Machine Learning
Elements of artificial neural networks
Elements of artificial neural networks
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Classification of microarray data with factor mixture models
Bioinformatics
Neural Networks in a Softcomputing Framework
Neural Networks in a Softcomputing Framework
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Extracting gene regulation information for cancer classification
Pattern Recognition
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Selecting differentially expressed genes using minimum probability of classification error
Journal of Biomedical Informatics
Disease-specific genomic analysis
Bioinformatics
Inferring differentiation pathways from gene expression
Bioinformatics
Brief Communication: Finding rule groups to classify high dimensional gene expression datasets
Computational Biology and Chemistry
A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Gene selection and PSO-BP classifier encoding a prior information
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Journal of Biomedical Informatics
Disease-related gene expression analysis using an ensemble statistical test method
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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A number of different approaches based on high-throughput data have been developed for cancer classification. However, these methods often ignore the underlying correlation between the expression levels of different biomarkers which are related to cancer. From a biological viewpoint, the modeling of these abnormal associations between biomarkers will play an important role in cancer classification. In this paper, we propose an approach based on the concept of Biomarker Association Networks (BAN) for cancer classification. The BAN is modeled as a neural network, which can capture the associations between the biomarkers by minimizing an energy function. Based on the BAN, a new cancer classification approach is developed. We validate the proposed approach on four publicly available biomarker expression datasets. The derived Biomarker Association Networks are observed to be significantly different for different cancer classes, which help reveal the underlying deviant biomarker association patterns responsible for different cancer types. Extensive comparisons show the superior performance of the BAN-based classification approach over several conventional classification methods.