Introduction to data mining for the life sciences

Introduction to Data Mining for the Life Sciences

Volumn 9781597452908, Issue , 2012, Pages 1-635

  Sullivan, Rob ^a  

^a NONE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION;

DATA ASSETS; HIDDEN PATTERNS; KERNEL LEARNING METHODS; LIFE-SCIENCES;

DATA MINING;

EID: 84930035933     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-59745-290-8     Document Type: Book

References (439)

1. Bronshtein IN (2003) Handbook of mathematics. Springer, Berlin/New York
2. Fayyad UM (1996) Advances in knowledge discovery and data mining. AAAI Press/MIT Press, Menlo Park
3. Heckerman D (1999) Learning Bayes nets from data. IJCAI
4. Kling J (1998) From hypertention to angina to Viagra. Mod Drug Discov 1:31, 33-34, 36, 38
5. Lyman P, Varian HR (2003) How much information. Universirty of California, Berkeley, p 100
6. Piantadosi S (1997) Clinical trials: a methodologic perspective. Wiley, New York
7. Refaat M (2007) Data preparation for data mining using SAS. Morgan Kaufmann Publishers, San Francisco
8. Schena M (2002) Microarray analysis. Wiley, Hoboken
9. Tramontano A (2005) The ten most wanted solutions in protein bioinformatics, Chapman & Hall/CRC mathematical biology and medicine series. Chapman & Hall/CRC, Boca Raton
10. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufman, Amsterdam, Boston
11. Bellman RE (1961) Adaptive control processes: a guided tour. Princeton University Press, Princeton
12. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York
13. Gallier JH (1986) Logic for computer science: foundations of automatic theorem proving, Harper & Row computer science and technology series. Harper & Row, New York
14. Graner F, Glazier JA (1992) Simulation of biological cell sorting using a two-dimensional extended Potts model. Phys Rev Lett 69:2014-2016
15. Han J, Kamber M (2006) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco
16. Kohavi R (2000) Data mining and visualization. US Frontiers of Engineering
17. Puuronen S, Tsymbal A (2001) Local feature selection with dynamic integration of classifiers. Fundam Inform 47:91-117
18. Rabin MO (1980) Probabilistic algorithm for testing primality. J Number Theory 12:128-138
19. Ribeiro MN et al (2010) Local feature selection for generation of ensembles in text clustering. Neural Networks (SBRN), 2010 Eleventh Brazilian Symposium on. pp 67-72
20. Tietz NW (1995) Clinical guide to laboratory tests. W. B. Saunders Co., Philadelphia, pp xxxix, 1096
21. Tufte ER (1990) Envisioning information. Graphics Press, Cheshire
22. Tufte ER (1997) Visual explanations: images and quantities, evidence and narrative. Graphics Press, Cheshire
23. Tufte ER (2001) The visual display of quantitative information. Graphics Press, Cheshire
24. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufman, Amsterdam/Boston
25. Yijun S (2010) Local-learning-based feature selection for high-dimensional data analysis. IEEE Trans Pattern Anal Mach Intell 32:1610-1626
26. Acharya T, Ray AK (2005) Image processing: principles and applications. Wiley, Hoboken
27. Burger W, Burge MJ (2008) Digital image processing: an algorithmic introduction using Java. Springer, New York
28. Burget R et al (2010) RapidMiner image processing extension: a platform for collaborative research. International conference on telecommunications and signal processing, Baden, Austria
29. Chen PPS (1976) The entity-relationship model: toward a unified view of data. M. I. T. Alfred P. Sloan School of Management, Cambridge
30. Codd EF (1970) A relational model of data for large shared data banks. Commun ACM 13:377-387
31. Dai H-J et al (2010) New challenges for biological text-mining in the next decade. J Comput Sci Technol 25:169-179
32. Date CJ (2004) An introduction to database systems. Pearson/Addison Wesley, Boston
33. Ferreira De Oliveira MC, Levkowitz H (2003) From visual data exploration to visual data mining: a survey. IEEE Trans Vis Comput Graph 9:378-394
34. Kamberova GL, Shah SK (2002) DNA array image analysis: nuts & bolts. DNA Press, Eagleville
35. Leach AR, Gillet VJ (2003) An introduction to chemoinformatics. Kluwer Academic Publishers, Dordrecht/Boston
36. Martone ME (2007) Multiscale integration of brain data: new insights into glial architecture in health and disease. In: Gulliver multiscale bioimaging workshop, Berkeley, USA
37. Nigam K et al (2000) Text classification from labeled and unlabeled documents using EM. Mach Learn 39:103-134
38. Oda K et al (2008) New challenges for text mining: mapping between text and manually curated pathways. BMC Bioinformatics 9:S5
39. Riloff E, Jones R (1999) Learning dictionaries for information extraction by multi-level bootstrapping. In: Proceedings of the sixteenth national conference on artificial intelligence and the eleventh innovative applications of artificial intelligence conference innovative applications of artificial intelligence. American Association for Artificial Intelligence, Orlando, Florida, United States, pp 474-479
40. Swanson DR, Smalheiser NR (1997) An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artif Intell 91:183-203
41. Tramontano A (2005) The ten most wanted solutions in protein bioinformatics, Chapman & Hall/CRC mathematical biology and medicine series. Chapman & Hall/CRC, Boca Raton
42. Wu T et al (2007) DataScope: viewing database contents in Google Maps' way. In: Proceedings of the 33rd international conference on very large data bases. VLDB Endowment, Vienna, Austria, pp 1314-1317
43. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Bocca JB, Jarke M, Zaniolo C (eds) Proceedings of the 20th international conference on very large data bases, {VLDB}. Morgan Kaufmann, San Francisco, pp 487-499
44. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, Washington DC, pp 207-216
45. Cohen W, Ravikumar P, Fienberg S (2003) A comparison of string distance metrics for namematching tasks
46. Chiaretti S et al (2004) Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood 103:2771-2778
47. Gentleman RC et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80
48. Gibson R, Smith DR (2003) Genome visualization made fast and simple. Bioinformatics 19:1449-1450
49. Hagel J, Facchini P (2010) Biochemistry and occurrence of O-demethylation in plant metabolism. Front Physiol 1:14
50. Hall BG (2001) Phylogenetic trees made easy: a how- to manual for molecular biologists. Sinauer, Sunderland, Mass
51. Han X (2006) Inferring species phylogenies: a microarray approach. In: Computational intelligence and bioinformatics: international conference on intelligent computing, ICIC 2006, Kunming, China. Springer, Berlin/Heidelberg, pp 485-493
52. Kerkhoven R et al (2004) Visualization for genomics: the Microbial Genome Viewer. Bioinformatics 20:1812-1814
53. Krzywinski MI et al (2009) Circos: an information aesthetic for comparative genomics. Genome Res 19:1639-1645
54. Ledford H (2010) Big science: the cancer genome challenge. Nature 464:972-974
55. Milne I et al (2010) Flapjack - graphical genotype visualization. Bioinformatics 26:3133-3134
56. Morris JA et al (2010) Evoker: a visualization tool for genotype intensity data. Bioinformatics 26:1786-1787
57. Neapolitan RE (2003) Learning Bayesian networks. Prentice Hall, Harlow
58. Novere NL et al (2009) The systems biology graphical notation. Nat Biotechnol 27:735-741
59. Rajaram S, Oono Y (2010) NeatMap - non-clustering heat map alternatives in R. BMC Bioinformatics 11:45
60. Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273:1516-1517
61. Rual J-F et al (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature 437:1173-1178
62. Sato N, Ehira S (2003) GenoMap, a circular genome data viewer. Bioinformatics 19:1583-1584
63. Stothard P, Wishart DS (2005) Circular genome visualization and exploration using CGView. Bioinformatics 21:537-539
64. Tufte ER (1990) Envisioning information. Graphics Press, Cheshire
65. Tufte ER (1997) Visual explanations: images and quantities, evidence and narrative. Graphics Press, Cheshire
66. Tufte ER (2001) The visual display of quantitative information. Graphics Press, Cheshire
67. Tufte ER (2003) The cognitive style of PowerPoint. Graphics Press, Cheshire
68. Tufte ER (2006) Beautiful evidence. Graphics Press, Cheshire
69. Witten IH, Frank E (2005) Emboss European molecular biology open software suite. In: Data mining - practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco
70. Zacharias V (2008) Development and Verification of Rule-Based Systems - A Survey of Developers, http://www.cs.manchester.ac.uk/ruleML/presentations/session1paper1.pdf
71. Chen LYY et al (2002) Single nucleotide polymorphism mapping using genome wide unique sequences. J Genome Res
72. Du Bois D, Du Bois E (1916) A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med 17:863-871
73. Horton NJ, Lipsitz SR (2001) Multiple imputation in practice: comparison of software packages for regression models with missing variables. Am Stat 55:244-254
74. Lippert R, Mobarry C, Walenz B (2005) A space-efficient construction of the Burrows-Wheeler transform for genomic data. J Comput Biol 12:943-951
75. Lyman P, Varian HR (2003) How much information. University of California, Berkeley, pp. 100
76. Pearson RK (2006) The problem of disguised missing data. SIGKDD Explorations Newsl 8:83-92
77. Refaat M (2007) Data preparation for data mining using SAS. Morgan Kaufmann Publishers, San Francisco
78. Tietz NW (1995) Clinical guide to laboratory tests. W. B. Saunders Co, Philadelphia, pp. xxxix, 1096
79. Witten I, Frank E (2005) Emboss European Molecular Biology Open Software Suite. In: Data mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufman, Amsterdam/Boston
80. Bolstad WM (2004) Introduction to Bayesian statistics. Wiley-Interscience, Hoboken
81. Chagoyen M, Pazos F (2011) MBRole: enrichment analysis of metabolomic data. Bioinformatics 27:730-731
82. Crawley MJ (2005) Statistics: an introduction using R. Wiley, Chichester
83. Del Greco F, Burgess JL (1973) Hypertension in terminal renal failure: observations pre and post bilateral nephrectomy. J Chronic Dis 26:471-501
84. Gelman A (1995) Bayesian data analysis. Chapman & Hall, London/New York
85. Gentleman RC et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80
86. Glantz SA (2005) Primer of biostatistics. McGraw-Hill Medical Pub. Division, New York
87. Golub TR et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531-537
88. Jabbi M et al (2009) Response to "Voodoo Correlations in Social Neuroscience" by Vul et al. - summary information for the press (search online)
89. Killeen PR (2005) An alternative to null-hypothesis significance tests. Psychol Sci 16:345-353
90. Lindquist MA, Gelman A (2009) Correlations and multiple comparisons in functional imaging: a statistical perspective (Commentary on Vul et al. 2009). Perspect Psychol Sci 4:310-313
91. Margolin BH, Shelby MD (1985) Sister chromatid exchanges: a reexamination of the evidence for sex and race differences in humans. Environ Mutagen 7:63-72
92. Scanfeld D et al (2006) Motif discovery: algorithm and application (search online)
93. Subramanian A 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
94. Vul E et al (2009) Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspect Psychol Sci 4:274-290
95. Yang W et al (2011) Variable set enrichment analysis in genome-wide association studies. Eur J Hum Genet 19(8):893-900
96. Besemer J, Lomsadze A, Borodovsky M (2001) GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res 29:2607-2618
97. Bishop CM (2006) Pattern recognition and machine learning. Information science and statistics. Springer, New York
98. Bolstad WM (2004) Introduction to Bayesian statistics. Wiley-Interscience, Hoboken
99. Borodovsky M, McIninch J (1993) GeneMark: parallel gene recognition for both DNA strands. Comput Chem 17:123-133
100. Chartrand G (1985) Introductory graph theory. Dover, New York
101. Chou K-C (2001) Prediction of protein cellular attributes using pseudo amino acid composition. Proteins 43:246-255
102. Culotta A, Kulp D, McCallum A (2005) Gene prediction with conditional random fields. University of Massachusetts, Amherst
103. De Ferrari L, Aitken S (2006) Mining housekeeping genes with a Naive Bayes classifier. BMC Genomics 7:277
104. De Caprio D et al (2007) Conrad: gene prediction using conditional random fields. Genome Res 17:1389-1398
105. Díaz-Uriarte R, Rueda OM (2007) ADaCGH: a parallelized web-based application and R package for the analysis of aCGH data. PLoS One 2:e737
106. Eddy SR (2004) What is a hidden Markov model? Nat Biotechnol 22:1315-1316
107. Fonzo VD, Aluffi-Pentini F, Parisi V (2007) Hidden Markov models in bioinformatics. Curr Bioinforma 2:49-61
108. Gupta AK, Nadarajah S (2004) Handbook of beta distribution and its applications. Marcel Dekker, New York
109. Han X (2006) Inferring species phylogenies: a microarray approach. Computational intelligence and bioinformatics: international conference on intelligent computing, ICIC 2006, Kunming, China. Springer, New York, pp 485-493
110. Hofmann WA et al (2009) Analysis of array-CGH data using the R and Bioconductor software suite. Comp Funct Genomics 2009:8
111. Ji Y et al (2005) Applications of beta-mixture models in bioinformatics. Bioinformatics 21:2118-2122
112. Kadane J (1990) Comment: codifying chance. Statistical Science 5(1):18-20
113. Lafferty JD, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. Proceedings of the eighteenth international conference on machine learning. Morgan Kaufmann Publishers Inc., San Francisco, pp 282-289
114. Lee Y et al (2009) Bayesian classifier for predicting malignant renal cysts on MDCT: early clinical experience. Am J Roentgenol 193:W106-W111
115. Liu Y, Carbonell J, Weigele P, Gopalakrishnan V (2005) Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition. In S. Miyano, J. Mesirov, S. Kasif, S. Istrail, P. Pevzner & M. Waterman (Eds.), Research in Computational Molecular Biology 3500:408-422: Springer Berlin/Heidelberg
116. Marcus DA, Mathematical Association of America (2008) Graph theory: a problem oriented approach. Mathematical Association of America, Washington, DC
117. Moore AW (2004) Naive Bayes Classifiers, from http://www.autonlab.org/tutorials/naive02.pdf. Accessed on July 26, 2009
118. Mosteller F, Youtz C (1990) Quantifying probabilistic expressions. Statistical Science 5(1):2-12
119. Myllykangas S et al (2008) Integrated gene copy number and expression microarray analysis of gastric cancer highlights potential target genes. Int J Cancer 123:817-825
120. Nakashima H, Nishikawa K (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol 238:54-61
121. Neapolitan RE (2003) Learning Bayesian networks. Prentice Hall, Harlow
122. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77:257-286
123. Roy S et al (2009) Exploiting amino acid composition for predicting protein-protein interactions. PLoS One 4:e7813
124. Scheinin I et al (2010) CGHpower: exploring sample size calculations for chromosomal copy number experiments. BMC Bioinforma 11:331
125. Shinawi M, Cheung SW (2008) The array CGH and its clinical applications. Drug Discov Today 13:760-770
126. Sivia DS (1996) Data analysis: a Bayesian tutorial. Oxford Science Publications/Clarendon Press/Oxford University Press, Oxford/New York
127. Wallach HM (2004) Conditional random fields: an introduction. Department of Computer and Information Science, University of Pennsylvania, Philadelphia
128. Wallach J (2007) Interpretation of diagnostic tests. Lippincott Williams & Wilkins, Philadelphia
129. Wang L, Sauer UH (2008) OnD-CRF: predicting order and disorder in proteins conditional random fields. Bioinformatics 24:1401-1402
130. Wilson RJ (1996) Introduction to graph theory. Longman, Harlow
131. Woodworth GG (2005) Biostatistics: a Bayesian introduction. Wiley-Interscience, Hoboken
132. Wu L-Y et al (2009) Conditional random pattern algorithm for LOH inference and segmentation. Bioinformatics 25:61-67
133. Alon U (2007a) An introduction to systems biology: design principles of biological circuits, Chapman & Hall/CRC mathematical and computational biology series. Chapman & Hall/CRC, Boca Raton
134. Alon U (2007b) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450-461
135. Alon N et al (2008) Biomolecular network motif counting and discovery by color coding. Bioinformatics 24:i241-i249
136. Alpaydin E (2004) Introduction to machine learning, Adaptive computation and machine learning. MIT Press, Cambridge
137. Altschul SF et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389-3402
138. Anekritmongkol S, Kasamsan MLK (2010) Boolean algebra and compression technique for association rule mining. In: Proceedings of the 6th international conference on advanced data mining and applications - Volume Part II. Springer, Chongqing, pp 150-157
139. Attwood TK et al (1998) The PRINTS protein fingerprint database in its fifth year. Nucleic Acids Res 26:304-308
140. Babu MM et al (2004) Structure and evolution of transcriptional regulatory networks. Curr Opin Struct Biol 14:283-291
141. Bairoch A, Bucher P, Hofmann K (1996) The PROSITE database, its status in 1995. Nucleic Acids Res 24:189-196
142. Baldan P et al (2010) Petri nets for modelling metabolic pathways: a survey. Natural Computing 9:955-989
143. Baldi P et al (1994) Hidden Markov models of biological primary sequence information. Proc Natl Acad Sci 91:1059-1063
144. Baskerville K, Paczuski M (2006) Subgraph ensembles and motif discovery using a new heuristic for graph isomorphism. Phys Rev E 74:13
145. Basu S, Bilenko M et al (2004) A probabilistic framework for semi-supervised clustering. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004). Seattle, WA: 59-68
146. Belkin M, Niyogi P (2003) Using manifold structure for partially labeled classification. Neural Inform Process Syst 15(2002):929
147. Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399-2434
148. Bennett KP, Demiriz A (1998) Semi-supervised support vector machines. In: Kearns MJ, Solla SA, Cohn DA (eds) Advances in neural information processing systems 11. MIT Press, Denver, pp 368-374
149. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms, Advanced applications in pattern recognition. Plenum Press, New York
150. Bilmes J (1998) A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. International Computer Science Institute, Berkeley
151. Bishop CM (2006) Pattern recognition and machine learning, Information science and statistics. Springer, New York
152. Blekas K, Fotiadis DI, Likas A (2005) Motif-based protein sequence classification using neural networks. J Comput Biol 12:64-82
153. Blum A, Chawla S (2001) Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the eighteenth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 19-26
154. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory. ACM, Madison, pp 92-100
155. Bonet P et al (2007) PIPE v2.5: a Petri net tool for performance modelling. In:23rd Latin American Conference on Informatics (CLEI'07), San Jose, Costa Rica
156. Boole G (1854) An investigation of the laws of thought. Prometheus Books, New York
157. Bottou L, Bengio Y (1995) Convergence properties of the KMeans algorithm. Advances in Neural Information Processing Systems. Denver, MIT Press. 7
158. Boykov Y, Veksler O et al (1998) Markov random fields with efficient approximations. Computer vision and pattern recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on
159. Castelli V, Cover TM (1996) The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter. IEEE Trans Inform Theory 42:2102-2117
160. Chaouiya C (2007) Petri net modelling of biological networks. Brief Bioinform 8:210-219
161. Committee on Applications of Toxicogenomic Technologies to Predictive Toxicology and Risk Assessment, NRC (2007) Applications of toxicogenomic technologies to predictive toxicology and risk assessment. The National Academies Press, Washington, DC
162. Comtet L (1974) Advanced combinatorics: the art of finite and infinite expansions. Springer, Dordrecht
163. Conant GC, Wagner A (2003) Convergent evolution of gene circuits. Nat Genet 34:264-266
164. Cox E (2005) Fuzzy modeling and genetic algorithms for data mining and exploration, The Morgan Kaufmann series in data management systems. Elsevier/Morgan Kaufmann, Amsterdam/Boston
165. Craven M, Di Pasquo D et al (2000) Learning to construct knowledge bases from the World Wide Web. Artif. Intell. 118(1-2):69-113
166. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Cont Sig Syst (MCSS) 2:303-314
167. D'haeseleer P, Liang S, Somogyi R (1999) Gene expression data analysis and modeling (Tutorial). In: Pacific symposium on biocomputing, 1999(PSB99), Hawaii, US
168. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc Ser B 39:1-38
169. Ding C (2004) Tutorial on spectral clustering. International conferenceon machine learning, Alberta
170. Doi A (2011) Glycolytic pathway and lac operon of E. coli, from http://www.csml.org/models/csml-models/glycolytic-pathway-and-lac-operon-of-e-coli/. Accessed 7/17/2011
171. Doi A et al (2004) Constructing biological pathway models with hybrid functional Petri nets. In Silico Biol 4:271-291
172. Dong Q-W, Wang X-L, Lin L (2006) Application of latent semantic analysis to protein remote homology detection. Bioinformatics 22:285-290
173. Durbin R (1998) Biological sequence analysis: probabalistic models of proteins and nucleic acids. Cambridge, UK New York, Cambridge University Press
174. Eddy SR (1995) Multiple alignment using hidden Markov models. In: Intelligent systems in molecular biology. AAAI Press, Menlo Park, pp 114-120
175. Enderle JE (2007) Machine learning in the life sciences. IEEE Eng Med Biol 26:86-93
176. Filippone M et al (2008) A survey of kernel and spectral methods for clustering. Pattern Recogn 41:176-190
177. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Machine learning: proceedings of the thirteenth international conference. Morgan Kaufmann, San Francisco
178. Fukuda K et al (1998) Toward information extraction: identifying protein names from biological papers. Pac Symp Biocomput 98:707-718
179. Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2:183-192
180. Gentleman RC et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80
181. Greig DM, Porteous BT et al (1989) Exact maximum a posteriori estimation for binary images. J Roy Stat Soc Ser B Methodological 51(2):271-279
182. Gribskov M, Robinson NL (1996) The use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Comput Chem 20:25-33
183. Gribskov M, McLachlan AD, Eisenberg D (1987) Profile analysis: detection of distantly related proteins. Proc Natl Acad Sci 84:4355-4358
184. Grochow JA, Kellis M (2007) Network motif discovery using subgraph enumeration and symmetry-breaking. In: Proceedings of the 11th annual international conference on research in computational molecular biology. Springer, Oakland, pp 92-106
185. Gupta MR, Chen Y (2010) Theory and use of the EM algorithm. Found Trend Sig Process 4:223-296
186. Hahn U, Romacker M et al (2002) Creating knowledge repositories from biomedical reports: the MEDSYNDIKATE text mining system. Pac Symp Biocomput: 338-349
187. Hartman E, Keeler JD, Kowalski JM (1990) Layered neural networks with Gaussian hidden units as universal approximations. Neural Comput 2:210-215
188. Haussler D, Diekhans M, Jaakkola T (1998) A discriminative framework for detecting remote protein homologies. J Comput Biol 5:211-221
189. Hawkins J (2007) Why can't a computer be more like a brain? IEEE Spectr 44:21-26
190. Heiner M et al (2003) Model validation of biological pathways using Petri nets - demonstrated for apoptosis. In: Proceedings of the first international workshop on computational methods in systems biology. Springer, London, p 173
191. Hirschman L, Morgan AA et al (2002) Rutabaga by any other name: extracting biological names. J Biomed Informat 35(4):247-259
192. Hofmann T (1999) Probabilistic latent semantic analysis. In: Laskey K, Prade H (eds) Fifteenth conference annual conference on uncertainty in artificial intelligence (UAI-99). Morgan Kaufmann, Stockholm, pp 289-296
193. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359-366
194. Ihmels J et al (2005) Comparative gene expression analysis by a differential clustering approach: application to the Candida albicans transcription program. PLoS Genet 1:e39
195. Jianbo S, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888-905
196. Jiang D, Tang C, Zhang A (2004) Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng 16:1370-1386
197. Jing Z et al (2008) Parallelization of spectral clustering algorithm on multi-core processors and GPGPU. In: Computer systems architecture conference, 2008. ACSAC 2008. 13th Asia-Pacific, Busan, pp 1-8
198. Kalton A, Langley P et al (2001) Generalized clustering, supervised learning, and data assignment. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. San Francisco, California, ACM: 299-304
199. Kashani Z et al (2009) Kavosh: a new algorithm for finding network motifs. BMC Bioinforma 10:318
200. Kashtan N et al (2004) Topological generalizations of network motifs. Phys Rev E 70:031909
201. Kittleson MM et al (2005) Gene expression analysis of ischemic and nonischemic cardiomyopathy: shared and distinct genes in the development of heart failure. Physiol Genomics 21:299-307
202. Kleinberg J, Tardos E (2002) Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields. J ACM 49(5):616-639
203. Knottenbelt WJ, Dingle NJ, Suto T (2009) Performance trees: a query specification formalism for quantitative performance analysis. In: Parallel, distributed and grid computing for engineering. Saxe-Coburg Publications, Kippen, pp 165-198
204. Koch I, Heiner M (2004) Qualitative modelling and analysis of biochemical pathways with Petri Nets (Tutorial Notes). In:5th international conference on systems biology - ICSB 2004, Heidelberg, Germany
205. Kononenko I, Kukar M (2007) Machine learning and data mining: introduction to principles and algorithms. Horwood Publishing Limited, Chichester
206. Krogel MA, Scheffer T (2004) Multi-relational learning, text mining, and semi-supervised learning for functional genomics. Mach Learn 57:61-81
207. Krogel M-A, Wrobel S (2001) Transformation-based learning using multirelational aggregation. In: Rouveirol C, Sebag M (eds) Inductive logic programming. Springer, Berlin/Heidelberg, pp 142-155
208. Krogel MA et al (2003) Comparative evaluation of approaches to propositionalization. In: Inductive logic programming:13th international conference, ILP'2003. Springer, Szeged, pp 197-214
209. Krogh A et al (1982) Hidden Markov models in computational biology. Computer Research Laboratory, Santa Cruz
210. Leek TR (1997) Information extraction using hidden Markov models
211. Leslie CS, Eskin E, Noble WS (2002) The spectrum kernel: a string kernel for SVM protein classification. In: Pacific symposium on biocomputing, CSH Press, pp 566-575
212. Leslie CS et al (2004) Mismatch string kernels for discriminative protein classification. Bioinformatics 20:467-476
213. Lewis DP, Jebara T, Noble WS (2006) Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure. Bioinformatics 22:2753-2760
214. Liao L, Noble WS (2002) Combining pairwise sequence similarity and support vector machines for remote protein homology detection. In: Proceedings of the sixth annual international conference on computational biology. ACM, Washington, DC, pp 225-232
215. Ling Z (1996) An effective approach for solving subgraph isomorphism problem. IASTED International Conference
216. Liu Y, Yao X et al (2000) Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation 4:380-387
217. Maeda YT, Sano M (2006) Regulatory dynamics of synthetic gene networks with positive feedback. J Mol Biol 359:1107-1124
218. Mangan S, Alon U (2003) Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci 100:11980-11985
219. Markowetz F (2004) Classification by support vector machines. practical DNA microarray analysis (Bioconductor course). Online (Max Planck Institute)
220. Marroquin JL (1993) Deterministic interactive particle models for image processing and computer graphics. Graphical Models and Image Processing 55(5):408-417
221. Mehta JP (2010) Gene expression analysis in breast cancer. Dublin City University, Dublin
222. Meila M, Shi J (2001) A random walks view of spectral segmentation. AISTATS 2001:8-11
223. Middendorf M, Ziv E, Wiggins CH (2005) Inferring network mechanisms: the Drosophila melanogaster protein interaction network. Proc Natl Acad Sci USA 102:3192-3197
224. Milo R et al (2002) Network motifs: simple building blocks of complex networks. Science 298:824-827
225. Milo R et al (2004) Superfamilies of evolved and designed networks. Science 303:1538-1542
226. Minsky ML, Papert S (1969a) Perceptrons: an introduction to computational geometry. MIT Press, Cambridge/London
227. Minsky ML, Papert S (1969b) Perceptrons: an introduction to computational geometry. MIT Press, Cambridge/London
228. Mitchell TM (1997) Machine learning. McGraw-Hill, New York
229. Morik K et al (2000) Knowledge discovery and knowledge validation in intensive care. Artif Intell Med 19:225-249
230. Murata T (1989) Petri nets: properties, analysis and applications. Proc IEEE 77:541-580
231. Nepusz T, Sasidharan R, Paccanaro A (2010) SCPS: a fast implementation of a spectral method for detecting protein families on a genome-wide scale. BMC Bioinforma 11:120
232. Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: analysis and an algorithm. In: Neural Information Processing Systems. MIT Press, Cambridge, pp 849-856
233. Ng S-K, McLachlan GJ, Lee AH (2006) An incremental EM-based learning approach for on-line prediction of hospital resource utilization. Artif Intell Med 36:257-267
234. Nguyen HT, Walker E (2006) A first course in fuzzy logic. Chapman & Hall/CRC, Boca Raton
235. Nigam K et al (1998) Learning to classify text from labeled and unlabeled documents. In: Proceedings of the fifteenth national/tenth conference on artificial intelligence/innovative applications of artificial intelligence. American Association for Artificial Intelligence, Madison, pp 792-799
236. Omidi S, Schreiber F, Masoudi-Nejad A (2009) MODA: an efficient algorithm for network motif discovery in biological networks. Genes Genet Syst 84:385-395
237. Paccanaro A, Casbon JA, Saqi MAS (2006) Spectral clustering of protein sequences. Nucleic Acids Res 34:1571-1580
238. Patrikainen A, Meilą M (2005) Spectral clustering for Microsoft Netscan Data, Washington (state), USA, p 74
239. Pentney W, Meila M (2005) Spectral clustering of biological sequence data. National conference on artificial intelligence, Pittsburgh, pp 845-850
240. Petri CA (1962) Kommunikation mit Automaten. In: Institut für Instrumentelle Mathematik. Institut für Instrumentelle Mathematik, Bonn
241. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77:257-286
242. Ratsaby J, Venkatesh SS (1995) Learning from a mixture of labeled and unlabeled examples with parametric side information. In: Proceedings of the eighth annual conference on computational learning theory. ACM, Santa Cruz, pp 412-417
243. Reich M et al (2006) GenePattern 2.0. Nat Genet 38:500-501
244. Reutemann P, Pfahringer B, Frank E (2004) A toolbox for learning from relational data with propositional and multi-instance learners. In:17th Australian joint conference on artificial intelligence (AI2004). Springer, Berlin
245. Ribeiro P, Silva F, Kaiser M (2009) Strategies for network motifs discovery. In: Proceedings of the 2009 fifth IEEE international conference on e-science. IEEE Computer Society, Oxford, pp 80-87
246. Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge/New York
247. Rosenblatt F (1962) Principles of neurodynamics; perceptrons and the theory of brain mechanisms. Spartan, Washington, DC
248. Rosenfeld N, Elowitz MB, Alon U (2002) Negative autoregulation speeds the response times of transcription networks. J Mol Biol 323:785-793
249. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323-2326
250. Roy S, Cox IJ (1998) A maximum-flow formulation of the N-camera stereo correspondence problem. ICCV: 492-502
251. Ruczinski I, Kooperberg C, Le Blanc M (2003) Logic regression. J Comput Graph Stat 12:475-511
252. Rumelhart DE, McClelland JL, University of California San Diego, PDP Research Group (1986a) Parallel distributed processing: explorations in the microstructure of cognition, Computational models of cognition and perception. MIT Press, Cambridge
253. Rumelhart DE, McClelland JL, University of California San Diego, PDP Research Group (1986b) Parallel distributed processing: explorations in the microstructure of cognition, Computational models of cognition and perception. MIT Press, Cambridge
254. Saigo H et al (2002) Comparison of SVM-based methods for remote homology detection. Genome Inform 13:396-397
255. Sandve G, Drablos F (2006) A survey of motif discovery methods in an integrated framework. Biol Direct 1:11
256. Schneider G, Rohlk S, Wrede P (1993) Analysis of cleavage-site patterns in protein precursor sequences with a perceptron-type neural network. Biochem Biophys Res Commun 194:951-959
257. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond, Adaptive computation and machine learning. MIT Press, Cambridge
258. Schuurmans D (1997) A new metric-based approach to model selection. In: Fourteenth national conference on artificial intelligence (AAAI-97). MIT Press (for AAAI Press), Providence, pp 552-558
259. Sewell M (2008) Structural risk minimization. University College London, London, p 3
260. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge/New York
261. Slonim DK, Yanai I (2009) Getting started in gene expression microarray analysis. PLoS Comput Biol 5:e1000543
262. Snow D (2000) Exact voxel occupancy with graph cuts
263. Stormo GD et al (1982) Use of the 'Perceptron' algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Res 10:2997-3011
264. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction, Adaptive computation and machine learning. MIT Press, Cambridge
265. Vapnik VN (2000) The nature of statistical learning theory, Statistics for engineering and information science. Springer, New York
266. Vapnik VN, Chervonenkis AY (1974) Theory of pattern recognition (In Russian)
267. Vishwanathan SVN, Smola AJ (2003) Fast kernels for string and tree matching. In: Advances in neural information processing systems 15. MIT Press, Cambridge, pp 569-576
268. Von Luxburg U (2006) A tutorial on spectral clustering. Online (Max Planck Institute)
269. Voss K, Heiner M, Koch I (2003) Steady state analysis of metabolic pathways using Petri nets. In Silico Biol 3(31):367-387
270. Walker MG (2001) Drug target discovery by gene expression analysis cell cycle genes. Curr Cancer Drug Targ 1:73-83
271. Wang JTL et al (2000) Application of neural networks to biological data mining: a case study in protein sequence classification. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Boston, pp 305-309
272. Waterman MS, Joyce J, Eggert M (1991) Computer alignment of sequences. In: Phylogenetic analysis of DNA sequences. Oxford University Press, New York, pp 59-72
273. Weston J, Kuang R et al (2006) Protein ranking by semi-supervised network propagation. BMC Bioinformatics 7(Suppl 1):S10
274. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Amsterdam/Boston
275. Wong L (2006) Knowledge discovery techniques for bioinformatics, Part IV: Gene expression analysis. National University of Singapore, Singapore
276. Wong EA, Baur B (2010) On network tools for network motif finding: a survey study. Online
277. Yona G, Linial N, Linial M (1999) ProtoMap: automatic classification of protein sequences, a hierarchy of protein families, and local maps of the protein space. Protein Struct Funct Bioinform 37:360-378
278. Zaki NM, Deris S, Illias RM (2003) A comparative analysis of protein homology detection methods. J Theor 5:7
279. Zhang T, Oles FJ (2000) The value of unlabeled data for classification problems. In: Seventeenth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 1191-1198
280. Zhu X (2005) Semi-supervised learning literature survey. University of Wisconsin, Madison
281. Zhu X, Goldberg AB (2009) Introduction to semi-supervised learning. Synth Lect Artif Intell Mach Learn 3:1-130
282. Zimmermann P, Hennig L, Gruissem W (2005) Gene-expression analysis and network discovery using Genevestigator. Trends Plant Sci 10:407-409
283. Baladandayuthapani V et al (2008) Bayesian hierarchical spatially correlated functional data analysis with application to colon carcinogenesis. Biometrics 64:64-73
284. Berger JO (1993) Statistical decision theory and Bayesian analysis, Springer series in statistics. Springer, New York
285. Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press/Oxford University Press, Oxford/New York
286. Breiman L (1996) Bagging predictors. Mach Learn 24:123-140
287. Cheeseman P (1988) AutoClass: a Bayesian classification system. In: Fifth international conference on machine learning. Morgan Kauffman, Ann Arbor, pp 54-64
288. Cheeseman P (1990) On finding the most probable model. In: Shrager J, Langley P (eds) Computational models of scientific discovery and theory formation. Morgan Kauffman, Palo Alto, pp 73-96
289. Cheeseman P, Stutz J (1996) Bayesian classification (AutoClass): theory and results. In: Advances in knowledge discovery and data mining. American Association for Artificial Intelligence, Menlo Park, pp 153-180
290. Dalgaard P (2002) Introductory statistics with R. In: Statistics and computing. Springer, New York
291. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York
292. Efron B, Tibshirani R (1997) Improvements on cross-validation: the.632+ Bootstrap method. J Am Stat Assoc 92:548-560
293. Engelman JA et al (2007) MET amplification leads to Gefitinib resistance in lung cancer by activating ERBB3 signaling. Science 316:1039-1043
294. Fisher DH (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2:139-172
295. Fraley C, Raftery AE (1998) How many clusters? which clustering method? Answers via modelbased cluster analysis. Comput J 41:578-588
296. Geiger D, Paz A, Pearl J (1990) Learning causal trees from dependence information, AAAI 1990. MIT Press, Boston, pp 770-776
297. Han X (2006) Inferring species phylogenies: a microarray approach. In: Computational intelligence and bioinformatics: international conference on intelligent computing, ICIC 2006, Kunming, China. Springer, Berlin/Heidelberg, pp 485-493
298. Hanson R, Stutz J, Cheeseman P (1991) Bayesian classification theory. NASA Ames Research Center, Moffett Field
299. Haughton D, Legrand P, Woolford S (2009) Review of three latent class cluster analysis packages: Latent Gold, poLCA, and MCLUST. Am Stat 63:81-91
300. Hoffman R et al (2002) Prostate-specific antigen testing accuracy in community practice. BMC Fam Pract 3:19
301. Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11:63-91
302. Hori K, Furumoto S, Kubota K (2008) Tumor blood flow interruption after radiotherapy strongly inhibits tumor regrowth. Cancer Sci 99:1485-1491
303. Jain RK (1988) Determinants of tumor blood flow: a review. Cancer Res 48:2641-2658
304. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International joint conference on artificial intelligence 1995. Morgan Kaufmann, San Francisco, pp 1137-1143
305. Lu H, Setiono R, Liu H (1996) Effective data mining using neural networks. IEEE Trans Knowl Data Eng 8:957-961
306. MacKay DJC (1992) The evidence framework applied to classification networks. Neural Comput 4:720-736
307. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Cam LML, Neyman J (eds) Fifth Berkeley symposium on mathematical statistics and probability. University of California Press, Berkeley, pp 281-297
308. Medvedovic M, Sivaganesan S (2002) Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics 18:1194-1206
309. Medvedovic M, Yeung KY, Bumgarner RE (2004) Bayesian mixture model based clustering of replicated microarray data. Bioinformatics 20:1222-1232
310. Ng SK et al (2006) A Mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics 22:1745-1752
311. Oken MM et al (2005) Baseline chest radiograph for lung cancer detection in the randomized prostate, lung, colorectal and ovarian cancer screening trial. J Natl Cancer Inst 97:1832-1839
312. Picard RR, Cook RD (1984) Cross-validation of regression models. J Am Stat Assoc 79:575-583
313. Prentice AM, Jebb SA (2001) Beyond body mass index. Obes Rev 2:141-147
314. Qin ZS (2006) Clustering microarray gene expression data using weighted Chinese restaurant process. Bioinformatics 22:1988-1997
315. Sing T et al (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21:3940-3941
316. Tadesse MG, Sha N, Vannucci M (2005) Bayesian variable selection in clustering high-dimensional data. J Am Stat Assoc 100:602-617
317. Wang JT-L. et al (1994) Combinatorial pattern discovery for scientific data: some preliminary results. In: Proceedings of the 1994 ACM SIGMOD international conference on management of data. ACM, Minneapolis, pp 115-125
318. Wang JTL et al (2000) Application of neural networks to biological data mining: a case study in protein sequence classification. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Boston, pp 305-309
319. Wu CH, McLarty JW (2000) Neural networks and genome informatics, vol 1, Methods in computational biology and biochemistry. Elsevier, Amsterdam/New York
320. Xiang Z, Qin ZS, He Y (2007) CRCView: a web server for analyzing and visualizing microarray gene expression data using model-based clustering. Bioinformatics 23:1843-1845
321. Yeung KY et al (2001) Model-based clustering and data transformations for gene expression data. Bioinformatics 17:977-987
322. ∗ nicotinic acetylcholine receptor. J Enzyme Inhib Med Chem 24:157-168
323. Altschul SF, Carroll RJ, Lipman DJ (1989) Weights for data related by a tree. J Mol Biol 207:647-653
324. Azuaje F, Dopazo J (2005) Data analysis and visualization in genomics and proteomics. John Wiley, Chichester/Hoboken
325. Bair E, Tibshirani R (2003) Machine learning methods applied to DNA microarray data can improve the diagnosis of cancer. ACM SIGKDD Explor Newsl 5:48-55
326. Baldi P (2002) DNA microarrays and gene expression: from experiments to data analysis and modeling. Cambridge University Press, Cambridge
327. Bessant C, Shadforth I, Oakley D (2009) Building bioinformatics solutions: with Perl, R, and mySQL. Oxford University Press, Oxford
328. Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21:33-37
329. Chen D et al (2008) PhyloFinder: an intelligent search engine for phylogenetic tree databases. BMC Evol Biol 8:90
330. Durbin R (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge/New York
331. Feng D-F, Doolittle RF (1987) Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J Mol Evol 25:351-360
332. Fitch RM, American Chemical Society (1971) Polymer colloids: proceedings. Plenum Press, New York
333. Gentleman RC (2005) Bioinformatics and computational biology solutions using R and Bioconductor, Statistics for biology and health. Springer, New York
334. Gibas C, Jambeck P (2001) Developing bioinformatics computer skills. O'Reilly, Beijing/Cambridge
335. Gotoh O (1982) An improved algorithm for matching biological sequences. J Mol Biol 162:705-708
336. Han X (2006) Inferring species phylogenies: a microarray approach. In: Computational intelligence and bioinformatics: international conference on intelligent computing, ICIC 2006, Kunming, China. Springer, Berlin/Heidelberg, pp 485-493
337. Harpaz Y, Chothia C (1994) Many of the immunoglobulin superfamily domains in cell adhesion molecules and surface receptors belong to a new structural set which is close to that containing variable domains. J Mol Biol 238:528-539
338. Hughey R, Krogh A (1996) Hidden Markov models for sequence analysis: extension and analysis of the basic method. Comput Appl Biosci 12:95-107
339. Iacobas AD et al (2002) Improved procedures to mine data obtained from spotted cDNA arrays. J Biomol Tech 13:5-19
340. Jiawei L (2006) A novel method for constructing phylogenetic tree based on 3D graphical representation. International conference on computational intelligence for modelling, control and automation, 2006 and international conference on intelligent agents, web technologies and internet commerce, Sydney, NSW. In: Xizhen Z (ed). pp 244-244
341. Kamberova GL, Shah SK (2002) DNA array image analysis: nuts & bolts. DNA Press, Eagleville
342. Knudsen S (2002) A biologist's guide to analysis of DNA microarray data. Wiley-Interscience, New York
343. Leng X, Muller H-G (2006) Classification using functional data analysis for temporal gene expression data. Bioinformatics 22:68-76
344. Lockhart D, Winzeler E (2000) Genomics, gene expression and DNA arrays. Nature 405:827-836
345. Markel S, León D (2003) Sequence analysis in a nutshell: a guide to common tools and databases. O'Reilly, Sebastopol/Farnham
346. Moorhouse M, Barry P (2004) Bioinformatics, biocomputing and Perl: an introduction to bioinformatics computing skills and practice. Wiley, Chichester/Hoboken
347. Nadimpally V, Jaki MJ (2003) A novel approach to determine normal variation in gene expression data. ACM SIGKDD Explor Newsl 5:6-15
348. Needleman S, Wunsch C (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48:443-453
349. Roberts RJ (1980) Restriction and modification enzymes and their recognition sequences. Nucleic Acids Res 8:r63-r80
350. Sankoff D, Cedergren RJ (1983) Simultaneous comparison of three or more sequences related by a tree. In: Sankoff D, Kruskal JB (eds) Time warps, string edits, and macromolecules: the theory and practice of sequence comparison. Addison-Wesley, Reading, pp 253-263
351. Schena M (2002) Microarray analysis. Wiley, Hoboken
352. Schwartz R (2008) Biological modeling and simulation: a survey of practical models, algorithms, and numerical methods. MIT Press, Cambridge
353. Shan Y (2006) Genome-wide EST data mining approaches to resolving incongruence of molecular phylogenies. Quantitative Biology/Genomics, arXiv:13
354. Shasha D, Wang JTL, Zhang S (2004) Unordered tree mining with applications to phylogeny. In: Proceedings of the 20th international conference on data engineering. IEEE Computer Society, Boston, pp 708
355. Shipp MA et al (2002) Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8:68-74
356. Simon R (2003) Supervised analysis when the number of candidate features (p) greatly exceeds the number of cases (n). ACM SIGKDD Explor Newsl 5:31-37
357. Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol 24:1596-1599
358. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673-4680
359. Tisdall JD (2001) Beginning Perl for bioinformatics. O'Reilly, Beijing/Sebastopol
360. Tisdall JD (2003) Mastering Perl for bioinformatics. O'Reilly, Beijing/Farnham
361. Tramontano A (2005) The ten most wanted solutions in protein bioinformatics. Chapman & Hall/CRC, Boca Raton
362. Tu Y, Stolovitzky G, Klein U (2002) Quantitative noise analysis for gene expression microarray experiments. Proc Natl Acad Sci USA 99:14031-14036
363. Vrana KE, Freeman WM, Aschner M (2002) Use of microarray technologies in toxicology research. Neurotoxicity 24:321-332
364. Wall L, Christiansen T, Orwant J (2000) Programming Perl. O'Reilly, Beijing/Cambridge
365. Waterman MS, Perlwitz MD (1984) Line geometries for sequence comparisons. Bull Math Biol 46:567-577
366. Wilson D et al (2009) SUPERFAMILY - sophisticated comparative genomics, data mining, visualization and phylogeny. Nucleic Acids Res 37:D380-D386
367. Zaki MJ (2005) Efficiently mining frequent trees in a forest: algorithms and applications. IEEE Trans Knowl Data Eng 17:1021-1035
368. Zhang S, Wang JTL (2006) Mining frequent agreement subtrees in phylogenetic databases. In: Ghosh J et al (eds) Sixth SIAM international conference on data mining. SIAM, Bethesda, pp 222-233
369. Altman R et al (2008) Text mining for biology - the way forward: opinions from leading scientists. Genome Biol 9:S7
370. Ananiadou S, Kell DB, Tsujii J-I (2006) Text mining and its potential applications in systems biology. Trends Biotechnol 24:571-579
371. Antoniotti M, Ramakrishnan N, Mishra B (2005) GOALIE, a common lisp application to discover "Kripke Models": redescribing biological processes from time-course data. In: Proceedings of the international Lisp conference, Stanford University, Stanford, 19-22 June 2005
372. Balakrishnan R, Ranganathan K (2000) A textbook of graph theory, Universitext. Springer, New York
373. Bollobás BI (1998) Modern graph theory, Graduate texts in mathematics. Springer, New York
374. Brin S, Page L (1998) The anatomy of a large-scale hypertextual Web search engine. Comput Networks ISDN Syst 30:107-117
375. Chang JT, Schütze H, Altman RB (2004) GAPSCORE: finding gene and protein names one word at a time. Bioinformatics 20:216-225
376. Chaouiya C (2007) Petri net modelling of biological networks. Brief Bioinform 8:210-219
377. Cho CR et al (2006) The application of systems biology to drug discovery. Curr Opin Chem Biol 10:294-302
378. Cline MS et al (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2:2366-2382
379. Crochemore M et al (2010) Algorithms for three versions of the shortest common superstring problem. In:21st annual symposium on Combinatorial Pattern Matching (CPM2010). Springer, New York
380. Diestel R (2010) Graph theory. Springer, New York
381. Dougherty ER et al (1999) Nonlinear filters in genomic control. IEEE-EURASIP workshop on nonlinear signal and image processing, Antalya, pp 10-14
382. Faro A, Giordano D, Spampinato C (2011) Combining literature text mining with microarray data: advances for system biology modeling. Brief Bioinform
383. Franzen K et al (2002) Protein names and how to find them. Int J Med Inform 67:49-61
384. Friedman N et al (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7:601-620
385. Fukuda K et al (1998) Toward information extraction: identifying protein names from biological papers. In: Pacific symposium on Biocomputing'98, Hawaii, pp 707-718
386. Funahashi A et al (2003) Cell designer: a process diagram editor for gene-regulatory and biochemical networks. Biosilico 1:159-162
387. Furber JD (2010) Systems biology of human aging - network model wall chart
388. Gentleman RC et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80
389. Gupta A et al (2009) Data mining by navigation - an experience with systems biology. In: Chaudhury S et al (eds) Pattern recognition and machine intelligence. Springer, Berlin/Heidelberg, pp 158-164
390. Gusfield D (1997) Algorithms on strings, trees, and sequences: computer science and computational biology. Cambridge University Press, Cambridge/New York
391. Hasty J et al (2001) Computational studies of gene regulatory networks: in numero molecular biology. Nat Rev Genet 2:268-279
392. Hirschman L et al (2005) Overview of BioCreAtIvE: critical assessment of information extraction for biology. BMC Bioinformatics 6(Suppl 1):S1
393. Huang S (1999) Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery. J Mol Med 77:469-480
394. Hucka M et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524-531
395. Iragne F et al (2004) AliasServer: a web server to handle multiple aliases used to refer to proteins. Bioinformatics 20:2331-2332
396. Kauffman SA (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22:437-467
397. Kell DB (2006) Metabolomics, modelling and machine learning in systems biology - towards an understanding of the languages of cells. FEBS J 273:873-894
398. Kell DB (2007) The virtual human: towards a global systems biology of multiscale, distributed biochemical network models. IUBMB Life 59:689-695
399. Klipp E et al (2005) Systems biology in practice: concepts, implementation and application. Wiley-VCH, Weinheim
400. Krallinger M, Valencia A (2005) Text-mining and information-retrieval services for molecular biology. Genome Biol 6:224
401. Krallinger M, Valencia A, Hirschman L (2008) Linking genes to literature: text mining, information extraction, and retrieval applications for biology. Genome Biol 9:S8
402. Lincoln P, Tiwari A (2004) Symbolic systems biology: hybrid modeling and analysis of biological networks. In: Bemporad A, Bicchi A, Buttazzo G (eds) Hybrid systems: computation and control HSCC, volume 2993 of LNCS. Springer, Berlin/Heidelberg/New York, pp 660-672
403. Liu, Y, Carbonell J et al (2005) Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition. Research in Computational Molecular Biology. S. Miyano, J. Mesirov, S. Kasif et al, Springer Berlin/Heidelberg. 3500:408-422.
404. Longabaugh WJR, Davidson EH, Bolouri H (2005) Computational representation of developmental genetic regulatory networks. Dev Biol 283:1-16
405. Longabaugh WJR, Davidson EH, Bolouri H (2009) Visualization, documentation, analysis, and communication of large-scale gene regulatory networks. Biochimica et Biophysica Acta (BBA)-Gene Regul Mech 1789:363-374
406. McShan DC, Rao S, Shah I (2003) PathMiner: predicting metabolic pathways by heuristic search. Bioinformatics 19:1692-1698
407. Mestl T, Plahte E, Omholt SW (1995) A mathematical framework for describing and analysing gene regulatory networks. J Theor Biol 176:291-300
408. Mika S, Rost B (2004) NLProt: extracting protein names and sequences from papers. Nucleic Acids Res 32:W634-W637
409. Mitrofanova A (2009) Efficient systems biology algorithms for biological networks over multiple time-scales: from evolutionary to regulatory time. Doctoral dissertation, Department of Computer Science, Courant Institute of Mathematical Sciences, New York University
410. Ning K, Leong H (2006) Towards a better solution to the shortest common supersequence problem: the deposition and reduction algorithm. BMC Bioinformatics 7:S12
411. Peng Y, Zhang X (2007) Integrative data mining in systems biology: from text to network mining. Artif Intell Med 41:83-86
412. Prill RJ et al (2010) Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS One 5:e9202
413. Ramakrishnan N et al (2010) Reverse engineering dynamic temporal models of biological processes and their relationships. Proc Natl Acad Sci 107:12511-12516
414. Rigoutsos I, Stephanopoulos G (2007) Systems biology, Series in systems biology. Oxford University Press, Oxford/New York
415. Sauro HM et al (2003) Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. OMICS J Integrat Biol 7:355-372
416. Schulz M et al (2006) SBMLmerge, a system for combining biochemical network models. Genome Inform 17:62-71
417. Settles B (2004) Biomedical named entity recognition using conditional random fields and rich feature sets. In: Proceedings of the international joint workshop on natural language processing in biomedicine and its applications. Association for Computational Linguistics, Geneva, pp 104-107
418. Shmulevich I et al (2002) Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18:261-274
419. Shu et al (2007) Simulation study in Probabilistic Boolean Network models for genetic regulatory networks. Int J Data Min Bioinform 1:217-240
420. Smolen P, Baxter DA, Byrne JH (2000) Mathematical modeling of gene networks. Neuron 26:567-580
421. Smoot ME et al (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431-432
422. Street N, Jansson S, Hvidsten T (2011) A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation. BMC Plant Biol 11:13
423. Tanabe L, Wilbur WJ (2002) Tagging gene and protein names in biomedical text. Bioinformatics 18:1124-1132
424. Thomas PD et al (2009) A systems biology network model for genetic association studies of nicotine addiction and treatment. Pharmacogenet Genomics 19:538-551, 510.1097/FPC.1090b1013e32832e32832ced
425. Van Someren EP, Wessels LFA, Reinders MJT (2000) Linear modeling of genetic networks from experimental data. In: Bourne PE et al (eds) Eighth international conference on intelligent systems for molecular biology. AAAI, La Jolla/San Diego, pp 355-366
426. Weaver DC, Workman CT, Stormo GD (1999) Modeling regulatory networks with weight matrices. Pac Symp Biocomput 4:112-123
427. Wilson RJ (2010) Introduction to graph theory. Longman, Harlow/New York
428. Yang Y, Adelstein SJ, Kassis AI (2009) Target discovery from data mining approaches. Drug Discov Today 14:147-154
429. Yuh C-H, Bolouri H, Davidson EH (1998) Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene. Science 279:1896-1902
430. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Machine learning: proceedings of the thirteenth international conference, San Francisco
431. Hoppensteadt FC, Peskin CS (2002) Modeling and simulation in medicine and the life sciences. Springer, New York
432. Leach AR (2001) Molecular modelling: principles and applications. Prentice Hall, Harlow/New York
433. Riley RH (2008) A manual of simulation in healthcare. Oxford University Press, New York
434. Schlick T (2002) Molecular modeling and simulation: an interdisciplinary guide, vol 21, Interdisciplinary applied mathematics. Springer, New York
435. Schwartz R (2008) Biological modeling and simulation: a survey of practical models, algorithms, and numerical methods. MIT Press, Cambridge
436. Sokolowski JA, Banks CM (2009) Principles of modeling and simulation: a multidisciplinary approach. John Wiley, Hoboken
437. Velten K (2009) Mathematical modeling and simulation: introduction for scientists and engineers. Wiley-VCH, Weinheim
438. Wu L-Y et al (2009) Conditional random pattern algorithm for LOH inference and segmentation. Bioinformatics 25:61-67
439. Zeigler BP, Kim TG, Praehofer H (2000) Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems. Academic, San Diego