Abstract
The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. These revision or extension are first formulated as theoretical hypotheses, then verified experimentally. Recently, biological data have been produced in great volumes and in diverse formats. It is a major challenge for biologists to process these data to reason about hypotheses. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding “pattern” in data and leave the reasoning to biologists. Few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalism they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with incomplete knowledge, which is often the case with respect to biochemical networks. We present a knowledge based framework for the general problem of hypothesis formation. The framework has been implemented by extending BioSigNet-RR. BioSigNet-RR is a knowledge based system that supports elaboration tolerant representation and non-monotonic reasoning. The main features of the extended system include: (1) seamless integration of hypothesis formation with knowledge representation and reasoning; (2) use of various resources of biological data as well as human expertise to intelligently generate hypotheses. The extended system can be considered as a prototype of an intelligent research assistant of molecular biologists. The system is available at http://www.biosignet.org.
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References
Shrager, J., Langley, P.: Computational Models of Scientific Discovery and Theory Formation. Morgan Kaufmann, San Francisco (1990)
Darden, L.: Recent work in computational scientific discovery. In: Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, pp. 161–166 (1997)
Karp, P.D.: Design methods for scientific hypothesis formation and their application to molecular biology. Machine Learning 12, 89–116 (1993)
Karp, P.D., Ouzounis, C., Paley, S.: HinCyc: A Knowledge Base of the Complete Genome and Metabolic Pathways of H. influenzae. In: Proc. of ISMB (1996)
Zupan, B., et al.: Genepath: a system for inference of genetic networks and proposal of genetic experiments. Artif. Intell. Med. 29, 107–130 (2003)
Karp, P.D., Paley, S., Romero, P.: The pathway tools software. Bioinformatics 18 S225– 232, S225–S232 (2002)
Yaffe, M.B., Leparc, G.G., Lai, J., Obata, T., Volinia, S., Cantley, L.C.: A motif-based profile scanning approach for genome-wide prediction of signaling pathways. Nat Biotechnol 19, 348–353 (2001)
Gomez, S., Rzhetsky, A.: Towards the prediction of complete protein - protein interaction networks. In: Pacific Symposium on Biocomputing 2000 (PSB 2000), pp. 413–424 (2002)
Obenauer, J.C., Cantley, L.C., Yaffe, M.B.: Scansite 2.0: proteome-wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Research 31, 3635–3641 (2003)
Valencia, A., Pazos, F.: Computational methods for the prediction of protein interactions. Curr Opin Struct Biol 12, 368–373 (2003)
Salwinski, L., Eisenberg, D.: Computational methods of analysis of proteinprotein interactions. Current Opinion in Structural Biology 13, 377–382 (2003)
Baral, C.: Knowledge representation, reasoning and declarative problem solving. Cambridge University Press, Cambridge (2003)
Baral, C., Chancellor, K., Tran, N., Tran, N., Berens, M.: A knowledge based approach for representing and reasoning about signaling networks. Bioinformatics 20(Suppl 1), i15–i22 (2004)
Sembugamoorthy, V., Chandrasekaran, B.: Functional Representation of Devices and Compilation of Diagnostic Problem-Solving Systems. Experience, Memory and Reasoning, 47–73 (1986)
Osterman, A., Overbeek, R.: Missing genes in metabolic pathways: a comparative genomics approach. Current Opinion in Chemical Biology 7, 238–251 (2003)
Su, Z., Dam, P., Chen, X., Olman, V., Jiang, T., Palenik, B., Xu, Y.: Computational inference of regulatory pathways in microbes: an application to phosphorus assimilation pathways in synechococcus sp. wh8102. In: Gribskov, M., Kanehisa, M., Miyano, S., Takagi, T. (eds.) Genome Informatics, vol. 14, pp. 3–13 (2003)
Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T.: Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research 13, 2498–2504 (2003)
Baliga, N.S., Bjork, S.J., Bonneau, R., Pan, M., Iloanusi, C., Kottemann, M.C., Hood, L., DiRuggiero, J.: Systems Level Insights Into the Stress Response to UV Radiation in the Halophilic Archaeon Halobacterium NRC-1. Genome Res 14, 1025–1035 (2004)
Racunas, S.A., Shah, N.H., Albert, I., Fedoroff, N.V.: HyBrow: a prototype system for computer-aided hypothesis evaluation. Bioinformatics 20, i257–264 (2004)
King, R., et al.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004)
Tran, N., Baral, C.: Reasoning about triggered actions in AnsProlog and its application to molecular interactions in cells. In: Proc. of KR 2004, pp. 554–563 (2004)
Peirce, C.: Collected papers of Charles Sanders Peirce, vol. 1-8, pp. 1931–1958. Havard University Press, Cambridge
Peirce, C.: Reasoning and the Logic of Things. Havard University Press, Cambridge (1992)
Poole, D., Mackworth, A., Goebel, R.: Computational Intelligence. Oxford University Press, Oxford (1998)
Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. Handbook of logic in Artificial Intelligence and Logic Programming, 235–324 (1998)
Kakas, A.C., Van Nuffelen, B., Denecker, M.: A-system: Problem solving through abduction. In: Proc. of the IJCAI, vol. 1, pp. 591–596 (2001)
Denecker, M., Kakas, A.C.: Abduction in Logic Programming. In: Computational Logic: Logic Programming and Beyond, pp. 402–436 (2002)
Doherty, P., Kertes, S., Magnusson, M., Szalas, A.: Towards a Logical Analysis of Biochemical Pathways. In: Alferes, J.J., Leite, J. (eds.) JELIA 2004. LNCS (LNAI), vol. 3229, pp. 667–679. Springer, Heidelberg (2004)
Reiter, R.: A theory of diagnosis from first principles. Artificial Intelligence 13, 81–132 (1980)
Allen, J., Kautz, H., Pelavin, R., Tenenberg, J.: Reasoning about plans. Morgan Kaufmann, San Mateo (1991)
Missiaen, M., Bruynooghe, L., Denecker, M.: CHICA: A planning system based on event calculus. J. Logic Comput. 5, 579–602 (1995)
Poole, D.: A logical framework for default reasoning. Artificial Intelligence 36, 27–48 (1988)
Eshghi, K., Kowalski, R.: Abduction compated with negation as failure. In: Proc. 6th Inter. Conf. in Logic Programming, pp. 234–255 (1989)
Boutilier, C.: Abduction to plausible causes: An even based model of belief update. Artificial Intelligence 83, 143–166 (1996)
Rzhetsky, A., Iossifov, I., Koike, T., Krauthammer, M., Kra, P., Morris, M., Yu, H., Duboue, P.A., Weng, W., Wilbur, W.J., Hatzivassiloglou, V., Friedman, C.: Geneways: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. J. of Biomedical Informatics 37, 43–53 (2004)
Bouwmeester, T., et al.: A physical and functional map of the human TNF-alpha/NF-kappaB signal transduction pathway. Nat. Cell. Biol. 6, 97–105 (2004)
Xenarios, I., Rice, D.E., Salwinski, L., Baron, M.K., Marcotte, E.M., Eisenberg, D.: Dip: The database of interacting proteins. Nucleic Acids Research 28, 289–291 (2000)
Bader, G.D., Donaldson, I., Wolting, C., Ouellette, B.F.F., Pawson, T., Hogue, C.W.V.: Bind the biomolecular interaction network database. Nucleic Acids Research 29, 242–245 (2001)
Demir, E., Babur, O., Dogrusoz, U., Gursoy, A., Ayaz, A., Gulesir, G., Nisanci, G., Cetin-Atalay, R.: An ontology for collaborative construction and analysis of cellular pathways. Bioinformatics 20, 349–356 (2004)
Joshi-Tope, G., Gillespie, M., Vastrik, I., D’Eustachio, P., Schmidt, E., de Bono, B., Jassal, B., Gopinath, G., Wu, G., Matthews, L., Lewis, S., Birney, E., Stein, L.: Reactome: a knowledgebase of biological pathways. Nucl. Acids Res. 33, D428–D432 (2005)
Michael, D., Oren, M.: The p53 and Mdm2 families in cancer. Curr. Opin. Genet. Dev. 12, 53–59 (2002)
Hamid, T., Kakar, S.: PTTG/securin activates expression of p53 and modulates its function. Mol. Cancer. 3, 18 (2004)
Bode, A.M., Dong, Z.: Post-translational modification of p53 in tumorigenesis. Nat. Rev. Cancer. 4, 793–805 (2004)
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Tran, N., Baral, C., Nagaraj, V.J., Joshi, L. (2005). Knowledge-Based Integrative Framework for Hypothesis Formation in Biochemical Networks. In: Ludäscher, B., Raschid, L. (eds) Data Integration in the Life Sciences. DILS 2005. Lecture Notes in Computer Science(), vol 3615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11530084_11
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DOI: https://doi.org/10.1007/11530084_11
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