David Page
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Abstract
Biological databases contain a wide variety of data types, often with rich relational structure. Consequently multi-relational data mining techniques frequently are applied to biological data. This paper presents several applications of multi-relational data mining to biological data, taking care to cover a broad range of multi-relational data mining techniques.
- Critical assessment of information extraction systems in biology, 2003. www.pdg.cnb.uam.es/BioLink/BioCreative.eval.html.]]Google Scholar
- A. Bernal, U. Ear, and N. Kyrpides. Genomes OnLline database (GOLD): A monitor of genome projects worldwide. Nucleic Acids Research, 29(1):126--127, 2001.]]Google ScholarCross Ref
- J. Bockhorst, M. Craven, D. Page, J. Shavlik, and J. Glasner. A Bayesian network approach to operon prediction. Bioinformatics, 19(10):1227--1235, 2003.]]Google ScholarCross Ref
- J. Bockhorst, Y. Qiu, J. Glasner, M. Liu, F. Blattner, and M. Craven. Predicting bacterial transcription units using sequence and expression data. Bioinformatics, 19(Suppl. 1):34--43, 2003.]]Google ScholarCross Ref
- C. Bryant, S. Muggleton, S. Oliver, D. Kell, P. Reiser, and R. King. Combining inductive logic programming, active learning, and robotics to discover the function of genes. Electronic Transactions in Artificial Intelligence, 2001.]]Google Scholar
- R. Bunescu, R. Ge, R. Kate, R. Mooney, E. Marcotte, and A. Ramani. Learning information extractors for proteins and their interactions. In Working Notes of the ICML Workshop on Machine Learning in Bioinformatics, 2003.]]Google Scholar
- C. Burge and S. Karlin. Prediction of complete gene structures in human genomic DNA. Journal of Molecular Biology, 268:78--94, 1997.]]Google ScholarCross Ref
- M. E. Califf and R. Mooney. Bottom-up relational learning of pattern matching rules for information extraction. Journal of Machine Learning Research, 4:177--210, 2003.]] Google ScholarDigital Library
- J. Cheng, C. Hatzis, H. Hayashi, M. A. Krogel, S. Morishita, D. Page, and J. Sese. KDD Cup 2001 report. SIGKDD Explorations, 3(2):47--64, 2002.]] Google ScholarDigital Library
- L. Chrisman, P. Langley, S. Bay, and A. Pohorille. Incorporating biological knowledge into evaluation of causal regulatory hypotheses. In Proceedings of the Eighth Pacific Symposium on Biocomputing. 2003.]]Google Scholar
- M. Craven. The genomics of a signaling pathway: A KDD cup challenge task. SIGKDD Explorations, 4(2):97--98, 2003.]] Google ScholarDigital Library
- M. Craven and J. Kumlien. Constructing biological knowledge bases by extracting information from text sources. In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, pages 77--86, Heidelberg, Germany, 1999. AAAI Press.]] Google ScholarDigital Library
- M. Craven, D. Page, J. Shavlik; J. Bockhorst, and J. Glasner. A probabilistic learning approach to wholegenome operon prediction. In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pages 116--127, La Jolla, CA, 2000. AAAI Press.]] Google ScholarDigital Library
- M. Craven and S. Slattery. Relational learning with statistical predicate invention: Better models for hypertext. Machine Learning, 43(1--2):97--119, 2001.]] Google ScholarDigital Library
- A. Debnath, R. L. de Compadre, G. Debnath, A. Schusterman, and C. Hansch. Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. Journal of Medicinal Chemistry, 34(2):786--797, 1991.]]Google ScholarCross Ref
- L. Dehaspe, H. Toivonen, and R. King. Finding frequent substructures in chemical compounds. In R. Agrawal, P. Stolorz, and G. Piatetsky-Shapiro, editors, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98). AAAI Press, New York, 1998.]]Google Scholar
- T. Dietterich, R. Lathrop, and T. Lozano-Pérez. Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1--2):31--71, 1997.]] Google ScholarDigital Library
- S. Džeroski, H. Blockeel, B. Kompare, S. Kramer, B. Pfahringer, and W. V. Laer. Experiments in predicting biodegradability. In Proceedings of the Ninth International Workshop on Inductive Logic Programming, pages 80--91. Springer-Verlag LNAI 1634, 1999.]] Google ScholarDigital Library
- S. Fine, Y. Singer, and N. Tishby. The hierarchical hidden Markov model: Analysis and applications. Machine Learning, 32:41--62, 1998.]] Google ScholarDigital Library
- P. Finn, S. Muggleton, D. Page, and A. Srinivasan. Discovery of pharmacophores using Inductive Logic Programming. Machine Learning, 30 :241--270, 1998.]] Google ScholarDigital Library
- D. Freitag and N. Kushmerick. Boosted wrapper induction. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, pages 577--583, Austin, TX, 2000. AAAI Press.]] Google ScholarDigital Library
- C. Helma and S. Kramer. A survey of the predictive toxicology challenge 2000--2001. Bioinformatics, 19( 10):1179--1182, 2003.]]Google Scholar
- L. Hirschman, J. Park, J. Tsujii, L. Wong, and C. Wu. Accomplishments and challenges in literature data mining for biology. Bioinformatics, 18:1553--1561, 2002.]]Google ScholarCross Ref
- L. Hood and D. Galas. The digital code of DNA. Nature, 421:444--448, 2003.]]Google ScholarCross Ref
- A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In Principles of Data Mining and Knowledge Discovery, pages 13--23, 2000.]] Google ScholarCross Ref
- A. Jain, T. Dietterich, R. Lathrop, D. Chapman, R. Critchlow, B. Bauer, T. Webster, and T. Lozano-Pérez. Compass: a shape-based machine learning tool for drug design. Journal of Computer-Aided Molecular Design, 8:635--652, 1994.]]Google ScholarCross Ref
- A. Jain, K. Koile, B. Bauer, and D. Chapman. Compass: Predicting biological activities from molecular surface properties. Journal of Medicinal Chemistry, 37:2315--2327. 1994.]]Google ScholarCross Ref
- P. Karp, M. Riley, S. Paley, and A. Pellegrini-Toole. EcoCyc: Electronic encyclopedia of E. coli genes and metabolism. Nucleic Acids Research, 25(l), 1997.]]Google Scholar
- R. King, S. Muggleton, R. Lewis, and M. Sternberg. Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proceedings of the National Academy of Sciences, 89(23):11322--11326, 1992.]]Google ScholarCross Ref
- R. King, S. Muggleton, A. Srinivasan, and M. Sternberg. Structure-activity relationships derived by machine learning: the use of atoms and their bond connectives to predict mutagenicity by inductive logic programming. Proceedings of the National Academy of Sciences, 93:438--442, 1996.]]Google ScholarCross Ref
- I. Korf, P. Flicek, D. Duan, and M. Brent. Integrating genomic homology into gene structure prediction. Bioinformatics, l7(Suppl. l):S140--S148, 2001.]]Google Scholar
- S. Kramer, L. D. Raedt, and C. Helma. Molecular feature mining in HIV data. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 136--143, 2001.]] Google ScholarDigital Library
- C. Lawrence, S. Altschul, M. Boguski, J. Liu, A. Neuwald, and J. Wootton. Detecting subtle sequence signals: A Gibbs sampling strategy for multiple alignment. Science, 262:208--214, 1993.]]Google ScholarCross Ref
- N. Marchand-Geneste, K. Watson, B. Alsberg, and R. King. A new approach to pharmacophore mapping and qsar analysis using inductive logic programming. application to thermolysin inhibitors and glycogen phosphorylase b inhibitors. Journal of Medicinal Chemistry, 45(2):399--409, January 2002.]]Google ScholarCross Ref
- I. Meyer and R. Durbin. Comparative ab initio prediction of gene structures using pair HMMs. Bioinformatics, 18(10):1309--1318, 2002.]]Google ScholarCross Ref
- M. Molla, P. Andrae, J. Glasner, F. Blattner, and J. Shavlik. Interpreting microarray expression data using text annotating the genes. Information Sciences, 146:75--88, 2002.]] Google ScholarDigital Library
- M. Molla, M. Waddell, D. Page, ind J. Shavlik. Using machine learning to design and interpret geneexpression microarrays. AI Magazine, 2003. (In Press).]] Google ScholarDigital Library
- S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13:245--286, 1995.]]Google ScholarDigital Library
- S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, Tokyo, 1990. Ohmsha.]]Google Scholar
- S. Muggleton, R. King, and M. Sternberg. Protein secondary structure prediction using logic-based machine learning. Protein Engineering, 5(7):647--657, 1992.]]Google ScholarCross Ref
- National Library of Medicine. Pubmed, 1999. http://www.ncbi.nlm.nih.gov/PubMed/.]]Google Scholar
- C. Perlich and F. Provost. Aggregation-based feature invention and relational concept classes. In Proceedings of KDD-03. ACM SIGKDD, August 2003.]] Google ScholarDigital Library
- J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239--2666, 1990.]] Google ScholarDigital Library
- J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In Proceedings of the European Conference on Machine Learning, pages 3--20, Vienna, Austria, 1993.]] Google ScholarDigital Library
- S. Ray and M. Craven. Representing sentence structure in hidden Markov models for information extraction. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pages 1273--1279, Seattle, WA, 2001. Morgan Kaufmann.]]Google Scholar
- M. Rebhan, V. Chalifa-Caspi, J. Prilusky, and D. Lancet. Genecards: Encyclopedia for genes, proteins and diseases, 1997. http://bighost.area.ba.cnr.it/GeneCards.]]Google Scholar
- P. Reiser, R. King, D. Kell, S. Muggleton, C. Bryant, and S. Oliver. Developing a logical model of yeast metabolism. Electronic Transactions in Artificial Intelligence, 2001.]]Google Scholar
- E. Riloff. An empirical study of automated dictionary construction for information extraction in three domains. Artificial Intelligence, 85:101--134, 1996.]] Google ScholarDigital Library
- E. Riloff. The sundance sentence analyzer, 1998. http://www.cs.utah.edu/projects/nlp/.]]Google Scholar
- B. Rost and C. Sander. Combining evolutionary information and neural networks to predict protein secondary structure. Proteins, 19:55--77, 1994.]]Google ScholarCross Ref
- S. Schmidler, J. Liu, and D. Brutlag. Bayesian segmentation of protein secondary structure. Journal of Computational Biology, 7:233--248, 2000.]]Google ScholarCross Ref
- E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller. Rich probabilistic models for gene expression. Bioinforrnatics, 1:l--10, 2001.]]Google Scholar
- H. Shatkay, S. Edwards, W. J. Wilbur, and M. Boguski. Genes, themes and microarrays: Using information retrieval for large-scale gene analysis. In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pages 317--328, La Jolla, CA, 2000. AAAI Press.]] Google ScholarDigital Library
- M. Skounakis, M. Craven, and S. Ray. Hierarchical hidden Markov models for information extraction. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico, 2003. Morgan Kaufmann.]]Google Scholar
- A. Srinivasan and R. King. Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes. In S. Muggleton, editor, Proceedings of the 6th International Workshop on Inductive Logic Programming, pages 352--367. Stockholm University, Royal Institute of Technology, 1996.]] Google ScholarDigital Library
- M. Turcotte, S. Muggleton, and M. Sternberg. Automated discovery of structural signatures of protein fold and function. Journal of Molecular Biology, 306:591--605, 2001.]]Google ScholarCross Ref
- X. Yan and J. Han. Closegraph: Mining closed frequent graph patterns. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'O3), 2003.]] Google ScholarDigital Library
Index Terms
- Biological applications of multi-relational data mining
Index terms have been assigned to the content through auto-classification.
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