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Understanding Nature Through the Symbiosis of Information Science, Bioinformatics, and Neuroinformatics Read chapter Read first chapter

2014 | OriginalPaper | Chapter

49. Ontologies and Machine Learning Systems

Authors : Shoba Tegginmath, Russel Pears, Nikola Kasabov

Published in: Springer Handbook of Bio-/Neuroinformatics

Publisher: Springer Berlin Heidelberg

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Abstract

In this chapter we review the uses of ontologies within bioinformatics and neuroinformatics and the various attempts to combine machine learning (ML) and ontologies, and the uses of data mining ontologies. This is a diverse field and there is enormous potential for wider use of ontologies in bioinformatics and neuroinformatics research and system development. A systems biology approach comprising of experimental and computational research using biological, medical, and clinical data is needed to understand complex biological processes and help scientists draw meaningful inferences and to answer questions scientists have not even attempted so far.

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previous chapter Neurocomputational Models of Natural Language
next chapter Integration of Large-Scale Neuroinformatics – The INCF
Literature
49.1.
B. Chandrasekaran, J.R. Josephson, V.R. Benjamins: What are ontologies, and why do we need them?, Intell. Syst. Appl. 14, 20–26 (1999)CrossRef
49.2.
A. Maedche, B. Motik, L. Stojanovic, R. Studer, R. Volz: Ontologies for enterprise knowledge management, Intell. Syst. IEEE 18(2), 26–33 (2003)CrossRef
49.3.
I.H. Witten, E. Frank, M.A. Hall: Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, Burlington 2011)
49.4.
M. Ashburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, A.P. Davis, K. Dolinski, S.S. Dwight, J.T. Eppig, M.A. Harris, D.P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J.C. Matese, J.E. Richardson, M. Ringwald, G.M. Rubin, G. Sherlock: Gene ontology: Tool for the unification of biology, Nat. Genet. 25(1), 25–29 (2000)CrossRef
49.5.
R. Stevens, P. Lord: Application of ontologies in bioinformatics. In: Handbook on Ontologies, ed. by S. Staab, R. Studer (Springer, Berlin, Heidelberg, 2009) pp. 735–756CrossRef
49.6.
49.7.
49.8.
49.9.
49.10.
N. Kasabov: Evolving Connectionist Systems The Knowledge Engineering Approach, 2nd edn. (Springer, Berlin, Heidelberg 2007) p. 451MATH
49.11.
49.12.
N. Kasabov, V. Jain, P.C.M. Gottgtroy, L. Benuskova, S.G. Wysoski, F. Joseph: Evolving brain-gene ontology system (EBGOS): Towards integrating bioinformatics and neuroinformatics data to facilitate discoveries, Int. Joint Conf. Neural Netw. (IJCNN) 2007 (IEEE 2007) pp. 131–135
49.13.
A. Verma, N. Kasabov, E. Rush, Q. Song: Ontology based personalized modeling for chronic disease risk analysis: An integrated approach, LNCS 5506, 1204–1210 (2008)
49.14.
Q. Song, N. Kasabov: TWNFI – a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling, Neural Netw. 19, 1556–1591 (2006)CrossRefMATH
49.15.
K. Khelif, R. Dieng-Kuntz, P. Barbry: An ontology-based approach to support text mining and information retrieval in the biological domain, J. Univers. Comput. Sci. 13(12), 1881–1907 (2007)
49.16.
Y. Kuo, A. Lonie, L. Sonenberg, K. Paizis: Domain Ontology Driven Data Mining: A Medical Case Study, ACM SIGKDD Workshop on Domain Driven DATA MINING (DDDM2007) (ACM, San Jose 2007)
49.17.
C. Diamantini, D. Potena, E. Storti: KDDONTO: An ontology for discovery and composition of KDD algorithms, Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery 19–24 (2009)
49.18.
A. Bernstein, F. Provost, S. Hill: Toward intelligent assistance for a DATA MINING process: An ontology-based approach for cost-sensitive classification, IEEE Trans. Knowl. Data Eng. 17(14), 503–518 (2005)CrossRef
49.19.
P. Panov, S. Dzeroski, L. Soldatova: OntoDM: An ontology of Data Mining, IEEE Int. Conf. DATA MINING Workshops (IEEE, Washington 2008) pp. 752–760
49.20.
R. Ramakrishnan, R. Agrawal, J.-C. Freytag, T. Bollinger, C.W. Clifton, S. Dzeroski, J. Hipp, D. Keim, S. Kramer, H.-P. Kriegel, U. Leser, B. Liu, H. Mannila, R. Meo, S. Morishita, R. Ng, J. Pei, P. Raghavan, M. Spiliopoulou, J. Srivastava, V. Torra: Data mining: The next generation, Perspectives Workshop: Data Mining: The Next Generation, number 04292, Dagstuhl Seminar Proc., ed. by R. Agrawal, J.C. Freytag, R. Ramakrishnan (Internationales Begegnungs- and Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl 2005)
49.21.
P. Panov, L.N. Soldatova, S. Džeroski: Towards an ontology of data mining investigations. In: Discovery Science, ed. by J. Gama (Springer Berlin, Heidelberg 2009) pp. 257–271CrossRef
49.22.
H. Wang, S. Wang: Ontology for data mining and its application to mining incomplete data, J. Database Manag. 19(4), 81–90 (2008)CrossRef
49.23.
M. Hilario, A. Kalousis, P. Nguyen, W. Woznica: A DATA MINING ontology for algorithm selection and meta-mining, Third Generation Data Mining: Towards Service Oriented Towards Service-Oriented Knowledge Discovery (SoKD) (2009) p. 76
49.24.
O. Bodenreider, R. Stevens: Bio-ontologies: Current trends and future directions, Brief. Bioinform. 7(3), 256–274 (2006)CrossRef
49.25.
M. Hepp: Ontologies: State of the art, business potential, and grand challenges. In: Data Management, ed. by M. Hepp, P. De Leenheer, A. de Moor, Y. Sure (Springer, Berlin, Heidelberg 2007) pp. 3–24
Metadata
Title
Ontologies and Machine Learning Systems
Authors
Shoba Tegginmath
Russel Pears
Nikola Kasabov
Copyright Year
2014
Book
Springer Handbook of Bio-/Neuroinformatics

Print ISBN: 978-3-642-30573-3

Electronic ISBN: 978-3-642-30574-0

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-642-30574-0_49

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