The nature of statistical learning theory
The nature of statistical learning theory
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Analyzing gene expression data in terms of gene sets
Bioinformatics
A novel signaling pathway impact analysis
Bioinformatics
Integrating Multiple-Platform Expression Data through Gene Set Features
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Computers in Biology and Medicine
Decision forest for classification of gene expression data
Computers in Biology and Medicine
Gene Set Cultural Algorithm: A Cultural Algorithm Approach to Reconstruct Networks from Gene Sets
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Analysis of gene expression data in terms of a priori-defined gene sets typically yields more compact and interpretable results than those produced by traditional methods that rely on individual genes. The set-level strategy can also be adopted in predictive classification tasks accomplished with machine learning algorithms. Here, sample features originally corresponding to genes are replaced by a much smaller number of features, each corresponding to a gene set and aggregating expressions of its members into a single real value. Classifiers learned from such transformed features promise better interpretability in that they derive class predictions from overall expressions of selected gene sets (e.g. corresponding to pathways) rather than expressions of specific genes. In a large collection of experiments we test how accurate such classifiers are compared to traditional classifiers based on genes. Furthermore, we translate some recently published gene set analysis techniques to the above proposed machine learning setting and assess their contributions to the classification accuracies.