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  • 1. 1. Papers for the Theme: Data Mining Methodology and Techniques – what data mining methods are applicable and when. Books: P. Baldi and S. Brunak, “Bioinformatics, The Machine Learning Approach,” Second Edition, The MIT Press, Cambridge, Massachusetts, 2001. Jiawei Han, Micheline Kamber : Data Mining, Concepts and Techniques, Morgan Kaufmann 2001 Richard O. Duda, Peter E. Hart, David G. Stork: Pattern Classification, Wiley Interscience Isaac S. Kohane, Alvin T. Kho, and Atul J. Butte: Microarrays for an Integrative Genomics, MIT Press. 2002 Pierre Baldi and Wesley G. Hatfield: DNA Microarrays and Gene Expression, Cambridge University Press. 2002 James M. Bower and Hamid Bolouri: Computational Modeling of Genetic and Biochemical Networks, MIT Press. 2002 S. K. Moore, “ Understanding The Human Genome,” IEEE Spectrum, November 2000, pp. 33-42. J. Seo and B. Shneiderman, “Interactively Exploring Hierarchical Clustering Results,” Computer, July 2002, pp.80-86. S. H. Friend and R. B. Stoughton, “The Magic of Microarray,” Scientific American, February 2002, pp. 44-49. Papers: (a) Large data problem: I. Foster and C. Kesselman. “Computational Grids,” Chapter 2 of "The Grid: Blueprint for a New Computing Infrastructure", Morgan-Kaufman, 1999. M. Karo, C. Dwan, J. Freeman, J. Weissman, M. Livny, E. Retzel, “Applying Grid technologies to bioinformatics,” Proceedings. 10th IEEE International Symposium on High Performance Distributed Computing,, 2001 pp. 441 –442. (b) Microarray process and overview: A. B. Goryachev, P. F. MacGregor and A. M. Edwards, “Unfolding Microarray Data,” Journal of Computational Biology, Volume 8, Number 4, 2001, pp. 443-461. IEEE Computer, July 2002, articles dedicated to bioinformatics. F. Model, T. Konig, C. Piepenbrock, P. Adorjan, “Statistical Process control for large scale microarray experiments,” Proceedings of Intelligent Systems for Molecular Biology, Edmonton, Alberta, Canada, August 2002. (c) Image analysis: M. Steinfath, W. Wruck, H. Seidel, H. Lehrach, U. Radelof, and J. O’Brien, “Automated image analysis for array hybridization experiments,” Bioinformatics 2001 17: 634-641. A. N. Jain, T. A. Tokuyasu, A. M. Snijders, R. Segraves, D. G. Albertson and D. Pinkel, “Fully Automated Quantification of Microarray Image Data,” Genome Research, Vol. 12, Issue 2, February 2002, pp. 325-332.
  • 2. M. Katzer, F. Kummert and G. Sagerer, “Robust Automatic Microarray Image Analysis,” In Proceedings of the International Conference on Bioinformatics: North-South Networking, Bangkok, 2002. (d) Data mining application T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller, M Loh, J.R. Downing, M.A. Caligiuri, “Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring,” Science 286: 1999, pp. 531-537. A.D. Keller, M. Schummer, L. Hood, W. L. Ruzzo, “ Bayesian Classification of DNA array Expression Data,” Tech. Report. UW-CSE-2000-08-01, August 2000. R. Miki, K. Kadota, H. Bono, Y. Mizuno, Y. Tomaru, P. Carninci, M. Itoh, K. Shibata, J. Kawai, H. Konno,et al. Delineating developmental and metabolic pathways in vivo by expression profiling using the RIKEN set of 18,816 full-length enriched mouse cDNA arrays. Proc. Natl. Acad. Sci. USA 98: 2199-2204, 2001. M. B. Einsen, P. T. Spellman, P. O. Brown, D. Botstein, “ Cluster Analysis and display of genome-wide expression patterns,” Proc. Natl. Acad. Sci., Vol. 95, pp. 14863-14868, December 1998, USA. (e) Visualization R. M Adams, B. Stancapiano, M. McKenna, D. Small, “Case Study: A Virtual Environment for Genomic Data Visualization,”Proceedings of IEEE Visualization, Boston, Oct. 2002. M. Sultan, D.A. Wigle, C.A. Cumbaa, M. Maziarz, J. Glasgow, M.S. Tsao and I. Jurisica, “ Binary Tree-structured vector quantization approach to clustering and visualizing microarray data,” Proceedings of Intelligent Systems for Molecular Biology, Edmonton, Alberta, Canada, August 2002. Software Products: Affymetrix Inc., “Gene Chip Arrays,” Product Description at http://www.affymetrix.com/ index.affx. P. Bajcsy, “Image To Knowledge (I2K),” Software Documentation at http://www.ncsa.uiuc.edu/Divisions/DMV/ALG/activities/projects/i2k/documentation/ind ex.html. Axon Instruments Inc., “GenePix Pro,” Product Description at http://www.axon.com/GN_Genomics.html. Scanalytics Inc., “MicroArray Suite,” Product Description at http://www.scanalytics.com/ product/hts/microarray.html. CLONDIAG Chip Technologies,” FluorIS: Array Standardization Tool,” Product Description at http://www.clondiag.com/products/dispo/fluoris/index.php. Packard BioChip Technologies, LLC, “Quant Array Analysis Software,” Product Description at http://www.packardbioscience.com/products/521.asp. Imaging Research Inc, “Array Vision,” Product Description at http://www.imagingresearch.com/products/Genomics_Software.asp. M. Eisen, “ScanAlyze, ” Product Description at.http://rana.lbl.gov/EisenSoftware.htm. CSIRO Mathematical and Informational Sciences, “Spot Image Analysis Software,” Product Documentation at http://experimental.act.cmis.csiro.au/Spot/index.php.
  • 3. J. Buhler, T. Ideker, D. Haynor, “ Dapple: Improved Techniques for Finding Spots on DNA Microarrays,” UV CSE Technical Report UWTR 2000-08-05. 2. Papers for the Theme: Genetic Networks – can we simulate a network of genes with mathematical tools? Books: James M. Bower and Hamid Bolouri: Computational Modeling of Genetic and Biochemical Networks, MIT Press. 2002 Pierre Baldi and Wesley G. Hatfield: DNA Microarrays and Gene Expression, Cambridge University Press. 2002 Isaac S. Kohane, Alvin T. Kho, and Atul J. Butte: Microarrays for an Integrative Genomics, MIT Press. 2002 Papers: V.A. Smith, E.D. Jarvis, A.J. Hartemink Influence of Network Topology and Data Collection on Network Inference Pacific Symposium on Biocomputing 8 2003 http://www.smi.stanford.edu/projects/helix/psb03/smith.pdf L. Chrisman, P. Langley, S. Bay Incorporating Biological Knowledge into Evaluation of Causal Regulatory Hypotheses Pacific Symposium on Biocomputing 8 2003 http://www.smi.stanford.edu/projects/helix/psb03/chrisman.pdf Trey Ideker, Owen Ozier, Benno Schwikowski, and Andrew F. Siegel Discovering regulatory and signalling circuits in molecular interaction networks Bioinformatics 2002 18: S233-S240. Patrik D’haeseleer, Shoudan Liang, and Roland Somogyi, Genetic network inference: from co-expression clustering to reverse engineering Bioinformatics 2000 16: 707-726. A.J. Hartemink, D.K. Gifford, T.S. Jaakkola, and R.A. Young Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models Pacific Symposium on Biocomputing 7:437-449 (2002). http://www.smi.stanford.edu/projects/helix/psb02/hartemink.pdf C. Yoo, V. Thorsson, and G.F. Cooper Discovery of Causal Relationships in a Gene- Regulation Pathway from a Mixture of Experimental and Observational DNA Microarray Data Pacific Symposium on Biocomputing 7:498-509 (2002). http://www.smi.stanford.edu/projects/helix/psb02/yoo.pdf L.F.A. Wessels, E.P. Van Someren, and M.J.T. Reinders A Comparison of Genetic Network Models Pacific Symposium on Biocomputing 6:508-519 (2001). http://www.smi.stanford.edu/projects/helix/psb01/wessels.pdf
  • 4. T.E. Ideker, V. Thorsson, and R.M. Karp Discovery of Regulatory Interactions Through Perturbation: Inference and Experimental Design Pacific Symposium on Biocomputing 5:302-313 (2000). http://www.smi.stanford.edu/projects/helix/psb00/ideker.pdf H. Matsuno, A. Doi, M. Nagasaki, and S. Miyano Hybrid Petri Net Representation of Gene Regulatory Network Pacific Symposium on Biocomputing 5:338-349 (2000). http://www.smi.stanford.edu/projects/helix/psb00/matsuno.pdf Ronen M, Rosenberg R, Shraiman BI, et al. Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics P NATL ACAD SCI USA 99 (16): 10555-10560 AUG 6 2002 3. Papers for the Theme: Biological Knowledge Integration – how can we analyze heterogeneous biological data sets? Daniel Hanisch, Alexander Zien, Ralf Zimmer, and Thomas Lengauer Co-clustering of biological networks and gene expression data Bioinformatics 2002 18: S145-S154. Hill DP, Blake JA, Richardson JE, et al. Extension and integration of the gene ontology (GO): Combining GO vocabularies with external vocabularies GENOME RES 12 (12): 1982-1991 DEC 2002 Desiere F, German B, Watzke H, et al. Bioinformatics and data knowledge: the new frontiers for nutrition and foods TRENDS FOOD SCI TECH 12 (7): 215-229 JUL 2001 Sujansky W Heterogeneous database integration in biomedicine J BIOMED INFORM 34 (4): 285-298 AUG 2001 4. Papers for the Theme: Comparative Functional Genomics – how do we compare microarray experiments across multiple conditions and multiple species? Andrew I. Su, Michael P. Cooke, Keith A. Ching, Yaron Hakak, John R. Walker, Tim Wiltshire, Anthony P. Orth, Raquel G. Vega, Lisa M. Sapinoso, Aziz Moqrich, Ardem Patapoutian, Garret M. Hampton, Peter G. Schultz, and John B. Hogenesch 2002 Large- scale analysis of the human and mouse transcriptomes PNAS 2002 99: 4465-4470 Inwald A, Hinds J, Dale J, et al. Microarray-based comparative genomics: genome plasticity in Mycobacterium bovis. COMPAR FUNCT GENOM 3 (4): 342-344 AUG 2002
  • 5. SolinasToldo S, Lampel S, Stilgenbauer S, et al. Matrix-based comparative genomic hybridization: Biochips to screen for genomic imbalances GENE CHROMOSOME CANC 20 (4): 399-407 DEC 1997 Hsiao LL, Dangond F, Yoshida T, et al. A compendium of gene expression in normal human tissues PHYSIOL GENOMICS 7 (2): 97-104 DEC 2001 5. Papers for the Theme: Statistical Approaches to Microarray Quality Assurance Control – what is our statistical confidence in reported experimental results? [1] P. Baldi and S. Brunak, “Bioinformatics, The Machine Learning Approach,” Second Edition, The MIT Press, Cambridge, Massachusetts, 2001. [2] M. Maziarz and R. Kustra, “Spotting Error in Microarray Data,” Poster Proceedings of the 10th Intelligent Systems for Molecular Biology, Edmonton, Alberta, Canada, 3-7 August 2002, pp. 102. [3] P. Bajcsy, “Image To Knowledge (I2K),” Software Documentation at http://www.ncsa.uiuc.edu/Divisions/DMV/ALG/activities/projects/i2k/documentatio n/index.html. [4] Axon Instruments Inc., “GenePix Pro,” Product Description at http://www.axon.com/GN_Genomics.html. [5] Scanalytics Inc., “MicroArray Suite,” Product Description at http://www.scanalytics.com/product/hts/microarray.html. [6] Packard BioChip Technologies, LLC, “Quant Array Analysis Software,” Product Description at http://www.packardbioscience.com/products/521.asp. [7] Imaging Research Inc, “Array Vision,” Product Description at http://www.imagingresearch.com/products/Genomics_Software.asp. [8] M. Eisen, “ScanAlyze, ” Product Description at.http://rana.lbl.gov/EisenSoftware.htm. [9] CSIRO Mathematical and Informational Sciences, “Spot Image Analysis Software,” Product Documentation at http://experimental.act.cmis.csiro.au/Spot/index.php. [10] J. Buhler, T. Ideker, D. Haynor, “ Dapple: Improved Techniques for Finding Spots on DNA Microarrays,” UV CSE Technical Report UWTR 2000-08-05. [11] B. Goryachev, P. F. MacGregor and A. M. Edwards, “Unfolding Microarray Data,” Journal of Computational Biology, Volume 8, Number 4, 2001, pp. 443-461. [12] M. Steinfath, W. Wruck, H. Seidel, H. Lehrach, U. Radelof, and J. O’Brien, “Automated image analysis for array hybridization experiments,” Bioinformatics 2001 17: 634-641.
  • 6. [13] N. Jain, T. A. Tokuyasu, A. M. Snijders, R. Segraves, D. G. Albertson and D. Pinkel, “Fully Automated Quantification of Microarray Image Data,” Genome Research, Vol. 12, Issue 2, February 2002, pp. 325-332. [14] M. Katzer, F. Kummert and G. Sagerer, “Robust Automatic Microarray Image Analysis,” In Proceedings of the International Conference on Bioinformatics: North- South Networking, Bangkok, 2002. [15] P. Bajcsy and L. Liu, “An Image-Based Visualization of Microarray Features and Classification Results,” Poster Proceedings of the 10th Intelligent Systems for Molecular Biology, Edmonton, Alberta, Canada, 3-7 August 2002, pp. 59. [16] P. Bajcsy, Gridline: Automatic Grid Alignment in DNA Microarray Scans,” IEEE Transactions on Image Processing, submitted October 2002. [17] C. S. Brown, P. C. Goodwin and P. K. Sorger (2001), "Image metrics in the statistical analysis of DNA microarray data ", Proceedings of the National Academy of Sciences, 98(16):8944-8949. [18] D. Rocke and B. Durbin, "A model for measurement error for gene expression arrays", Journal of Computational Biology, 8(6):557-569. [19] X. Wang, S. Ghosh, and Sun-Wei Guo (2001), "Quantitative quality control in microarray image processing and data acquisition", Nucleic Acids Research, 29(15):e75. [20] G. C. Tseng, Min-Kyu Oh, L. Rohlin, J. C. Liao, and W. H. Wong Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects Nucleic Acids Res. 2001 29: 2549-2557 6. Papers for the Theme: Systems Biology – can we model complex systems using microarray analysis, promoter analysis, genetic networks and pathway analysis? Kitano H Computational systems biology NATURE 420 (6912): 206-210 NOV 14 2002. Ideker T, Galitski T, Hood L A new approach to decoding life: Systems biology ANNU REV GENOM HUM G 2: 343-372 2001 Ideker T, Thorsson V, Ranish JA, et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network SCIENCE 292 (5518): 929-934 MAY 4 2001 Rosenfeld N, Elowitz MB, Alon U Negative autoregulation speeds the response times of transcription networks J MOL BIOL 323 (5): 785-793 NOV 8 2002 Cornish-Bowden A, Cardenas ML Systems biology: Metabolic balance sheets NATURE 420 (6912): 129-130 NOV 14 2002
  • 7. Stelling J, Klamt S, Bettenbrock K, et al. Metabolic network structure determines key aspects of functionality and regulation NATURE 420 (6912): 190-193 NOV 14 2002 Forst CV Network genomics - A novel approach for the analysis of biological systems in the post-genomic era MOL BIOL REP 29 (3): 265-280 SEP 2002 Wolkenhauer O Mathematical modelling in the post-genome era: understanding genome expression and regulation - a system theoretic approach BIOSYSTEMS 65 (1): 1-18 FEB 2002 Ettinger M The complexity of comparing reaction systems BIOINFORMATICS 18 (3): 465-469 MAR 2002 Stoll M, Cowley AW, Tonellato PJ, et al. A genomic-systems biology map for cardiovascular function SCIENCE 294 (5547): 1723-1726 NOV 23 2001