Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Structural Risk Minimisation based gene expression profiling analysis
International Journal of Bioinformatics Research and Applications
IEEE Transactions on Information Technology in Biomedicine
Sparse Support Vector Machines with L_{p} Penalty for Biomarker Identification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SELDI-TOF-MS pattern analysis for cancer detection as a base for diagnostic software
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Combining functional networks and sensitivity analysis as wrapper method for feature selection
Expert Systems with Applications: An International Journal
Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: The classification of high-dimensional data is always a challenge to statistical machine learning. We propose a novel method named shallow feature selection that assigns each feature a probability of being selected based on the structure of training data itself. Independent of particular classifiers, the high dimension of biodata can be fleetly reduced to an applicable case for consequential processing. Moreover, to improve both efficiency and performance of classification, these prior probabilities are further used to specify the distributions of top-level hyperparameters in hierarchical models of Bayesian neural network (BNN), as well as the parameters in Gaussian process models. Results: Three BNN approaches were derived and then applied to identify ovarian cancer from NCI's high-resolution mass spectrometry data, which yielded an excellent performance in 1000 independent k-fold cross validations (k = 2,...,10). For instance, indices of average sensitivity and specificity of 98.56 and 98.42%, respectively, were achieved in the 2-fold cross validations. Furthermore, only one control and one cancer were misclassified in the leave-one-out cross validation. Some other popular classifiers were also tested for comparison. Availability: The programs implemented in MatLab, R and Neal's fbm.2004-11-10. Contact: xwchen@ku.edu