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E-grāmata: Machine Learning Approaches To Bioinformatics

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(Univ Of Exeter, Uk)
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Machine Learning Approaches To BioinformaticsPalielināt
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This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. Furthermore, the book includes R codes and example data sets to help readers develop their own bioinformatics research skills. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research.Unlike most of the bioinformatics textbooks on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for undergraduate/graduate teaching.An essential textbook for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects.
Preface v
1 Introduction
1(14)
1.1 Brief history of bioinformatics
3(3)
1.2 Database application in bioinformatics
6(2)
1.3 Web tools and services for sequence homology Alignment
8(2)
1.3.1 Web tools and services for protein functional site identification
9(1)
1.3.2 Web tools and services for other biological data
10(1)
1.4 Pattern analysis
10(1)
1.5 The contribution of information technology
11(1)
1.6
Chapters
12(3)
2 Introduction to Unsupervised Learning
15(9)
3 Probability Density Estimation Approaches
24(14)
3.1 Histogram approach
24(1)
3.2 Parametric approach
25(3)
3.3 Non-parametric approach
28(8)
3.3.1 K-nearest neighbour approach
28(1)
3.3.2 Kernel approach
29(7)
Summary
36(2)
4 Dimension Reduction
38(14)
4.1 General
38(1)
4.2 Principal component analysis
39(3)
4.3 An application of PCA
42(4)
4.4 Multi-dimensional scaling
46(2)
4.5 Application of the Sammon algorithm to gene data
48(2)
Summary
50(2)
5 Cluster Analysis
52(17)
5.1 Hierarchical clustering
52(3)
5.2 K-means
55(3)
5.3 Fuzzy C-means
58(2)
5.4 Gaussian mixture models
60(4)
5.5 Application of clustering algorithms to the Burkholderia pseudomallei gene expression data
64(3)
Summary
67(2)
6 Self-organising Map
69(23)
6.1 Vector quantization
69(4)
6.2 SOM structure
73(2)
6.3 SOM learning algorithm
75(4)
6.4 Using SOM for classification
79(2)
6.5 Bioinformatics applications of VQ and SOM
81(5)
6.5.1 Sequence analysis
81(2)
6.5.2 Gene expression data analysis
83(3)
6.5.3 Metabolite data analysis
86(1)
6.6 A case study of gene expression data analysis
86(2)
6.7 A case study of sequence data analysis
88(2)
Summary
90(2)
7 Introduction to Supervised Learning
92(12)
7.1 General concepts
92(2)
7.2 General definition
94(2)
7.3 Model evaluation
96(5)
7.4 Data organisation
101(2)
7.5 Bayes rule for classification
103(1)
Summary
103(1)
8 Linear/Quadratic Discriminant Analysis and K-nearest Neighbour
104(16)
8.1 Linear discriminant analysis
104(5)
8.2 Generalised discriminant analysis
109(2)
8.3 K-nearest neighbour
111(7)
8.4 KNN for gene data analysis
118(1)
Summary
118(2)
9 Classification and Regression Trees, Random Forest Algorithm
120(13)
9.1 Introduction
120(1)
9.2 Basic principle for constructing a classification tree
121(4)
9.3 Classification and regression tree
125(1)
9.4 CART for compound pathway involvement prediction
126(2)
9.5 The random forest algorithm
128(1)
9.6 RF for analyzing Burkholderia pseudomallei gene expression profiles
129(3)
Summary
132(1)
10 Multi-layer Perceptron
133(21)
10.1 Introduction
133(4)
10.2 Learning theory
137(8)
10.2.1 Parameterization of a neural network
137(1)
10.2.2 Learning rules
137(8)
10.3 Learning algorithms
145(3)
10.3.1 Regression
145(1)
10.3.2 Classification
146(1)
10.3.3 Procedure
147(1)
10.4 Applications to bioinformatics
148(2)
10.4.1 Bio-chemical data analysis
148(1)
10.4.2 Gene expression data analysis
149(1)
10.4.3 Protein structure data analysis
149(1)
10.4.4 Bio-marker identification
150(1)
10.5 A case study on Burkholderia pseudomallei gene expression data
150(3)
Summary
153(1)
11 Basis Function Approach and Vector Machines
154(23)
11.1 Introduction
154(2)
11.2 Radial-basis function neural network (RBFNN)
156(6)
11.3 Bio-basis function neural network
162(6)
11.4 Support vector machine
168(5)
11.5 Relevance vector machine
173(3)
Summary
176(1)
12 Hidden Markov Model
177(18)
12.1 Markov model
177(2)
12.2 Hidden Markov model
179(12)
12.2.1 General definition
179(4)
12.2.2 Handling HMM
183(1)
12.2.3 Evaluation
184(4)
12.2.4 Decoding
188(1)
12.2.5 Learning
189(2)
12.3 HMM for sequence classification
191(3)
Summary
194(1)
13 Feature Selection
195(18)
13.1 Built-in strategy
195(9)
13.1.1 Lasso regression
196(3)
13.1.2 Ridge regression
199(1)
13.1.3 Partial least square regression (PLS) algorithm
200(4)
13.2 Exhaustive strategy
204(1)
13.3 Heuristic strategy - orthogonal least square approach
204(4)
13.4 Criteria for feature selection
208(4)
13.4.1 Correlation measure
209(1)
13.4.2 Fisher ratio measure
210(1)
13.4.3 Mutual information approach
210(2)
Summary
212(1)
14 Feature Extraction (Biological Data Coding)
213(12)
14.1 Molecular sequences
214(1)
14.2 Chemical compounds
215(1)
14.3 General definition
216(1)
14.4 Sequence analysis
216(8)
14.4.1 Peptide feature extraction
216(6)
14.4.2 Whole sequence feature extraction
222(2)
Summary
224(1)
15 Sequence/Structural Bioinformatics Foundation - Peptide Classification
225(13)
15.1 Nitration site prediction
225(5)
15.2 Plant promoter region prediction
230(7)
Summary
237(1)
16 Gene Network - Causal Network and Bayesian Networks
238(15)
16.1 Gene regulatory network
238(3)
16.2 Causal networks, networks, graphs
241(1)
16.3 A brief review of the probability
242(3)
16.4 Discrete Bayesian network
245(1)
16.5 Inference with discrete Bayesian network
246(1)
16.6 Learning discrete Bayesian network
247(1)
16.7 Bayesian networks for gene regulartory networks
247(1)
16.8 Bayesian networks for discovering peptide patterns
248(1)
16.9 Bayesian networks for analysing Burkholderia pseudomallei gene data
249(3)
Summary
252(1)
17 S-Systems
253(16)
17.1 Michealis-Menten change law
253(3)
17.2 S-system
256(3)
17.3 Simplification of an S-system
259(1)
17.4 Approaches for structure identification and parameter estimation
260(2)
17.4.1 Neural network approach
260(1)
17.4.2 Simulated annealing approach
261(1)
17.4.3 Evolutionary computation approach
262(1)
17.5 Steady-state analysis of an S-system
262(5)
17.6 Sensitivity of an S-system
267(1)
Summary
268(1)
18 Future Directions
269(10)
18.1 Multi-source data
270(2)
18.2 Gene regulatory network construction
272(2)
18.3 Building models using incomplete data
274(1)
18.4 Biomarker detection from gene expression data
275(3)
Summary
278(1)
References 279(40)
Index 319
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