The emergence of pattern discovery techniques in computational biology

Metabolic Engineering

Volumn 2, Issue 3, 2000, Pages 159-177

  Rigoutsos, Isidore ^a   Floratos, Aris ^a   Parida, Laxmi ^a   Gao, Yuan ^a   Platt, Daniel ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0033671736     PISSN: 10967176     EISSN: None     Source Type: Journal    
DOI: 10.1006/mben.2000.0151     Document Type: Article

References (91)

1. Protein data bank archives of three-dimensional macromolecular structures
2. Basic local alignment search tool
3. Gap costs for multiple sequence alignment
4. Sensitivity comparison of protein amino acid sequences
5. The PRINTS protein fingerprint database in its fifth year
6. The value of prior knowledge in discovering motis with MEME
7. The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1998
8. The PROSITE database: Its status in 1995
9. Predicting gene regulatory elements in silico on a genomic scale
10. Data mining for regulatory elements in yeast genome
11. Clustering gene expression patterns
12. A method for fast database search for all k-nucleotide repeats
13. An algorithm for finding tandem repeats of unspecified pattern size
14. Automatic extraction of biological information from scientific text: Protein-protein interactions
15. Approaches to the automatic discovery of patterns in biosequences
16. A generalized profile syntax for biomolecular sequence comparison methods
17. Prediction of local structure in proteins using a library of sequence-structure motifs
18. SPLASH: Structural pattern localization analysis by sequential histograms
19. The multiple sequence alignment problem in biology
20. Constructing biological knowledge bases by extracting information from text sources
21. Atlas of protein sequence and structure
22. Sequence comparison by dynamic programming
23. Mining the gene expression matrix: Infering gene relationships form large scale gene expression data
24. Cluster analysis and display of genome-wide expression patterns
25. Progressive sequence alignment as prerequisite to correct phylogenetic trees
26. Sequence homology detection through large-scale pattern discovery
27. On the time complexity of the TEIRESIAS algorithm
28. Light-directed, spatially addressable parallel chemical synthesis
29. Motif detection in protein sequences
30. Detection of HTH motifs via data mining
31. Mutation data matrix and its uses
32. Profile analysis: Detection of distantly related proteins
33. Meta-MEME: Motif-based hidden Markov models of protein families
34. A first look-up algorithm for detecting repetitive DNA sequences
35. Three-dimensional structures and contexts associated with recurrent amino acid sequence patterns
36. Automatic assembly of protein blocks for database searching
37. Protein family classification based on searching a database of Blocks
38. Amino acid substitution matrices form protein blocks
39. Comprehensive study on iterative algorithms of multiple sequence alignment
40. Discovery of local packing motifs in protein structures
41. Finding flexible patterns in unaligned protein sequences
42. Rapid identification of repeated patterns in strings, trees and arrays
43. Hidden Markov model in computational biology: Applications to protein modeling
44. An algorithm for approximate tandem repeats
45. An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences
46. REVEAL, a general reverse engineering algorithm for inference of genetic network architectures
47. The complexity of some problems on subsequences and supersequences
48. A flexible multiple sequence alignement program
49. Cluster analysis and data visualization of large-scale gene expression data
50. Local multiple alignment via subgraph enumeration
51. Discovering simple DNA sequences by the algorithmic significance method
52. A general method applicable to the search for similarities in the amino acid sequence of two proteins
53. Detecting patterns in protein sequences
54. Enumerating and ranking discrete motifs
55. An approximation algorithm for alignment of multiple sequences using motif discovery
56. MUSCA: An algorithm for constrained aligment of multiple data sequences
57. Improved tools for biological sequence comparison
58. Motif discovery without alignment or enumeration
59. Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm
60. Dictionary building via unsupervised hierarchical motif discovery in the sequence space of natural proteins
61. Building dictionaries of 1D and 3D motifs by mining the unaligned 1D sequences of 17 archaeal and bacterial genomes,
62. A search for common patterns in many sequences
63. Multiple sequence comparison: A peptide matching approach,
64. A double combinatorial approach to discovering patterns in biological sequences
65. Identification of sequence motifs from a set of proteins with related function
66. Quantitative monitoring of gene expression patterns with a complementary DNA microarray
67. Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins
68. Pattern-induced multi-sequence alignment (PIMA) algorithm employing secondary structure-dependent gap renalties for use in comparative protein modelling
69. Comparative biosequence metrics
70. Identification of common molecular subsequences
71. Finding sequence motifs in groups of functionally related proteins
72. Automatic generation of primary sequence patterns from sets of related protein sequences
73. A multiple sequence alignment program
74. Pfam: A comprehensive database of protein domain families based on seed alignments
75. Tandem repeat detection using pattern discovery with application to the identification of yeast satellites
76. Searching for common sequence patterns among distantly related proteins
77. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
78. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice
79. Discovering active motifs in sets of related protein sequences and using them for classification
80. Combinatorial pattern discovery for scientific data: Some preleminary results
81. On the complexity of multiple sequence alignment
82. Pattern recognition in several sequences: Consensus and alignment
83. Large scale temporal gene expression mapping of CNS development
84. Identification of protein motifs using conserved amino acid properties and partitioning techniques