Biodata Mining and Visualization

The following sections are included:

• Preface

• Acknowledgements

• About the Author

• Contents

The following sections are included:

• Cells store large amounts of information in DNA

• Cells process complex information

• Cellular life is chemically complex and somewhat stochastic

• Challenges in analyzing complex biodata

• References

The following sections are included:

• Primary sequence and structure data
  • DNA sequence databases
    • Gene sequence data
    • Species-specific genome data
    • Metagenome data
    • Multiply aligned genome datasets
  • Protein sequence databases
  • Molecular structure databases
    • Protein structures
    • Chemical structures
    • Epigenomic data (methylation, histone codes)

• Secondary annotation data
  • Motif annotations
    • DNA motif databases
    • Protein motif databases
  • Gene function annotations
  • Genomic annotations
  • Inter-species phylogeny and gene family annotations

• Experimental and personalized data
  • DNA expression profiles
  • Proteomics data and degradomics
  • Protein expression profiles, 2D gel and protein interaction data
  • Metabolomics and metabolic pathway databases
  • Personalized data
    • SNP data
    • E-health data

• Semantic and processed text data
  • Ontologies
    • Bioinformatics ontologies
    • Medical ontologies
  • Text-mined annotation data

• Integrated and federated databases

• References

The following sections are included:

• DNA/RNA motif discovery
  • Single motif models: MEME, AlignAce etc.
  • Multiple motif models: LOGOS and MotifRegressor
  • Informative k-mers approach

• Protein motif discovery
  • InterProScan and other traditional methods
  • Protein k-mer and other string based methods

• Genetic algorithms, particle swarms and ant colonies
  • Genetic algorithms
  • Particle swarm optimization
  • Ant colony optimization

• Sequence visualization

• References

The following sections are included:

• Alignment-based methods
  • Pairwise genome-wide search algorithms: LAGAN, AVID etc
  • Multiple alignment methods: MLAGAN, MAVID, MULTIZ etc.
  • Dotplots
  • Visualization of genome comparisons
  • Global motif maps

• Alignmentless methods
  • K-mer based methods
  • Average common substring and compressibility based methods
  • 2D portraits of genomes
    • Fractal genome displays
    • DNA walks
    • Visualizing Chargaff's rule: nucleotide skews

• Genome scale non-sequence data analysis
  • DNA physical structure based methods
  • Secondary structure based comparisons
    • DNA quadruplex forming motifs
    • RNA secondary structure motifs

• References

The following sections are included:

• Molecule structures as graphs or strings
  • 3D to 1D transformations
  • Graph matching methods
  • Graph visualization
  • Graph grammars

• RNA structure comparison and prediction

• Image comparison based methods
  • Gabor filter based methods
  • Image symmetry set based methods
  • Other graph topology based methods

• References

The following sections are included:

• Annotation ontologies

• Gene Ontology based mining

• Sequence similarity based function prediction

• Cellular location prediction

• New integrative methods: Utilizing networks

• Text mining bioliterature for automated annotation
  • Natural language processing (NLP)
    • Integrating text mining with other data
  • Semantic profiling
  • Matrix factorization methods
    • Latent Semantic Indexing
    • Non-negative Matrix Factorization

• References

The following sections are included:

• SVM kernels

• SVM trees

• Methods for microarray data
  • Gene selection algorithms
  • Gene selection by consistency methods

• Genome as a time series and discrete wavelet transform

• Parameterless clustering for gene expression

• Transductive confidence machines, conformal predictors and ROC isometrics

• Text compression methods for biodata analysis

• References

The following sections are included:

• Comparative genome annotation systems

• Phylogenetics methods

• Network inference from interaction and coexpression data

• Bayesian inference, association rule mining and Petri nets

• References

The following sections are included:

• Network analysis methods

• Unsupervised and supervised clustering

• Neural networks and evolutionary methods

• Semantic web and ontologization of biology

• Biological data fusion

• Rise of the GPU machines

• References

The following sections are included:

• Index