On the potential of domain literature for clustering and Bayesian network learning

ABSTRACT

Thanks to its increasing availability, electronic literature can now be a major source of information when developing complex statistical models where data is scarce or contains much noise. This raises the question of how to integrate information from domain literature with statistical data. Because quantifying similarities or dependencies between variables is a basic building block in knowledge discovery, we consider here the following question. Which vector representations of text and which statistical scores of similarity or dependency support best the use of literature in statistical models? For the text source, we assume to have annotations for the domain variables as short free-text descriptions and optionally to have a large literature repository from which we can further expand the annotations. For evaluation, we contrast the variables similarities or dependencies obtained from text using different annotation sources and vector representations with those obtained from measurement data or expert assessments. Specifically, we consider two learning problems: clustering and Bayesian network learning. Firstly, we report performance (against an expert reference) for clustering yeast genes from textual annotations. Secondly, we assess the agreement between text-based and data-based scores of variable dependencies when learning Bayesian network substructures for the task of modeling the joint distribution of clinical measurements of ovarian tumors.

REFERENCES

• 1: Péter Antal , Bart de Moor , Tamás Mészáros , Tadeusz Dobrowiecki, Annotated Bayesian Networks: A Tool to Integrate Textual and Probabilistic Medical Knowledge, Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems, p.177, March 26-27, 2001
• 2: P. Antal , D. Timmerman , T. Mészáros , T. Dobrowiecki, DOMAIN KNOWLEDGE BASED INFORMATION RETRIEVAL LANGUAGE: AN APPLICATION OF ANNOTATED BAYESIAN NETWORK IN OVARIAN CANCER DOMAIN, Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02), p.213, June 04-07, 2002
• 3: C. Blaschke, J. Oliveros, and A. Valencia. Mining functional information associated with expression arrays. Funct Integr Genomics, 1:256--268, 2001.
• 4: Gregory F. Cooper , Edward Herskovits, A Bayesian Method for the Induction of Probabilistic Networks from Data, Machine Learning, v.9 n.4, p.309-347, Oct. 1992 [doi>10.1023/A:1022649401552]
• 5: D. M. et al. Use of keyword hierarchies to interpret gene expression patterns. Bioinformatics, 17:319--326, 2001.
• 6: W. B. Frakes, Stemming algorithms, Information retrieval: data structures and algorithms, Prentice-Hall, Inc., Upper Saddle River, NJ, 1992
• 7: David Heckerman , Dan Geiger , David M. Chickering, Learning Bayesian Networks: The Combination of Knowledge and Statistical Data, Machine Learning, v.20 n.3, p.197-243, Sept. 1995 [doi>10.1023/A:1022623210503]
• 8: Anil K. Jain , Richard C. Dubes, Algorithms for clustering data, Prentice-Hall, Inc., Upper Saddle River, NJ, 1988
• 9: T. Jenssen, A. Laegreid, J. Komorowski, and E. Hovig. A literature network of human genes for high-throughput analysis of gene expression. Nature Genetics, 28:21--28, may 2001.
• 10: L. Kaufman and P. Rousseeuw. Finding groups in data. Wiley-Interscience, 1990.
• 11: Robert R. Korfhage, Information storage and retrieval, John Wiley & Sons, Inc., New York, NY, 1997
• 12: D. Masys. Linking microarray data to the literature. Nature Genetics, 28:9--10, 2001.
• 13: G. Milligan and M. Cooper. A study of the comparability of external criteria for hierarchical cluster analysis. Multivariate Behavorial Research, 21:441--458, 1986.
• 14: Paul Pavlidis , Jason Weston , Jinsong Cai , William Noble Grundy, Gene functional classification from heterogeneous data, Proceedings of the fifth annual international conference on Computational biology, p.249-255, April 22-25, 2001, Montreal, Quebec, Canada [doi>10.1145/369133.369228]
• 15: Judea Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1988
• 16: J. Quakenbush. Computational analysis of microarray data. Nature Reviews Genetics, 2:418--427, 2001.
• 17: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 18: Hagit Shatkay , Stephen Edwards , W. John Wilbur , Mark Boguski, Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis, Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, p.317-328, August 19-23, 2000
• 19: D. Timmerman. Ultrasonography in the assessment of ovarian and tamoxifen-associated endometrial pathology. Ph.D. dissertation, Leuven University Press, D/1997/1869/70, 1997.
• 20: D. Timmerman, L. Valentin, T. H. Bourne, W. P. Collins, H. Verrelst, and I. Vergote. Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the international ovarian tumor analysis (iota) group. Ultrasound Obstet Gynecol, 16(5):500--505, Oct 2000.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering

Keywords:
Bayesian networks, clustering, data mining, text mining