Knowledge-based artificial neural networks
Artificial Intelligence
Structural learning with forgetting
Neural Networks
Knowledge-based neurocomputing
Knowledge-based neurocomputing
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Evolutionary fuzzy cluster analysis with Bayesian validation of gene expression profiles
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary computation in bioinformatics
Natural computing methods in bioinformatics: A survey
Information Fusion
Applied Soft Computing
Identification of critical genes in microarray experiments by a Neuro-Fuzzy approach
Computational Biology and Chemistry
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Neural networks and other machine learning methods in cancer research
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A survey on use of soft computing methods in medicine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Expert Systems with Applications: An International Journal
A bounded version of online boosting on open-ended data streams
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Computational intelligence in bioinformatics
Transactions on Rough Sets III
Hi-index | 0.00 |
Microarray techniques have made it possible to observe the expression of thousands of genes simultaneously. They have recently been applied to study gene expression patterns in tissue samples. This may lead to highly desirable improvements in the diagnosis and treatment of human diseases. Statistical and machine learning methods have recently been used to classify cancer tissue based on gene expression data. Although some of these methods have achieved a high degree of accuracy, they generally lack transparency in their classification process. This, however, is crucial for the application in the medical field. In order to overcome this obstacle, we used knowledge-based neurocomputing (KBN), since KBN seeks to gain knowledge that is comprehensible to humans. In particular, we applied evolving fuzzy neural networks (EFuNNs) to classify cancer tissue, which is illustrated on the case studies of leukaemia and colon cancer. EFuNNs belong to the evolving connectionist system paradigm (ECOS) that has been recently introduced. They are well suited for adaptive learning and knowledge discovery. Fuzzy logic rules can be extracted from the trained networks and offer knowledge about the classification process in an easily accessible form. These rules point to genes that are strongly associated with specific types of cancer and may be used for the development of new tests and treatment discoveries.