K-State KDD Lab: Bioinformatics and Medical Informatics Group

Bioinformatics and Medical Informatics Group
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About the Bioinformatics and Medical Informatics (BMI) Group

(Last updated 23 Feb 2002)

The BMI Working Group is a reading, seminar, and research group jointly organized by members of four labs at Kansas State University:

Crop Simulation Laboratory, Agronomy Department
Developmental Plant Genetics Laboratory, Division of Biology
Laboratory for Knowledge Discovery in Databases (KDD), Computing and Information Sciences (CIS) Department
Natural Metaheuristics Laboratory, Electrical Engineering and Computer Engineering (EECE) Department

Its research emphases are intelligent systems for learning and reasoning in bioinformatics and medical informatics for decision support and diagnosis.

Projects

(Last updated 17 Sep 2002)

Hsu, W. H. Activities of the KSU Bioinformatics and Medical Informatics (BMI) Working Group, 2001-2002. Technical Report KSU-CIS-2002-1, Department of Computing and Information Sciences. Manhattan, KS: Kansas State University
DESCRIBER: Data Entity and Source Code Repository Index for Bioinformatics Experimental Research
[Entity-relational (E-R) data model for DESCRIBER (derived from MAGE-ML): Screen capture (Microsoft Access XP) MS Access XP Database File (4Mb)]
[Documentation of E-R and UML diagrams]
KSU Microarray Gene Expression group [mailing list]

Bioinformatics and Medical Informatics References

(Last updated 25 Feb 2002)

[ARRT00] A. Zien, R. Kuffner, R. Zimmer, T. Lenguaer (2000) Analysis of gene expression data with pathway scores . In Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology. AAAI Press. pdf
[BF01] Y. Barash and N. Friedman (2001, April). Context-specific bayesian clustering for gene expression data. In T. Lengauer et al. (Eds.), RECOMB 2001, Proceedings of the Fifth Annual International Conference on Computational Biology, Montreal, Canada, pp. 12--21. pdf
[BFGK96] C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller Context-specific independence in bayesian networks. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (1996), pp. 115-- 123. pdf
[BSY99] Amir Ben-Dor, Ron Shamir, and Zohar Yakhini Clustering gene expression patterns,InPro- ceedings of the Third Annual International Conference on Computational Molecular Biology (RECOMB'99, Lyon, France, Aug. 11-14, 1999) pdf
[Bu91] W. Buntine. Theory refinement on Bayesian networks . In Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA, pp. 52-60. Morgan Kaufmann, San Mateo, CA, 1991. pdf
[CD00] J. Cheng and M. J. Druzdzel (2000) AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks , Volume 13, pages 155-188. pdf
[CFS99] T. Chen, V. Filkov and S. Skiena (1999) Identifying Gene Regulator Networks from Experimental Data. ACM-SIGAT The Third Annual International Conference on Computational Moledular Biology (RECOMB99), 1999. pdf
[CHM97] D. Chickering, D. Heckerman, and C.Meek. A Bayesian approach to learning Bayesian networks with local structure. In Proceedings of UAI-97, pages 80--89. Morgan Kaufmann, 1997. pdf
[DM00] S. Davies and A. Moore Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Continuous and Discrete Variables tech. report CMU-CS-00-119, Computer Science Department, Carnegie Mellon University, April, 2000. pdf
[FG96] N. Friedman and M. Goldszmidt. Context-specific independence in bayesian networks. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (1996), pp. 115-- 123. pdf
[FGKP99] L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In IJCAI '99. 1999. pdf
[FNP00] N. Friedman, I. Nachman and D. Pe'er Using Bayesian Networks to Analyze Expression Data (Submitted to RECOMB 2000). pdf
[FNP99] N. Friedman, I, Nachman, and D. Pe’er, Learning Bayesian Network Structures from Massive Datasets: The Sparse Candidate Algorithm, In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), pp. 206-215, 1999. pdf
[Fr98] N. Friedman (1998), The Bayesian structural EM algorithm , in G. F. Cooper & S. Moral, eds., `Proc. Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI '98)', Morgan Kaufmann, San Francisco, CA. pdf
[He95] D. Heckerman, A Tutorial on Learning with Bayesian Networks , Technical Report MSR-TR-95-06, Microsoft Research, March 1995. pdf
[He97] D. Heckerman, C. Meekand G. Cooper (1997). A Bayesian approach to causal discovery . Technical Report MSR-TR97 -05. Microsoft Research. pdf
[HGC94] D. Heckerman, D. Geiger, D. Chickering. Learning Bayesian networks: The Combination of Knowledge and Statistical Data .Technical Report MSR-TR-94-09, Microsoft Research, March, 1994 pdf
[Ja97] M. Jaeger (1997) Relational bayesian networks. in `Proceedings of the Thirteenth Conference on Uncertainty in Articial Intelligence (UAI-97)'. pdf
[KKR95] K. Kanazawa, D. Koller, and S.J. Russell. Stochastic simulation algorithms for dynamic probabilistic networks . In Proc. UAI, 1995. pdf
[KP97] D. Koller and A. Pfe er. Object-oriented Bayesian networks. In D. Geiger and P.P. Shenoy, editors, Proc. 13th Conf. on Uncertainty in Articial Intelligence, pages 302-313, Providence, Rhode Island, 1997. pdf
[li99] Jun S. Liu. Markov Chain Monte Carlo and Related Topics Technical Report. Department of Statistics, Stanford University, 1999.
[MC97] S. Monti & G. Cooper (1998b). Learning Hybrid Bayesian Networks from Data. Learning in Graphical Models . Kluwer Academic Publishers. pdf
[MT95] P. Myllymaki and H. Tirri. Constructing computationally efficient Bayesian models via unsupervised clustering . In A.Gammerman, editor, Probabilistic Reasoning and Bayesian Belief Networks, pp. 237-248. Alfred Waller Publishers, Suffolk, 1995. pdf
[Mu01a] Kevin P. Murphy The Bayes Net Toolbox for MATLAB (2001) pdf
[Mu01b] K. Murphy. Learning Bayes net structure from sparse data sets . Technical report, Computer Science Division, University of California, Berkeley, 2001. pdf
[Mu01c] Kevin P. Murphy An introduction to graphical models. Technical report, Intel Research Technical Report., 2001. pdf
[Mu01d] Kevin P. Murphy (2001d). Active Learning of Causal Bayes Net Structure March 2001.
[Mu02] Kevin Murphy. Learning Markov Processes To appear in The Encyclopedia of Cognitive Science L. Nadel et al. (eds), Nature Macmillan, 2002 pdf
[My94] P. Myllymäki. Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines . In Proceedings of SOUTHCON-94, pp. 97-102, Orlando, FL. IEEE Press, 1994. pdf
[Pe91] J. Pearl and T. Verma (1991). A theory of inferred causation , in Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference (Morgan Kaufmann, San Mateo, CA). pdf
[Pe98] J. Pearl (1998). Graphs, causality, and structural equation models . Sociological Methods and Research, 27, 226--84. pdf
[RMH93] A. E. Raftery, D. M. Madigan, and J. Hoeting (1993). Model selection and accounting for model uncertainty in linear regression models . TR 262, Department of Statistics, University of Washington. pdf
[RR94] Rakesh Agrawal and Ramakrishnan Srikant Fast Algorithms for Mining Association Rules . In Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile, September 1994. pdf
[RS97] M. Ramoni and P. Sebastiani. Discovering Bayesian networks in incomplete databases , Technical report KMI-TR-46, Knowledge Media Institute, The Open University, March 1997. pdf
[RS98] M. Ramoni, & P. Sebastiani (1998). Parameter Estimation in Bayesian Networks from Incomplete Databases . Intelligent Data Analysis, 2. 27 pdf
[SGS+] C.Glymour, P.Spirtes, C.Meek and T.Richardson, The TETRAD Project: Constraint Based Aids to Model Specification . R.Scheines, to appear in Multivariate Behavioral Research. pdf
[SGS+00] P. Spirtes, C. Glymour, R. Scheines, S. Kauffman, V. Aimale, and F. Wimberly. Constructing Bayesian network models of gene expression networks from microarray data . In Proceedings of the Atlantic Symposium on Computational Biology. pdf
[SWR00] E. van Someren, L. Wessels, and M. Reinders, Information extraction for modeling gene expressions , Proceedings of the 21st Symposium on Information Theory in the Benelux , pp. 215--222, 2000. pdf
[TK01] S. Tong and D. Koller. Active Learning for Structure in Bayesian Networks . Seventeenth International Joint Conference on Artificial Intelligence, Seattle, Washington, August 2001, pages 863--869. pdf

Group Members and Affiliates (Last updated 13 Mar 2003)

Faculty and Affiliates

Sue Brown, Assistant Professor, Biology
Sanjoy Das, Assistant Professor, Electrical Engineering and Computer Engineering (EECE)
William H. Hsu, Assistant Professor, Computing and Information Sciences (CIS)
Lev Kapitanski, Professor, Mathematics
Jan E. Leach, University Distinguished Professor, Plant Pathology
J. Clare Nelson, Assistant Professor, Plant Pathology
Judith L. Roe, Assistant Professor, Biology
Virgil Wallentine, Professor and Department Head, Computing and Information Sciences (CIS)
Stephen M. Welch, Professor, Agronomy

Graduate Students

Undergraduate Students

Julie (Stilson) Thornton, Computing and Information Sciences (CIS) / Mathematics
James W. Plummer, Electrical Engineering and Computer Engineering (EECE)

Alumni

Back to the KDD Lab main page

Group founded: 28 Aug 2001
Page created: 28 Aug 2001
Last updated: 02 Jul 2003

Haipeng Guo