Third International BioInfo'2007 Workshop
October
5th, 2007
on Computational Intelligence in
Bioinformatics
Plenary Speakers
Professor Nikola Kasabov
Knowledge Engineering and Discovery Research Institute, KEDRI
Auckland University of Technology, Auckland, New Zealand
nkasabov@aut.ac.nz, www.kedri.inf
Abstract
The paper presents a contemporary approach of building intelligent decision support systems for biomedical applications that integrate ontology knowledge repository methods and systems with machine learning techniques, to facilitate adaptive data and information storage, retrieval, modelling and knowledge discovery. After a patient information is entered into the DSS, that may include clinical and genetic/proteomic data and tests, relevant data and information will be retrieved from the ontology, a personalised neuro-fuzzy inference model will be created using transductive learning and reasoning techniques, and personalised results will be displayed. The results may include risk of disease, outcome prognosis of disease, nutritional dietary advice, personal statistical information, etc.
A generic framework and a software platform are presented that include the following main modules:
- Protege ontology development environment;
- Data retrieval module to search and retrieve relevant data from an ontology;
- Machine learning module that includes traditional statistical analysis and learning techniques, rule based reasoning systems, several neural network learning techniques, fuzzy rule representation and reasoning techniques, novel personalised neuro-fuzzy inference techniques developed and published by KEDRI (www.kedri.info);
- A friendly user interface that can be tailored for specific applications.
The methodology and the platform are illustrated on three case studies of specialised ontology based decision support systems:
1) Chronic disease personal risk evaluation (cardio-vascular disease, obesity, diabetes II) based on clinical and genetic information
2) Brain-gene-disease ontology and simulation system
3) Cancer diagnosis and prognosis on a large scale of the whole human genome of multiple types of cancer
Future directions for intelligent decision support systems using methods of computational intelligence are outlined.
Keywords: Decision support systems; Ontology; Neuro-fuzzy inference systems; Neuroinformatics, Bionformatics, Brain-gene ontology, Gene expression data.
References
[1] N.Kasabov (2007) Evolving Connectionist Systems: The Knowledge Engineering Approach, Springer, London (www.springer.de)
[2] L.Benuskova and N.Kasabov (2007) Computational Neurogenetic Modelling, Springer, New York
[3] Q. Song and N. Kasabov, TNFI: A Neuro-Fuzzy Inference Method for Transductive Reasoning, IEEE Transactions on Fuzzy Systems, December, vol.13, issue 6, 2005, 799-808.
[4] Gottgtroy P., Kasabov N., Macdonell S., Evolving Ontologies for Intelligent Decision Support, Elsevier, Fuzzy Logic And The Semantic Web, Chapter 21, pp 415-439, 2006
[5] Kasabov, N., Global, local and personalised modelling and profile discovery in Bioinformatics: An integrated approach, Pattern Recognition Letters, Vol. 28, Issue 6, April 2007, 673-685
[6] Song, Q. and Kasabov, N. TWNFI- a transductive neuro-fuzzy inference system with weighted data normalisation for personalised modelling, Neural Networks, Vol.19, Issue 10, Dec. 2006, pp. 1591-1596
[7] Kasabov, N. Adaptation and Interaction in Dynamical Systems: Modelling and Rule Discovery Through Evolving Connectionist Systems, Applied Soft Computing, 2006, Volume 6, Issue 3, pages 307-322.
[8] Q. Song, N. Kasabov, T. Ma, M. Marshall, Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: a case study on renal function evaluation, Artificial Intelligence in Medicine, February, 2006
[9] N. Kasabov, L. Benuskova L and Wysoski SG (2005) Computational neurogenetic modeling: integration of spiking neural networks, gene networks, and signal processing techniques. In: ICANN 2005, LNCS 3697, W. Duch et al (Eds), Springer-Verlag, Berlin Heidelberg, pp. 509-514.
Biography
Professor Nikola Kasabov is the Founding Director and the Chief Scientist of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland (www.kedri.info/). He holds a Chair of Knowledge Engineering at the School of Computing and Mathematical Sciences at Auckland University of Technology. He is a Fellow of the Royal Society of New Zealand, Fellow of the New Zealand Computer Society and a Senior Member of IEEE. He is a Vice President the International Neural Network Society (INNS), a Past president of the Asia Pacific Neural Network Assembly (APNNA), and serves on several technical committees of the IEEE Computational Intelligence Society. Kasabov is on the editorial boards of several international journals, that include IEEE Transactions of NN, IEEE Tr of Industrial Informatics, Information Science, J. Theoretical and Computational Nanoscience. He chaired the series of ANNES conferences (1993-2001) and is the chair of the NCEI conference series (2002, 2003, 04, 06, -). Kasabov holds MSc and PhD from the Technical University of Sofia. His main research interests are in the areas of intelligent information systems, soft computing, neuro-computing, bioinformatics, brain study, speech and image processing, novel methods for data mining and knowledge discovery. He has published more than 400 publications that include 15 books, 120 journal papers, 60 book chapters, 32 patents and numerous conference papers. He has extensive academic experience at various academic and research organisations: University of Otago, New Zealand; University of Essex, UK; University of Trento, Italy; Technical University of Sofia, Bulgaria; University of California at Berkeley; RIKEN and KIT, Japan; T.University Kaiserslautern, Germany, and others. More information of Prof. Kasabov can be found on the KEDRI web site: http://www.kedri.info.