • ISBN-13: 9781447168652
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781447168652.html
Märksõnad:
This comprehensive textbook presents a self-contained guide to bioinformatics, defined in its broadest sense as the application of information science to biology. Thoroughly updated and greatly expanded, this third edition now includes material on the growing array of “-omics”; covering metagenomics, toxicogenomics, glycomics, lipidomics, microbiomics and phenomics. New chapters have also been added on ecosystems management and the nervous system. Emphasis is placed on providing both a firm grounding in the core concepts and a clear overview of the complete field of bioinformatics. Features: explains the fundamentals of information science relevant to biology; covers both organismal (ontogeny and phylogeny, as well as genome structure) and molecular aspects; examines the most important practical applications of bioinformatics, providing detailed descriptions of both the experimental process and the data analysis; provides a varied selection of problems throughout the book, to stimulate further thinking.

Arvustused

The book gives short introductions to many topics, providing an overview of the field and leading readers to pursue specific areas as necessary. It can serve as an excellent supplement to a textbook used in bioinformatics or computational biology courses. The audience is advanced students with backgrounds in fields associated with bioinformatics, such as genetics, biostatistics, and computer science. Summing Up: Recommended. Upper-division undergraduates and graduate students. (M. C. Pavao, Choice, Vol. 53 (11), July, 2016)

1 Introduction
1(8)
1.1 What is Bioinformatics?
2(1)
1.2 What Can Bioinformatics Do?
3(6)
References
6(3)
Part I Information
2 The Nature of Information
9(24)
2.1 Structure and Quantity
15(2)
2.1.1 The Generation of Information
15(1)
2.1.2 Conditional and Unconditional Information
15(1)
2.1.3 Experiments and Observations
16(1)
2.2 Constraint
17(6)
2.2.1 The Value of Information
21(2)
2.2.2 The Quality of Information
23(1)
2.3 Accuracy, Meaning, and Effect
23(5)
2.3.1 Accuracy
23(1)
2.3.2 Meaning
24(3)
2.3.3 Effect
27(1)
2.3.4 Signifies
28(1)
2.4 Further Remarks on Information Generation
28(1)
2.5 Summary
29(4)
References
31(2)
3 The Transmission of Information
33(16)
3.1 The Capacity of a Channel
36(1)
3.2 Coding
37(2)
3.3 Decoding
39(1)
3.4 Compression
40(4)
3.4.1 Use of Compression to Measure Distance
43(1)
3.4.2 Ergodicity
43(1)
3.5 Noise
44(2)
3.6 Error Correction
46(1)
3.7 Summary
47(2)
References
48(1)
4 Sets and Combinatorics
49(6)
4.1 The Notion of Set
49(1)
4.2 Combinatorics
49(4)
4.2.1 Ordered Sampling with Replacement
50(1)
4.2.2 Ordered Sampling Without Replacement
50(1)
4.2.3 Unordered Sampling Without Replacement
51(1)
4.2.4 Unordered Sampling with Replacement
52(1)
4.3 The Binomial Theorem
53(2)
5 Probability and Likelihood
55(16)
5.1 The Notion of Probability
55(1)
5.2 Fundamentals
56(6)
5.2.1 Generalized Union
58(1)
5.2.2 Conditional Probability
59(2)
5.2.3 Bernoulli Trials
61(1)
5.3 Moments of Distributions
62(4)
5.3.1 Runs
64(1)
5.3.2 The Hypergeometric Distribution
65(1)
5.3.3 Multiplicative Processes
65(1)
5.4 Likelihood
66(3)
5.5 The Maximum Entropy Method
69(2)
References
69(2)
6 Randomness and Complexity
71(14)
6.1 Random Processes
74(1)
6.2 Markov Chains
75(2)
6.3 Random Walks
77(1)
6.4 Noise
78(2)
6.5 Complexity
80(5)
References
83(2)
7 Systems, Networks, and Circuits
85(16)
7.1 General Systems Theory
86(5)
7.1.1 Automata
88(1)
7.1.2 Cellular Automata
89(1)
7.1.3 Percolation
90(1)
7.2 Networks (Graphs)
91(4)
7.2.1 Trees
93(1)
7.2.2 Complexity Parameters
94(1)
7.2.3 Dynamical Properties
94(1)
7.3 Synergetics
95(3)
7.3.1 Some Examples
96(1)
7.3.2 Reception and Generation of Information
96(1)
7.3.3 Habituation
97(1)
7.4 Evolutionary Systems
98(3)
References
99(2)
8 Algorithms
101(16)
8.1 Evolutionary Computing
102(1)
8.2 Pattern Recognition
103(1)
8.3 Botryology
104(5)
8.3.1 Clustering
105(3)
8.3.2 Principal Component and Linear Discriminant Analyses
108(1)
8.3.3 Wavelets
108(1)
8.4 Multidimensional Scaling and Sedation
109(2)
8.5 Visualization
111(6)
References
112(5)
Part II Biology
9 Introduction to Part II
117(12)
9.1 Genotype, Phenotype, and Species
117(2)
9.2 Adaptation
119(1)
9.3 Timescales of Adaptation
120(2)
9.3.1 The Role of Memory
121(1)
9.3.2 The Integrating Role of Directive Correlation
121(1)
9.4 Regulation
122(1)
9.5 The Concept of Machine
123(1)
9.6 The Architecture of Functional Systems
124(1)
9.7 Biological Complexity
125(2)
9.8 Self-Organization
127(1)
9.9 Cybernetics
127(2)
References
128(1)
10 The Nature of Living Things
129(46)
10.1 The Cell
129(2)
10.1.1 The Structure of a Cell
131(1)
10.2 Mitochondria
131(2)
10.2.1 Observational Overview
132(1)
10.3 Metabolism
133(2)
10.4 The Cell Cycle
135(12)
10.4.1 The Chromosome
137(3)
10.4.2 The Structures of Genome and Genes
140(3)
10.4.3 The C-Value Paradox
143(3)
10.4.4 The Structure of the Chromosome
146(1)
10.5 The Immune System
147(1)
10.6 Molecular Mechanisms
148(5)
10.6.1 Replication
149(1)
10.6.2 Proofreading and Repair
149(1)
10.6.3 Recombination
150(2)
10.6.4 Summary of Sources of Genome Variation
152(1)
10.7 Gene Expression
153(5)
10.7.1 Transcription
153(1)
10.7.2 Regulation of Transcription
154(1)
10.7.3 Prokaryotic Transcriptional Regulation
154(1)
10.7.4 Eukaryotic Transcriptional Regulation
155(2)
10.7.5 mRNA Processing
157(1)
10.7.6 Translation
158(1)
10.8 Ontogeny (Development)
158(6)
10.8.1 Stem Cells
160(1)
10.8.2 Epigenesis
161(1)
10.8.3 The Epigenetic Landscape
162(1)
10.8.4 r and K Selection
162(1)
10.8.5 Homeotic Genes
163(1)
10.9 Phylogeny and Evolution
164(11)
10.9.1 Group and Kin Selection
166(1)
10.9.2 Models of Evolution
167(2)
10.9.3 Further Remarks on Sources of Genome Variation
169(1)
10.9.4 The Origin of Proteins
170(1)
10.9.5 Taxonomy and Geological Eras
170(2)
References
172(3)
11 The Molecules of Life
175(22)
11.1 Molecules and Supramolecular Structure
175(2)
11.2 Water
177(1)
11.3 DNA
178(5)
11.4 RNA
183(2)
11.5 Proteins
185(6)
11.5.1 Amino Acids
186(2)
11.5.2 Protein Folding and Interaction
188(2)
11.5.3 Experimental Techniques for Protein Structure Determination
190(1)
11.5.4 Protein Structure Overview
191(1)
11.6 Polysaccharides
191(1)
11.7 Lipids
192(5)
References
194(3)
Part III Applications
12 Introduction to Part III
197(6)
References
201(2)
13 Genomics
203(20)
13.1 DNA Sequencing
204(3)
13.1.1 Extraction of Nucleic Acids
205(1)
13.1.2 The Polymerase Chain Reaction
205(1)
13.1.3 Sequencing
205(2)
13.1.4 Expressed Sequence Tags
207(1)
13.2 DNA Methylation Profiling
207(1)
13.3 Gene Identification
207(1)
13.4 Extrinsic Methods
208(5)
13.4.1 Database Reliability
209(1)
13.4.2 Sequence Comparison and Alignment
209(2)
13.4.3 Trace, Alignment and Listing
211(1)
13.4.4 Dynamic Programming Algorithms
212(1)
13.5 Intrinsic Methods
213(2)
13.5.1 Signals
214(1)
13.5.2 Hidden Markov Models
215(1)
13.6 Beyond Sequence
215(1)
13.7 Minimalist Approaches
216(2)
13.8 Phylogenies
218(2)
13.9 Metagenomics
220(3)
References
221(2)
14 Proteomics
223(18)
14.1 Transcriptomics
224(4)
14.2 Proteomics
228(4)
14.2.1 Two-Dimensional Gel Electrophoresis
230(1)
14.2.2 Column Chromatography
231(1)
14.2.3 Other Kinds of Electrophoresis
232(1)
14.3 Protein Identification
232(1)
14.4 Isotope-Coded Affinity Tags
233(1)
14.5 Protein Microarrays
234(1)
14.6 Protein Expression Patterns---Temporal and Spatial
235(1)
14.7 The Kinome
236(1)
14.8 Biochemical Signalling
237(4)
References
238(3)
15 The Glycome, Lipidome and Microbiome
241(2)
15.1 Glycomics
241(1)
15.2 Lipidomics
241(1)
15.3 Microbiomics
242(1)
References
242(1)
16 Interactomics: Interactions and Regulatory Networks
243(18)
16.1 Inference of Regulatory Networks
247(1)
16.2 The Physical Chemistry of Interactions
247(3)
16.3 Intermolecular Interactions
250(3)
16.4 In Vivo Experimental Methods
253(2)
16.4.1 The Yeast Two-Hybrid Assay
254(1)
16.4.2 Crosslinking
254(1)
16.4.3 Correlated Expression
255(1)
16.4.4 Other Methods
255(1)
16.5 In Vitro Experimental Methods
255(4)
16.5.1 Chromatography
256(1)
16.5.2 Direct Affinity Measurement
257(1)
16.5.3 Protein Chips
258(1)
16.6 Interactions from Sequence
259(1)
16.7 Global Statistics of Interactions
259(2)
References
260(1)
17 The Nervous System
261(4)
17.1 The Neuron and Neural Networks
262(1)
17.2 Outstanding Problems
263(1)
17.3 Artificial Neural Networks
264(1)
References
264(1)
18 Metabolomics and Metabonomics
265(6)
18.1 Data Collection
266(1)
18.2 Data Analysis
267(1)
18.3 Metabolic Regulation
268(1)
18.3.1 Metabolic Control Analysis
268(1)
18.3.2 The Metabolic Code
269(1)
18.4 Metabolic Networks
269(2)
References
270(1)
19 Phenomics
271(4)
19.1 Polygenic Disease
271(1)
19.2 Activity-Based Protein Profiling
272(1)
19.3 Phenotype Microarrays
272(1)
19.4 Ethomics
273(1)
19.5 Modelling Life
273(2)
References
274(1)
20 Medical Applications
275(12)
20.1 The Genetic Basis of Disease
276(1)
20.2 Cancer
277(2)
20.3 Toward Automated Diagnosis
279(1)
20.4 Drug Discovery and Testing
280(1)
20.5 Nanodrugs
281(1)
20.6 Personalized Medicine
282(1)
20.7 Bacterial Multiresistance
283(1)
20.8 Toxicogenomics
284(1)
20.9 Reprogramming Stem Cells
284(1)
20.10 Tracing Genetically Modified Ingredients in Food
285(2)
References
285(2)
21 Ecosystems Management
287(4)
References
289(2)
22 The Organization of Knowledge
291(8)
22.1 Ontology
292(1)
22.2 Knowledge Representation
293(1)
22.3 The Problem of Bacterial Identification
294(1)
22.4 Text Mining
295(2)
22.5 The Automation of Research
297(2)
References
298(1)
Bibliography 299(4)
Index 303
Dr. Jeremy Ramsden is a Professor of Nanotechnology in the Clore Laboratory at the University of Buckingham, UK.