Ontology-Based Framework for Personalized Diagnosis and Prognosis of Cancer Based on Gene Expression Data

Authors:
Yingjie Hu;Nikola Kasabov
Affiliations:
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand
Venue:
Neural Information Processing
Year:
2008

Citing 5
Cited 0

Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines

Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
TWNFI - a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling

Neural Networks
Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach

Pattern Recognition Letters
Classification consistency analysis for bootstrapping gene selection

Neural Computing and Applications
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

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Abstract

This paper presents an ontology-based framework for personalized cancer decision support system based on gene expression data. This framework integrates the ontology and personalized cancer predictions using a variety of machine learning models. A case study is proposed for demonstrating the personalized cancer diagnosis and prognosis on two benchmark cancer gene data. Different methods based on global, local and personalized modeling, including Multi Linear Regression (MLR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Evolving Classifier Function (ECF), weighted distance weighted variables K-nearest neighbor method (WWKNN) and a transductive neuro-fuzzy inference system with weighted data normalization (TWNFI) are investigated. The development platform is general that can use multimodal information for personalized prediction and new knowledge creation within an evolving ontology framework.