Title Page -- Copyright Page -- Brief Contents -- Contents -- Preface -- List of Symbols -- About the Authors -- Part One Introduction -- Chapter 1 Introduction to Bioinformatics -- 1.1 Introduction -- 1.2 The ``Omics ́́Era -- 1.3 The Scope of Bioinformatics -- 1.3.1 Areas of Molecular Biology Subject to Bioinformatics Analysis -- 1.3.2 Data Storage, Retrieval and Organisation -- 1.3.3 Data Analysis -- 1.3.4 Statistical Analysis -- 1.3.5 Presentation -- 1.4 What Do Information Engineers and Biologists Need to Know? -- 1.5 Discussion and Summary -- References -- Chapter 2 Computational Methods in Bioinformatics -- 2.1 Introduction -- 2.2 Machine Learning and Data Mining -- 2.2.1 Supervised Learning -- 2.2.2 Unsupervised Learning -- 2.3 Optimisation -- 2.4 Image Processing: Bioimage Informatics -- 2.5 Network Analysis -- 2.6 Statistical Analysis -- 2.7 Software Tools and Technologies -- 2.8 Discussion and Summary -- References -- Part Two Introduction to Molecular Biology -- Chapter 3 The Living Cell -- 3.1 Introduction -- 3.2 Prokaryotes and Eukaryotes -- 3.3 Multicellularity -- 3.3.1 Unicellular and Multicellular Organisms -- 3.3.2 Stem Cells and Cell Differentiation -- 3.4 Cell Components -- 3.4.1 Plasma Membrane and Transport Proteins -- 3.4.2 Cytoplasm -- 3.4.3 Extracellular Matrix -- 3.4.4 Centrosome and Microtubules -- 3.4.5 Actin Filaments and the Cytoskeleton -- 3.4.6 Nucleus -- 3.4.7 Vesicles -- 3.4.8 Ribosomes -- 3.4.9 Endoplasmic Reticulum -- 3.4.10 Golgi Apparatus -- 3.4.11 Mitochondrion and the Energy of the Cell -- 3.4.12 Lysosome -- 3.4.13 Peroxisome -- 3.5 Discussion and Summary -- References -- Chapter 4 Central Dogma of Molecular Biology -- 4.1 Introduction -- 4.2 Central Dogma of Molecular Biology Overview -- 4.3 Proteins -- 4.4 DNA -- 4.5 RNA -- 4.6 Genes -- 4.7 Transcription and Post-transcriptional Processes.

4.7.1 Post-transcriptional Processes -- 4.7.2 Gene-specific TFs -- 4.7.3 Post-transcriptional Regulation -- 4.8 Translation and Post-translational Processes -- 4.8.1 The Genetic Code -- 4.8.2 tRNAand Ribosomes -- 4.8.3 The Steps of Translation -- 4.8.4 Polyribosomes (Polysomes) -- 4.8.5 Post-translational Processes -- 4.9 Discussion and Summary -- References -- Part Three Data Acquisition and Pre-processing -- Chapter 5 High-throughput Technologies -- 5.1 Introduction -- 5.2 Microarrays -- 5.2.1 DNAMicroarrays -- 5.2.2 Protein Microarrays -- 5.2.3 Carbohydrate Microarrays (Glycoarrays) -- 5.2.4 Other Types of Microarrays -- 5.3 Next-generation Sequencing (NGS) -- 5.3.1 DNA Sequencing -- 5.3.2 RNA Sequencing (Transcripome Analysis) -- 5.3.3 Metagenomics -- 5.3.4 Other Applications of Sequencing -- 5.4 ChIPon Microarrays and Sequencing -- 5.5 Discussion and Summary -- References -- Chapter 6 Databases, Standards and Annotation -- 6.1 Introduction -- 6.2 NCBI Databases -- 6.2.1 Literature Databases (PubMed, PMC, the Bookshelf and MeSH) -- 6.2.2 GenBank (Nucleotide Database) -- 6.2.3 Reference Sequences (RefSeq) Database -- 6.2.4 Gene Database -- 6.2.5 Protein Database -- 6.2.6 Gene Expression Omnibus -- 6.2.7 Taxonomy and HomoloGene Databases -- 6.2.8 Sequence Read Archive -- 6.2.9 Genomic and Epigenomic Variations -- 6.2.10 Other NCBI Databases -- 6.3 The EBI Databases -- 6.4 Species-specific Databases -- 6.4.1 Animals -- 6.4.2 Plants -- 6.4.3 Fungi -- 6.4.4 Archaea and Bacteria -- 6.4.5 Viruses -- 6.5 Discussion and Summary -- References -- Chapter 7 Normalisation -- 7.1 Introduction -- 7.2 Issues Tackled by Normalisation -- 7.2.1 Within-slide and Between-slides Normalisation -- 7.2.2 Normalisation Based on Non-differentially Expressed Genes -- 7.2.3 Background Correction -- 7.2.4 Logarithmic Transformation.

7.2.5 Intensity-dependent Bias - (MA) Plots -- 7.2.6 Replicates and Summarisation -- 7.3 Normalisation Methods -- 7.3.1 Microarray Suite 5 (MAS5.0) -- 7.3.2 Robust Multi-array Average (RMA) -- 7.3.3 Quantile Normalisation -- 7.3.4 Locally Weighted Scatter-plot Smoothing (Lowess) Normalisation -- 7.3.5 Scaling Methods -- 7.3.6 Model-based Expression Index (MBEI) -- 7.3.7 Other Normalisation Methods -- 7.4 Discussion and Summary -- References -- Chapter 8 Feature Selection -- 8.1 Introduction -- 8.2 FS and FG - Problem Definition -- 8.3 Consecutive Ranking -- 8.3.1 Forward Search (Most Informative First Admitted) -- 8.3.2 Backward Elimination (Least Useful First Eliminated) -- 8.4 Individual Ranking -- 8.4.1 Information Content -- 8.4.2 SNR Criteria -- 8.5 Principal Component Analysis -- 8.6 Genetic Algorithms and Genetic Programming -- 8.7 Discussion and Summary -- References -- Chapter 9 Differential Expression -- 9.1 Introduction -- 9.2 Fold Change -- 9.3 Statistical Hypothesis Testing - Overview -- 9.3.1 p-Values and Volcano Plots -- 9.3.2 The Multiple-hypothesis Testing Problem -- 9.4 Statistical Hypothesis Testing - Methods -- 9.4.1 t-Statistic, Modified t-Statistics and the Analysis of Variance (ANOVA) -- 9.4.2 B-Statistic -- 9.4.3 Fisherś Exact Test -- 9.4.4 Likelihood Ratio Test -- 9.4.5 Methods for Over-dispersed Poisson Distribution -- 9.5 Discussion and Summary -- References -- Part Four Clustering Methods -- Chapter 10 Clustering Forms -- 10.1 Introduction -- 10.2 Proximity Measures -- 10.2.1 Distance Metrics for Discrete Feature Objects -- 10.2.1.1 Hamming Distance -- 10.2.1.2 Matching Coefficient -- 10.2.2 Distance Metrics for Continuous Feature Objects -- 10.3 Clustering Families -- 10.3.1 Partitional Clustering -- 10.3.2 Hierarchical Clustering -- 10.3.3 Fuzzy Clustering -- 10.3.4 Neural Network-based Clustering.

10.3.5 Mixture Model Clustering -- 10.3.6 Graph-based Clustering -- 10.3.7 Consensus Clustering -- 10.3.8 Biclustering -- 10.4 Clusters and Partitions -- 10.5 Discussion and Summary -- References -- Chapter 11 Partitional Clustering -- 11.1 Introduction -- 11.2 k-Means and its Applications -- 11.2.1 Principles -- 11.2.2 Variations -- 11.2.3 Applications -- 11.3 k-Medoids and its Applications -- 11.3.1 Principles -- 11.3.2 Variations -- 11.3.3 Applications -- 11.4 Discussion and Summary -- References -- Chapter 12 Hierarchical Clustering -- 12.1 Introduction -- 12.2 Principles -- 12.2.1 Agglomerative Methods -- 12.2.2 Divisive Methods -- 12.3 Discussion and Summary -- References -- Chapter 13 Fuzzy Clustering -- 13.1 Introduction -- 13.2 Principles -- 13.2.1 Fuzzy c-Means -- 13.2.2 Probabilistic c-Means -- 13.2.3 Hybrid c-Means -- 13.2.4 Gustafson-Kessel Algorithm -- 13.2.5 Gath-Geva Algorithm -- 13.2.6 Fuzzy c-Shell -- 13.2.7 FANNY -- 13.2.8 Other Fuzzy Clustering Algorithms -- 13.3 Discussion -- References -- Chapter 14 Neural Network-based Clustering -- 14.1 Introduction -- 14.2 Algorithms -- 14.2.1 SOM -- 14.2.2 GLVQ -- 14.2.3 Neural-gas -- 14.2.4 ART -- 14.2.5 OPTOC -- 14.2.6 SOON -- 14.3 Discussion -- References -- Chapter 15 Mixture Model Clustering -- 15.1 Introduction -- 15.2 Finite Mixture Models -- 15.2.1 Various Mixture Models -- 15.2.2 Non-Bayesian Methods -- 15.2.3 Bayesian Methods -- 15.3 Infinite Mixture Models -- 15.3.1 DPM Model -- 15.3.2 CRP Mixture Model -- 15.3.3 SBP Mixture Model -- 15.4 Discussion -- References -- Chapter 16 Graph Clustering -- 16.1 Introduction -- 16.2 Basic Definitions -- 16.2.1 Graph and Adjacency Matrix -- 16.2.2 Measures and Metrics -- 16.2.3 Similarity Matrices -- 16.3 Graph Clustering -- 16.3.1 Graph Cut Clustering -- 16.3.2 Spectral Clustering -- 16.3.3 AP Clustering -- 16.3.4 Modularity-based Clustering.

16.3.5 Multilevel Graph Partitioning and Hypergraph Partitioning -- 16.3.6 Markov Cluster Algorithm -- 16.4 Resources -- 16.5 Discussion -- References -- Chapter 17 Consensus Clustering -- 17.1 Introduction -- 17.2 Overview -- 17.3 Consensus Functions -- 17.3.1 P-P Comparison -- 17.3.2 C-C Comparison -- 17.3.3 MIC Voting -- 17.3.4 M-M Co-occurrence -- 17.4 Discussion -- References -- Chapter 18 Biclustering -- 18.1 Introduction -- 18.2 Overview -- 18.2.1 Statement of the Biclustering Problem -- 18.2.2 Types of Biclusters -- 18.2.3 Classification of Biclustering -- 18.3 Biclustering Methods -- 18.3.1 Variance-minimisation Biclustering Methods -- 18.3.2 Correlation-maximisation Biclustering Methods -- 18.3.3 Two-way Clustering Methods -- 18.3.4 Probabilistic and Generative Methods -- 18.4 Discussion -- References -- Chapter 19 Clustering Methods Discussion -- 19.1 Introduction -- 19.2 Hierarchical Clustering -- 19.2.1 Yeast Cell Cycle Data -- 19.2.2 Breast Cancer -- 19.2.3 Diffuse Large B-Cell Lymphoma -- 19.3 Fuzzy Clustering -- 19.3.1 DNA Motifs Clustering -- 19.3.2 Microarray Gene Expression -- 19.4 Neural Network-based Clustering -- 19.5 Mixture Model-based Clustering -- 19.5.1 Examples of Finite Mixture Models -- 19.5.2 Examples of Infinite Mixture Models -- 19.6 Graph-based Clustering -- 19.7 Consensus Clustering -- 19.8 Biclustering -- 19.9 Summary -- References -- Part Five Validation and Visualisation -- Chapter 20 Numerical Validation -- 20.1 Introduction -- 20.2 External Criteria -- 20.2.1 Rand Index -- 20.2.2 Adjusted Rand Index -- 20.2.3 Jaccard Index -- 20.2.4 Normalised Mutual Information -- 20.3 Internal Criteria -- 20.3.1 Adjusted Figure of Merit -- 20.3.2 CLEST -- 20.4 Relative Criteria -- 20.4.1 Minimum Description Length -- 20.4.2 Minimum Message Length -- 20.4.3 Bayesian Information Criterion.

20.4.4 Akaikeś Information Criterion.