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Numerical Methods in Bioinformatics An Introduction |
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Author(s): Jiasong Wang |
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Publisher: Science Press Published Date: 2013 ISBN: 9787030369857 Pages: 199 Language: English Type: Book Cover: Paper Cover |
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Our Price: $65.00
Avail: In-Stock |
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Main Contents
PREFACE
CHAPTER 1 SOME BIOLOGICAL CONCEPTS
1.1 Cell
1.2 Genetic Material:DNA,Gene and RNA
1.2.1 DNA
1.2.2 Gene
1.2.3 RNA
1.3 Protein and Amino Acids
1.4 Chromosome
1.5 Omics
1.5.1 Genomics
1.5.2 Microarray
1.5.3 Proteomics
1.5.4 Lipidomics
REFERENCES
CHAPTER 2 GRAPHICAL REPRESENTATIONS OF DNA SEQUENCE
2.1 Three-Dimension(3-D)Graphical Representation
2.2 2-D Graphical Representation
2.3 2-D Graphical Representations Without Degeneracy
2.4 Used a 1-D Numerical Representation of four Nucleotides to
Construct a 2-D Graphical Representation of the DNA Sequence
REFERENCES
CHAPTER 3 NUMERICAL REPRESENTATIONS OF DNA SEQUENCE
3.1 4-D and 3-D Numerical Representations of a DNA Sequence
3.2 2-D Numerical Representations of a DNA Sequence
3.3 The Complex Numerical Representation
3.4 1-D Numerical Representations of four Nucleotides and 2-D
GraphicalRepresentation of a DNA Sequence
3.5 The Representations of Feature Vector,Genome Space and Matrix
Representation of DNA Sequence
3.6 The Numerical Representation Based on Physical,Chemical and
Structural Properties of DNA Sequence
3.6.1 The numerical representations based on some attribute
equivalences of nucleotides
3.6.2 The representation of DNA by the inspiration from codon and
the idea of three attribute equivalences
3.6.3 EIIP numerical representation for nucleotides
REFERENCES
CHAPTER 4 NUMERICAL REPRESENTATIONS OF PROTEIN
4.1 1-D Numerical and Graphical Representations of the Amino Acid
Sequence
4.2 2-D Numerical and Graphical Representations of the Amino Acid
Sequence
4.3 A 2-D Graphical Representation and Moment Vector
Representation of Protein
4.4 3-D Numerical Representation of Protein
4.5 The 10-D Representation of an Amino Acid
4.6 The Vector and Matrix Representations of Protein Sequence and
Protein Space
4.7 Other Schemes of the Representation for Protein
REFERENCES
CHAPTER 5 PRACTICAL ORTHOGONAL TRANSFORM
5.1 Some Features and Algorithms for the Discrete Fourier
Transform
5.1.1 Fourier transforms of the original sequence and its
subsequence
5.1.2 The independency of the Fourier transforms at several
frequencies
5.1.3 The Fourier transform of symbolic sequence
5.1.4 Fourier transform of binary sequence
5.1.5 Several algorithms of Fourier transform
5.1.6 The properties of Fourier transform of real sequence
5.2 Wavelet Analysis
5.2.1 Introduction
5.2.2 Multiresolution analysis of a function by Haar scaling and
wavelet function
5.2.3 Construction of wavelet systems
5.2.4 Mallet transform
REFERENCES
CHAPTER 6 IDENTIFYING PROTEIN-CODING REGIONS(EXONS)BYNUCLEOTIDE
DISTRIBUTIONS
6.1 Portein Coding Regions Finding in DNA Sequence
6.1.1 Introduction
6.1.2 The stochastic simulation and several computing
formulae
6.1.3 FEND algorithm,predicting protein coding regions from
nucleotide distributions on the three positions of a DNA
sequence
6.1.4 Performance evaluation of FEND algorithm
6.2 The Experiment for Distinguishing Exon and Intron Sequences by
a Threshold
6.2.1 Motivation
6.2.2 Idea of distinguishing exon and intron sequences
6.2.3 Results and discussion
REFERENCES
CHAPTER 7 PROTEIN COMPARISON BY ORTHOGONAL TRANSFORMS
7.1 Protein Comparison by Discrete Fourier
Transformation(DFT)
7.1.1 EIIP representation of protein sequence
7.1.2 Symmetry of discrete Fourier transform of real
sequence
7.1.3 Cross-spectral function
7.2 Protein Comparison by Discrete Wavelet Transformation
7.2.1 Several techniques needed for DWT method
7.2.2 The performance of the DWT method
REFERENCES
CHAPTER 8 THE APPLICATION OF VECTOR REPRESENTATIONSTO BIOLOGICAL
MOLECULE ANALYSIS
8.1 Use Feature Vector to Analyze DNA Sequences
8.1.1 Feature vector representation of DNA sequence
8.1.2 Comparing DNA sequences
8.2 A Protein Map and its Applications
8.2.1 Recalling a 2-D graphical representation and moment vector
representation of protein
8.2.2 Protein map and cluster analysis
8.3 An Appendix:Introduction to Cluster Analysis
REFERENCES
CHAPTER 9 THE STATISTICS ANALYSIS OF LARGE AMOUNT OF EXPERIMENTAL
DATA
9.1 A Way to Process Microarray Data
9.1.1 Data form
9.1.2 Microarray data set
9.1.3 Preliminary filtering
9.1.4 Assessing normalization
9.1.5 Hypothesis test
9.1.6 Conclusion
9.2 The Statistical Analysis of a Set of Lipidomics Data
9.2.1 Introduction
9.2.2 Statistical techniques of initial data processing
9.2.3 Initial data arrangement
9.2.4 Hypothesis testing analysis
REFERENCES
CHAPTER 10 APPLY SINGULAR VALUE DECOMPOSITION TO MICROARRAY
ANALYSIS
10.1 SVD,PCA and GSVD
10.1.1 Singular value decomposition
10.1.2 Principal component analysis
10.1.3 Generalized singular value decomposition
10.2 Apply SVD/PCA to Microarray Analysis
10.3 GSVD Analyzes the Microarray Data
REFERENCES
CHAPTER 11 DYNAMICAL ANALYSIS MODELS OF GENE EXPRESSIO
11.1 Differential Equations Model of Gene Expression
11.1.1 Transcription model
11.1.2 Nonlinear dynamic equations
11.1.3 Linearization of the nonlinear transcription model
11.1.4 Approximating coefficient matrix M by Fourier series
11.1.5 Solution to transcription matrix C and V
11.2 Modified Linear Differential Equations Model
11.3 Dynamical Model Based on Singular Value Decomposition
11.3.1 Introduction
11.3.2 Reducing gene?s number
11.3.3 The approach based on singular value
decomposition(SVD)
11.3.4 The methods of solving dynamical models
REFERENCES
CHAPTER 12 MISSING MICROARRAY DATA INPUTTING
12.1 The Ad Hoc Methods
12.2 Missing Data Inputting Based on SVD
12.2.1 A new way for missing data inputting
12.2.2 Other method based on SVD
12.3 Weighted K-Nearest Neighbors,KNN,Impute Algorithm
12.4 Estimation of Missing Values in Microarray Data Based on the
Least Square Principle
12.4.1 Least squares estimate of the unknown iable
12.4.2 The least square estimation of missing data based on
genes
12.4.3 The least square estimation of missing data based on
arrays
12.4.4 Combining the gene and array based estimates
12.5.1 Selecting genes
12.5.2 Gene-wise formulation of local least squares
imputation
12.5 Local Least Square Inputting(LLSinpute)
12.6 The Comparison of the Methods of Missing Data Inputting
REFERENCES
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