Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
A rank sum test method for informative gene discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Application of the GA/KNN method to SELDI proteomics data
Bioinformatics
Semi-supervised protein classification using cluster kernels
Bioinformatics
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE Transactions on Computers
Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
Artificial Intelligence in Medicine
Tumor clustering using nonnegative matrix factorization with gene selection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Computers in Biology and Medicine
Feature extraction from tumor gene expression profiles using DCT and DFT
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Semi-Supervised Learning
Mining of MicroRNA expression data—a rough set approach
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Dimensionality reduction for semi-supervised face recognition
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Artificial Intelligence in Medicine
Discriminant sparse neighborhood preserving embedding for face recognition
Pattern Recognition
Commentary: Breakthroughs in genomics data integration for predicting clinical outcome
Journal of Biomedical Informatics
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Objective: Both supervised methods and unsupervised methods have been widely used to solve the tumor classification problem based on gene expression profiles. This paper introduces a semi-supervised graph-based method for tumor classification. Feature extraction plays a key role in tumor classification based on gene expression profiles, and can greatly improve the performance of a classifier. In this paper we propose a novel multi-step dimensionality reduction method for extracting tumor-related features. Methods and materials: First the Wilcoxon rank-sum test is used for gene selection. Then gene ranking and discrete cosine transform are combined with principal component analysis for feature extraction. Finally, the performance is evaluated by semi-supervised learning algorithms. Results: To show the validity of the proposed method, we apply it to classify four tumor datasets involving various human normal and tumor tissue samples. The experimental results show that the proposed method is efficient and feasible. Compared with other methods, our method can achieve relatively higher prediction accuracy. Particularly, it is found that semi-supervised method is superior to support vector machines in classification performance. Conclusions: The proposed approach can effectively improve the performance of tumor classification based on gene expression profiles. This work is a meaningful attempt to explore and apply multi-step dimensionality reduction and semi-supervised learning methods in the field of tumor classification. Considering the high classification accuracy, there should be much room for the application of multi-step dimensionality reduction and semi-supervised learning methods to perform tumor classification.