LEARNING WITH GENE ONTOLOGY ANNOTATION USING FEATURE SELECTION AND CONSTRUCTION

REFERENCES

• Akand , E. , M. Bain , and M. Temple . 2007 . Learning from ontological annotation: an application of formal concept analysis to feature construction in the gene ontology . In: Proc. Third Australasian Ontology Workshop (AOW-2007) , eds. T. Meyer and A. Nayak , Vol. 85 , 15 – 23 . Conferences in Research and Practice in Information Technology, Australian Computer Society, Inc.
   Google Scholar
• Alexa , A. , J. Rahnenfuhrer , and T. Lengauer . 2006 . Improved scoring of functional groups from gene expression data by decorrelating GO graph structure . Bioinformatics 22 ( 13 ): 1600 .
   PubMed Web of Science ®Google Scholar
• Alpaydin , E. 2004 . Introduction to Machine Learning. Cambridge , MA : MIT Press .
   Google Scholar
• Antonov , A. , and H. Mewes . 2006 . Complex functionality of gene groups identified from from high-throughput data . Journal of Molecular Biology 363 : 289 – 296 .
   PubMedGoogle Scholar
• Ashburner , M. , and The Gene Ontology Consortium . 2000 . Gene ontology: tool for the unification of biology . Nature Genetics 25 ( 1 ): 25 – 29 .
   PubMed Web of Science ®Google Scholar
• Baader , F. , D. Calvanese , D. McGuinness , D. Nardi , and P. Patel-Schneider . 2003 . The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge , UK : Cambridge University Press .
   Google Scholar
• Bada , M. , R. Stevens , C. Goble , Y. Gil , M. Ashburner , J. A. Blake , J. M. Cherry , M. Harris , and S. Lewis . 2004 . A short study on the success of the gene ontology . Web Semantics: Science, Services and Agents on the World Wide Web 1 ( 2 ): 235 – 240 .
   Google Scholar
• Bain , M. 2002 . Structured features from concept lattices for unsupervised learning and classification . In: AI 2002: Proc. of the 15th Australian Joint Conference on Artificial Intelligence , eds. B. McKay and J. Slaney , 557 – 568 . LNAI 2557 , Berlin : Springer .
   Google Scholar
• Baldi , P. , and W. Hatfield . 2002 . DNA Microarrays and Gene Expression. Cambridge , UK : Cambridge University Press .
   Google Scholar
• Bard , J. , and S. Rhee . 2004 . Ontologies in biology: design, applications and future challenges . Nature Reviews Genetics 5 : 213 – 222 .
   PubMed Web of Science ®Google Scholar
• Bodenreider , O. , and R. Stevens 2006 . Bio-ontologies: current trends and future directions . Briefings in Bioinformatics 7 ( 3 ): 256 – 274 .
   PubMedGoogle Scholar
• Boyle , E. , S. Weng , J. Gollub , H. Jin , D. Botstein , J. M. Cherry , and G. Sherlock . 2004 . GO::TermFinder – open source software for accessing gene ontology information and finding signficantly enriched Gene Ontology terms associated with a list of genes . Bioinformatics 20 ( 18 ): 3710 – 3715 .
   PubMed Web of Science ®Google Scholar
• Carey , V. J. 2004 . Ontology concepts and tools for statistical genomics . Journal of Multivariate Analysis 90 : 213 – 228 .
   Google Scholar
• Carpineto , C. , and G. Romano . 1993 . GALOIS: An order-theoretic approach to conceptual clustering . In: Proc. 10th Intl. Conf. on Machine Learning. Los Altos , CA : Morgan Kaufmann, pp . 33 – 40 .
   Google Scholar
• Causton , H. , B. Ren , S. Koh , C. T. Harbison , E. Kanin , E. G. Jennings , T. I. Lee , H. L. True , E. S. Lander , and R. A. Young . 2001. Remodeling of yeast genome expression in response to environmental changes. Molecular Biology of the Cell 12:323–337.
   PubMed Web of Science ®Google Scholar
• Chaitin , G. 1987 . Information, Randomness and Incompleteness – Papers on Algorithmic Information Theory. Singapore : World Scientific Press .
   Google Scholar
• Ganter , B. , and R. Wille . 1999 . Formal Concept Analysis: Mathematical Foundations. Berlin : Springer .
   Google Scholar
• Godin , R. , and R. Missaoui . 1994 . An incremental concept formation approach for learning from databases . Theoretical Computer Science 133 : 387 – 419 .
   Web of Science ®Google Scholar
• Godon , C. , G. Lagniel , J. Lee , J. M. Buhler , S. Kieffer , M. Perrot , H. Boucherie , M. B. Toledano , and J. Labarre . 1998 . The H202 Stimulon in Saccharomyces cerevisiae . Journal of Biological Chemistry 273 ( 34 ): 22480 – 22489 .
   PubMedGoogle Scholar
• Grossmann , S. , S. Bauer , P. Robinson , and M. Vingron . 2007 . Improved detection of overrepresentation of gene ontology annotations with parent–child analysis . Bioinformatics 23 ( 22 ): 3024 .
   PubMedGoogle Scholar
• Guo , Z. , T. Zhang , X. Li , Q. Wang , J. Xu , H. Yu , J. Zhu , H. Wang , C. Wang , E. J. Topol , and S. Rao . 2005 . Towards precise classification of cancers based on robust gene functional expression profiles . BMC Bioinformatics 6 ( 1 ): 58 .
   PubMedGoogle Scholar
• Guyon , I. , and A. Elisseeff . 2003 . An introduction to variable and feature selection . Journal of Machine Learning Research 3 : 1157 – 1182 .
   Google Scholar
• Hall , M. 2000 . Correlation-based feature selection for discrete and numeric class machine learning . In: Proc. Seventeenth Intl. Conference of Machine Learning. San Mateo , CA : Morgan Kaufmann , pp. 359 – 366 .
   Google Scholar
• Hall , M. , and G. Holmes . 2003 . Benchmarking attribute selection techniques for discrete class data mining . IEEE Transactions on Knowledge and Data Engineering 15 ( 6 ): 1437 – 1447 .
   Web of Science ®Google Scholar
• Kanehisa , M. 2000 . Post-Genome Informatics. Oxford : Oxford University Press .
   Google Scholar
• Kanehisa , M. , and S. Goto . 2002 . KEGG for computational genomics . In: Current Topics in Computational Molecular Biology , eds. T. Jiang , Y. Xu , and M. Zhang , 301 – 315 . Boston : MIT Press .
   Google Scholar
• Khatri , P. , and S. Drăghici . 2005 . Ontological analysis of gene expression data: current tools, limitations, and open problems . Bioinformatics 21 ( 18 ): 3587 – 3595 .
   PubMed Web of Science ®Google Scholar
• Mitchell , T. 1997 . Machine Learning. New York : McGraw-Hill .
   Google Scholar
• Quinlan , J. R. 1993 . C4.5: Programs for Machine Learning. San Mateo , CA : Morgan Kaufmann .
   Google Scholar
• Stevens , R. , M. Aranguren , K. Wolstencroft , U. Sattler , N. Drummond , M. Horridge , and A. Rector . 2007 . Using OWL to model biological knowledge . International Journal of Human-Computer Studies 65 : 583 – 594 .
   Web of Science ®Google Scholar
• Temple , M. , G. Perrone , and I. Dawes . 2005 . Complex cellular responses to reactive oxygen species . Trends in Cell Biology 15 ( 6 ): 319 – 326 .
   PubMed Web of Science ®Google Scholar
• Thorpe , G. , C. Fong , N. Alic , V. Higgins , and I. Dawes . 2004 . Cells have distinct mechanisms to maintain protection against different reactive oxygen species: oxidative-stress-response genes . Proc. Natl. Acad. Sci. USA 101 ( 17 ): 6564 – 6569 .
   PubMed Web of Science ®Google Scholar
• Tucker , C. , and S. Fields . 2004 . Quantitative genome-wide analysis of yeast deletion strain sensitivities to oxidative and chemical stress . Comparative and Functional Genomics 5 : 216 – 224 .
   PubMedGoogle Scholar
• Winzeler , E. , D. Shoemaker , A. Astromoff , H. Liang , K. Anderson , B. Andre , R. Bangham , R. Benito , J. D. Boeke , et al. . 1999 . Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis . Science 285 : 901 – 906 .
   PubMed Web of Science ®Google Scholar
• Witten , I. , and E. Frank . 2005 . Data Mining, () , 2nd ed. . San Francisco , CA : Morgan Kaufmann .
   Google Scholar
• Wroe , C. , R. Stevens , C. Goble , and M. Ashburner , 2003 . A methodology to migrate the gene ontology to a description logic environment using DAML +OIL. In: Proc. of the Pacific Symposium on Biocomputing , pp. 624 – 635 .
   Google Scholar
• Zhang , J. , D. Caragea , and V. Honavar . 2005 . Learning ontology-aware classifiers . In: DS-2005: Proc. Discovery Science Conference , pp. 308 – 321 .
   Google Scholar