Advanced bioinformatics tools and strategies

Principles and Practices of Plant Genomics

Volumn 3, Issue , 2016, Pages 447-493

  Hibbs, Matthew A ^a  

^a JACKSON LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84874518186     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (90)

1. Alter O, Brown PO, Botstein D (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA 97: 10101-10106
2. Arnau V, Marin I (2003) A hierarchical clustering strategy and its application to proteomic interaction data. Proc 1st Conf Pattern Recog Img Anal, pp 62-69
3. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene ontology:Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25-29
4. Bar-Joseph Z, Gifford DK, Jaakkola TS (2001) Fast optimal leaf ordering for hierarchical clustering. Bioinformatics 17 (Suppl 1): S22-S29
5. Beal MJ, Falciani F, Ghahramani Z, Rangel C, Wild DL (2005) A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21: 349-356
6. Ben-Dor A, Shamir R, Yakhini Z (1999) Clustering gene expression patterns. J Comput Biol 6: 281-297
7. Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29: 1165-1188
8. Bergmann S, Ihmels J, Barkai N (2003) Iterative signature algorithm for the analysis of large-scale gene expression data. Phys Rev E Stat Nonlin Soft Matter Phys 67: 031902
9. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, et al. (2004) GO::TermFinder - open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 20: 3710-3715
10. Bradford JR, Needham CJ, Bulpitt AJ, Westhead DR (2006) Insights into proteinprotein interfaces using a Bayesian network prediction method. J Mol Biol 362: 365-386
11. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and Regression Trees. Tech Rep, Wadsworth Intl, Monterey, CA, USA
12. Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW et al. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA 97: 262-267
13. Cawley GC (2000) Matlab Support Vector Machine Toolbox: http://theoval. sys.uea.ac.uk/svm/toolbox/
14. Chan ALF, Chen J-X, Wang H-Y (2006) Application of data mining to predict the dosage of vancomycin as an outcome variable in a teaching hospital population. Int J Clin Pharmacol Ther 44: 533-538
15. Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to Algorithms. MIT Press, Cambridge, MA, USA
16. Datta S, Datta S (2003) Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19: 459-466
17. Davis J, Goadrich M (2006) The relationship between Precision-Recall and ROC curves. Proc 23rd Int Conf on Mach Learn, pp 233-240
18. Degroeve S, Baets BD, de Peer YV, Rouzé P (2002) Feature subset selection for splice site prediction. Bioinformatics 18 (Suppl 2): S75-S83
19. Dettling M, Bühlmann P (2003) Boosting for tumor classification with gene expression data. Bioinformatics 19: 1061-1069
20. Ding CH, Dubchak I (2001) Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17: 349-358
21. Edelman E, Porrello A, Guinney J, Balakumaran B, Bild A, et al. (2006) Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles. Bioinformatics 22: e108-e116
22. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95: 14863-14868
23. Freund Y, Schapire R (1999) A Short Introduction to Boosting. Jap Soc Artif Intel 14: 771-780
24. Garber ME, Troyanskaya OG, Schluens K, Petersen S, Thaesler Z, et al. (2001) Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci USA 98: 13784-13789
25. Gasch AP, Eisen MB (2002) Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol 3(11):RESEARCH0059
26. Golub G, Kahan W (1965) Calculating the Singular Values and Pseudo-Inverse of a Matrix. J Soc Indust Appl Math 2: 205-224
27. Guan Y, Myers CL, Lu R, Lemischka IR, Bult CJ, Troyanskaya OG (2008) A genomewide functional network for the laboratory mouse. PLoS Comput Biol 4: e1000165
28. Helmbold D, Schapire R (1997) Predicting nearly as well as the best pruning of a decision tree. Mach Learn 27: 61-68
29. Heyer LJ, Kruglyak S, Yooseph S (1999) Exploring expression data: Identification and analysis of coexpressed genes. Genome Res 9: 1106-1115
30. Hibbs MA, Dirksen NC, Li K, Troyanskaya OG (2005) Visualization methods for statistical analysis of microarray clusters. BMC Bioinformat 6: 115
31. Hibbs MA, Hess DC, Myers CL, Huttenhower C, Li K, Troyanskaya OG (2007) Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 23: 2692-2699
32. Hibbs MA, Myers CL, Huttenhower C, Hess DC, Li Kai, et al. (2009) Directing experimental biology: a case study in mitochondrial biogenesis. PLoS Comput Biol 5(3): e1000322
33. Huttenhower C, Schroeder M, Chikina MD, Troyanskaya OG (2008) The Sleipnir library for computational functional genomics. Bioinformatics 24: 1559-1561
34. Jaakkola T, Diekhans M, Haussler D (1999) Using the Fisher kernel method to detect remote protein homologies. Proc Int Conf Intell Syst Mol Biol, pp 149-158
35. Jansen R, Gerstein M (2004) Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Curr Opin Microbiol 7: 535-545
36. Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, et al. (2003) A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302: 449-453
37. Jardine N, Sibson R (1968) The construction of hierarchic and non-hierarchic classifications. Comput J 11: 177-184
38. Joachims T (1999) Making large-scale SVM learning practical. In: Scholkopf B, Burges C, Smola A (eds) Advances in Kernel Methods - Support Vector Learning. MIT Press, Boston, MA, USA
39. Kanehisa M (2002) The KEGG database. Novartis Found Symp 247: 91-101; discussion 101-3, 119-28, 244-52
40. Kloster M, Tang C, Wingreen NS (2005) Finding regulatory modules through large-scale gene-expression data analysis. Bioinformatics 21: 1172-1179
41. Kohavi R (1995) A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Proc Int Joint Conf Artif Intell, pp 1137-1145
42. Kundaje A, Middendorf M, Shah M, Wiggins CH, Freund Y, Leslie C (2006) A classification-based framework for predicting and analyzing gene regulatory response. BMC Bioinformat 7 (Suppl 1): S5
43. Lanckriet GR, Deng M, Cristianini N, Jordan MI, Noble WS (2004) Kernel-based data fusion and its application to protein function prediction in yeast. Pac Symp Biocomput, pp 300-311
44. Lee I, Li Z, Marcotte EM (2007) An improved, bias-reduced probabilistic functional gene network of baker's yeast, Saccharomyces cerevisiae. PLoS ONE 2(10): e988
45. Leek J, Storey J (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3(9): e161
46. Liao L, Noble WS (2003) Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. J Comput Biol 10: 857-868
47. MacKay DJC (2003) Information Theory, Inference, and Learning Algorithms. Cambridge Univ Press, Cambridge, UK
48. Madeira SC, Oliveira AL (2004) Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Trans Comput Biol Bioinform 1: 24-45
49. Mansour Y (1997) Pessimistic decision tree pruning based on tree size. Proc 14th Intl Workshop on Mach Learn, pp 195-201
50. Mendez MA, Hödar C, Vulpe C, González M, Cambiazo V (2002) Discriminant analysis to evaluate clustering of gene expression data. FEBS Lett 522: 24-28
51. Middendorf M, Kundaje A, Wiggins C, Freund Y, Leslie C (2004) Predicting genetic regulatory response using classification. Bioinformatics 20 (Suppl 1): i232-i240
52. Model F, Adorján P, Olek A, Piepenbrock C (2001) Feature selection for DNA methylation based cancer classification. Bioinformatics 17 (Suppl 1): S157-S164
53. Moler EJ, Chow ML, Mian IS (2000) Analysis of molecular profile data using generative and discriminative methods. Physiol Genom 4: 109-126
54. Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q (2008) GeneMANIA:a real-time multiple association network integration algorithm for predicting gene function. Genome Biol 9 (Suppl 1): S4
55. Murphy K (2001) The Bayes Net Toolbox for Matlab. Comput Sci Stat 33: 200-201
56. Myers CL, Robson D, Wible A, Hibbs MA, Chiriac C, et al. (2005) Discovery of biological networks from diverse functional genomic data. Genome Biol 6:R114
57. Myers CL, Barrett DR, Hibbs MA, Huttenhower C, Troyanskaya OG (2006) Finding function: Evaluation methods for functional genomic data. BMC Genom 7: 187
58. Neal RM (1993) Probabilistic Inference using Markov Chain Monte Carlo Methods. Tech Rep CRG-TR-93-1, Univ of Toronto, Canada
59. Noble WS (2004) Support vector machine applications in computational biology. In: Schoelkopf B, Tsuda K, Vert JP (eds) Kernel Methods in Computational Biology. MIT Press, Boston, MA, USA, pp 71-92
60. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24: 1565-1567
61. Owen AB, Stuart J, Mach K, Villeneuve AM, Kim S (2003) A gene recommender algorithm to identify coexpressed genes in C. elegans. Genome Res 13: 1828-1837
62. Povalej P, Lenic M, Zorman M, Kokol P, Dinevski D (2005) Accuracy of intelligent medical systems. Comput Meth Prog Biomed 80 (Suppl 1): S95-S105
63. Raychaudhuri S, Stuart JM, Altman RB (2000) Principal components analysis to summarize microarray experiments: Application to sporulation time series. Pac Symp Biocomput, pp 455-466
64. Rodgers J, Nicewander W (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42: 59-66
65. Rosenkrantz DJ, Stearns RE, Lewis PM, II (1977) An analysis of several heuristics for the Traveling Salesman Problem. SIAM J Comput 6: 563-581
66. Russell N (2002) Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, USA
67. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP (2005) Causal proteinsignaling networks derived from multiparameter single-cell data. Science 308: 523-529
68. Saeed AI, Sharov V, White J, Li J, Liang W et al. (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34: 374-378
69. Saldanha AJ, Brauer MJ, Botstein D (2004) Nutritional homeostasis in batch and steady-state culture of yeast. Mol Biol Cell 15: 4089-4104
70. Schrijver A (2005) On the history of combinatorial optimization (till 1960). In:Aardal K, Nemhauser GL, Weismantel R (eds) Handbook of Discrete Optimization. Elsevier, Amsterdam, Netherlands, pp 1-68
71. Sealfon RS, Hibbs MA, Huttenhower C, Myers CL, Troyanskaya OG (2006) GOLEM: An interactive graph-based gene-ontology navigation and analysis tool. BMC Bioinformat 7: 443
72. Segal NH, Pavlidis P, Antonescu CR, Maki RG, Noble WS, et al. (2003) Classification and subtype prediction of adult soft tissue sarcoma by functional genomics. Am J Pathol 163: 691-700
73. Seo J, Shneiderman B (2002) Interactively exploring hierarchical clustering results [geneidentification]. Computer 35: 80-86
74. Sharan R, Maron-Katz A, Shamir R (2003) CLICK and EXPANDER: A system for clustering and visualizing gene expression data. Bioinformatics 19: 1787-1799
75. Sherlock G (2000) Analysis of large-scale gene expression data. Curr Opin Immunol 12: 201-205
76. Sokal RR, Michener CD (1958) A statistical method for evaluating systematic relationships. Univ Kansas Sci Bull 28: 1409-1438
77. Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15: 72-101
78. St Onge RP, Mani R, Oh J, Proctor M, Fung E et al. (2007) Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions. Nat Genet 39: 199-206
79. Sturn A, Quackenbush J, Trajanoski Z (2002) Genesis: Cluster analysis of microarray data. Bioinformatics 18: 207-208
80. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102: 15545-15550
81. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, et al. (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96: 2907-2912
82. Troyanskaya OG, Dolinski K, Owen AB, Altman RB, Botstein D (2003) A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc Natl Acad Sci USA 100: 8348-8353
83. Vapnik VN (1995) The Nature of Statistical Learning Theory. Springer, New York, NY, USA
84. Varshavsky R, Horn D, Linial M (2008) Global considerations in hierarchical clustering reveal meaningful patterns in data. PLoS ONE 3: e2247
85. Vert JP (2002) Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings. Pac Sym Biocomput: 649-660
86. Wall E, Rechtsteiner, Rocha M (2003) Singular value decomposition and principal component analysis. In: Berrar P, Dubitzky, Granzow (eds) A Practical Approach to Microarray Data Analysis. Kluwer Academic Publ, Boston, MA, USA, pp 91-109
87. Waxman HS, Worley WE (1990) Computer-assisted adult medical diagnosis:subject review and evaluation of a new microcomputer-based system. Medicine (Baltimore) 69: 125-136
88. Wilson D, Martinez T (1997) Improved Heterogeneous Distance Functions. J Artif Intel Res 6: 1-34
89. Yeung KY, Haynor DR, Ruzzo WL (2001) Validating clustering for gene expression data. Bioinformatics 17: 309-318
90. Zien A, Rätsch G, Mika S, Schölkopf B, Lengauer T, Müller KR (2000) Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 16: 799-807