Target Discovery and Validation Reviews and Protocols

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Gene Networks 55 23. Tinerey, L. and Kadane, J. B. (1986) Accurate approximations for posterior moments and marginal densities. J. Am. Stat. Assoc. 81, 82–86. 24. Konishi, S., Ando, T., and Imoto, S. (2004) Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika 91, 27–43. 25. Neapolitan, R. E. (2004) Learning Bayesian Networks. Peirson Prentice Hall, Upper Saddle River, NJ. 26. Ott, S., Imoto, S., and Miyano, S. (2004) Finding optimal models for small gene networks. Pac. Symp. Biocomput. 9, 557–567. 27. Shmulevich, I. and Zhang, W. (2002) Binary analysis and optimization-based normalization of gene expression data. Bioinformatics 18, 555–565. 28. Imoto, S., Higuchi, T., Kim, S., Jeong, E., and Miyano, S. (2005) Residual bootstrapping and median filtering for robust estimation of gene networks from microarray data, in Proceedings of the 2nd Computational Methods in Systems Biology, Lecture Note in Bioinformatics 3082, Springer-Verlag, Berlin, 149–160. 29. Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., and Miyano, S. (2004) Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. J. Bioinform. Comput. Biol. 2, 77–98. 30. Tamada, Y., Kim, S., Bannai, H., et al. (2003) Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics 19 (Suppl. 2), II227–II236. 31. Nariai, N., Kim, S., Imoto, S., and Miyano, S. (2004) Using protein–protein interactions for refining gene networks estimated from microarray data by Bayesian networks. Pac. Symp. Biocomput. 9, 336–347. 32. Nariai, N., Tamada, Y., Imoto, S., and Miyano, S. (2005) Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics 21 (Suppl.), II206–II212. 33. Tamada, Y., Bannai, H., Imoto, S., Katayama, T., Kanehisa, M., and Miyano, S. (2005) Utilizing evolutionary information and gene expression data for estimating gene regulations with Bayesian network models. J. Bioinform. Comput. Biol. 3, 1295–1313. 34. Murphy, K. and Mian, S. (1999) Modelling gene expression data using dynamic Bayesian networks. Technical Report, Computer Science Division, University of California, Berkeley, CA. 35. Kim, S., Imoto, S., and Miyano, S. (2003) Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief. Bioinform. 4, 228–235. 36. Kim, S., Imoto, S., and Miyano, S. (2004) Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems 75, 57–65. 37. Yoshida, R., Imoto, S., and Higuchi, T. (2005) Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching. Proc. IEEE 4th Comput. Syst. Bioinform. Conf., 289–298. 38. Efron, B. and Tibshirani, R. J. (1993) An Introduction to the Bootstrap. Chapman & Hall, New York.
56 Imoto et al. 39. Kamimura, T., Shimodaira, H., Imoto, S., et al. (2003) Multiscale bootstrap analysis of gene networks based on Bayesian networks and nonparametric regression. Genome Inform. 14, 350–351. 40. Shimodaira, H. (2004) Approximately unbiased tests of regions using multistepmultiscale bootstrap resampling. Ann. Stat. 32, 2616–2641. 41. Pearl, J. (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, CA. 42. Cooper, G. (1990) The computational complexity of probabilistic inference using bayesian belief networks. Artif. Intell. 42, 393–405. 43. Lauritzen, S. L. (1996) Graphical Models. Oxford University Press, New York. 44. Neal, R. M. (1993) Probabilistic inference using Markov chain Monte Carlo methods, Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
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METHODS IN MOLECULAR BIOLOGY 360 T
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M E T H O D S I N M O L E C U L A R
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© 2007 Humana Press Inc. 999 River
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vi Preface get. Approaches to targe
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viii Contents 10 Transgenic Animal
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x Contributors SAHOHIME MATSUMOTO
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xii Contents of Volume 2 12 Validat
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2 Sioud disease pathogenesis and pe
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Molecular Profiling of Breast Cance
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6 Discovery of Differentially Expre
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Gene Target Discovery 117 In the fi
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Gene Target Discovery 119 sequences
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Gene Target Discovery 121 There is
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Gene Target Discovery 123 digestion
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Gene Target Discovery 125 Fig. 1. P
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Gene Target Discovery 127 5. Sargen
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Gene Target Discovery 129 41. Berry
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132 Matsumoto, Miyagishi, and Taira
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144 Fig. 1. The ribozyme-expression
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146 Sano and Taira 5. 10X L (low-sa
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148 Sano and Taira ribozymes may cl
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150 Fig. 3. A system for the identi
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152 Sano and Taira 4. Notes 1. We r
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9 Production of siRNA- and cDNA-Tra
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Transfected Cell Arrays 157 Fig. 1.
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Transfected Cell Arrays 159 2. The
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Transfected Cell Arrays 161 5. Simp
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164 Houdebine Fig. 1. Different met
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166 Houdebine concentrations become
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168 Houdebine Fig. 2. Major methods
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170 Houdebine integrate into the me
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172 Houdebine 2.2.3. Knock-In Into
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174 Houdebine as insulators. The co
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176 Houdebine Fig. 5. Different met
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178 Houdebine systems. Moreover, st
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180 Houdebine contribute to generat
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182 Houdebine Fig.7. Example of a v
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184 Houdebine more extensively. Tra
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186 Houdebine and stroma. Several o
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188 Houdebine explained at the mole
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190 Houdebine of the intestinal epi
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192 Houdebine interference of the g
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194 Houdebine 17. Whitelaw, C. B. A
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196 Houdebine 51. Bell, A. C., West
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198 Houdebine 86. Carmichael, G. G.
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200 Houdebine 121. Pailhoux, E., Vi
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202 Houdebine 156. Auerbach, A. B.,
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204 Vijayaraj, Söhl, and Magin 1.
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206 Vijayaraj, Söhl, and Magin Fig
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208 Vijayaraj, Söhl, and Magin fil
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210 Table 1 Orthologous Human and M
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212 Table 2 Orthologous Human and M
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214 Vijayaraj, Söhl, and Magin ulc
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216 Vijayaraj, Söhl, and Magin of
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218 Vijayaraj, Söhl, and Magin sug
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220 Vijayaraj, Söhl, and Magin abs
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222 Vijayaraj, Söhl, and Magin 1.2
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224 Vijayaraj, Söhl, and Magin Fig
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226 Vijayaraj, Söhl, and Magin gen
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228 Vijayaraj, Söhl, and Magin the
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230 Vijayaraj, Söhl, and Magin f.
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234 Vijayaraj, Söhl, and Magin b.
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240 Vijayaraj, Söhl, and Magin alt
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242 Table 3 Time Schedule For Aggre
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250 Vijayaraj, Söhl, and Magin 101
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12 The HUVEC/Matrigel Assay An In V
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256 Skovseth, Küchler, and Haralds
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270 Iversen and Sørensen witnessed
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272 Iversen and Sørensen Fig. 1. P
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274 Iversen and Sørensen the core
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14 An Overview of the Immune System
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Overview of Immune System 279 diffe
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Overview of Immune System 281 Fig.
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Overview of Immune System 283 then
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Overview of Immune System 285 expre
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Overview of Immune System 287 Once
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Overview of Immune System 289 solub
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Overview of Immune System 291 amino
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Overview of Immune System 293 (unre
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Overview of Immune System 297 the c
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Overview of Immune System 299 (44).
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Overview of Immune System 301 demon
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Overview of Immune System 307 some
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Overview of Immune System 309 sera
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Overview of Immune System 313 measu
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Overview of Immune System 315 42. H
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Overview of Immune System 317 74. D
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15 Potential Target Antigens for Im
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Tumor Antigen Discovery 321 develop
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Tumor Antigen Discovery 323 panel b
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Tumor Antigen Discovery 325 16. Joc
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16 Identification of Tumor Antigens
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Immunoproteomics 329 by “shot gun
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Immunoproteomics 331 isoelectric fo
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Immunoproteomics 333 3. 2D-PAGE is
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17 Protein Arrays A Versatile Toolb
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Protein Arrays 337 Table 1 Classifi
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Protein Arrays 339 fractions from c
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Protein Arrays 341 combinatorial sc
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Protein Arrays 343 The ideal proteo
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Protein Arrays 345 18. Cekaite, H.
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Protein Arrays 347 49. Yuko Kawahas
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Index 349 Index A Acetyl-CoA carbox
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Index 351 conditional by using FRT
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Index 353 Recombinant tumor antigen
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