Abstract
We propose a method for predicting types of protein-protein interactions using a multiple-instance learning (MIL) model. Given an interaction type to be predicted, the MIL model was trained using interaction data collected from biological pathways, where positive bags were constructed from interactions between protein complexes of that type, and negative bags from those of other types. In an experiment using the KEGG pathways and the Gene Ontology, the method successfully predicted an interaction type (phosphorylation) to an accuracy rate of 86.1%.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Gene Ontology. Nature Genetics 25, 25–29 (2000), http://www.geneontology.org/
Dietterich, T.G., Lathrop, R H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1–2), 31–71 (1997)
Ekins, S., Nikolsky, Y., Nikolskaya, T.: Techniques: Application of systems biology to absorption, distribution, metabolism, excretion and toxicity. Trends in Pharmacological Sciences 26(4), 202–209 (2005)
Kanehisa, M., Goto, S.: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research 28(1), 27–30 (2000), http://www.genome.jp/kegg/
Lee, M S, Park, S.-S., Kim, M K: A protein interaction verification system based on a neural network algorithm. In: CSB Workshops, pp. 151–154. IEEE Computer Society Press, Los Alamitos (2005)
Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Advances in Neural Information Processing Systems, vol. 10, MIT Press, Cambridge (1998)
Peri, S., Navarro, J D., Amanchy, R., Kristiansen, T.Z., Jonnalagadda, C.K., Surendranath, V., Niranjan, V., Muthusamy, B., Gandhi, T.K.B., Gronborg, M., Ibarrola, N., Deshpande, N., Shanker, K., Shivashankar, H.N., Rashmi, B.P., Ramya, M.A., Zhao, Z., Chandrika, K.N., Padma, N., Harsha, H.C., Yatish, A.J., Kavitha, M.P., Menezes, M., Choudhury, D.R., Suresh, S., Ghosh, N., Saravana, R., Chandran, S., Krishna, S., Joy, M., Anand, S.K., Madavan, V., Joseph, A., Wong, G.W., Schiemann, W.P., Constantinescu, S.N., Huang, L., Khosravi-Far, R., Steen, H., Tewari, M., Ghaffari, S., Blobe, G.C., Dang, C.V., Garcia, J.G.N., Pevsner, J., Jensen, O.N., Roepstorff, P., Deshpande, K.S., Chinnaiyan, A.M., Hamosh, A., Chakravarti, A., Pandey, A.: Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Research 13(10), 2363–2371 (2003)
Rhodes, D.R., Tomlins, S.A., Varambally, S., Mahavisno, V., Barrette, T., Kalyana-Sundaram, S., Ghosh, D., Pandey, A., Chinnaiyan, A.M.: Probabilistic model of the human protein-protein interaction network. Nature Biotechnology 23(8), 951–959 (2005)
Yang, J., Yan, R., Hauptmann, A.G.: Multiple instance learning for labeling faces in broadcasting news video. In: Proceedings of the 13th annual ACM international conference on Multimedia, pp. 31–40. ACM Press, New York (2005)
Zhou, Z.-H., Jiang, K., Li, M.: Multi-instance learning based web mining. Applied Intelligence 22(2), 135–147 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Yamakawa, H., Maruhashi, K., Nakao, Y. (2007). Predicting Types of Protein-Protein Interactions Using a Multiple-Instance Learning Model. In: Washio, T., Satoh, K., Takeda, H., Inokuchi, A. (eds) New Frontiers in Artificial Intelligence. JSAI 2006. Lecture Notes in Computer Science(), vol 4384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69902-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-69902-6_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69901-9
Online ISBN: 978-3-540-69902-6
eBook Packages: Computer ScienceComputer Science (R0)