Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Fuzzy rule induction and artificial immune systems in female breast cancer familiarity profiling
International Journal of Hybrid Intelligent Systems - Recent Advances in Intelligent Paradigms Fusion and Their Applications
Artificial Intelligence in Medicine
Computational Statistics & Data Analysis
An expert system to classify microarray gene expression data using gene selection by decision tree
Expert Systems with Applications: An International Journal
Formulating and testing hypotheses in functional genomics
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Feature cluster selection for high-throughput data analysis
International Journal of Data Mining and Bioinformatics
Feature Selection by Transfer Learning with Linear Regularized Models
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Ensemble gene selection by grouping for microarray data classification
Journal of Biomedical Informatics
A GMM-IG framework for selecting genes as expression panel biomarkers
Artificial Intelligence in Medicine
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Review Article: Stable feature selection for biomarker discovery
Computational Biology and Chemistry
Integration of gene signatures using biological knowledge
Artificial Intelligence in Medicine
Computational Biology and Chemistry
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Sparse and stable gene selection with consensus SVM-RFE
Pattern Recognition Letters
Stable Gene Selection from Microarray Data via Sample Weighting
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Two-Class SVM trees (2-SVMT) for biomarker data analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Finding disease similarity based on implicit semantic similarity
Journal of Biomedical Informatics
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computational Biology and Chemistry
Engineering Applications of Artificial Intelligence
Module-based breast cancer classification
International Journal of Data Mining and Bioinformatics
Algorithms for discovery of multiple Markov boundaries
The Journal of Machine Learning Research
Journal of Biomedical Informatics
PLS-based recursive feature elimination for high-dimensional small sample
Knowledge-Based Systems
Incorporation of gene exchangeabilities improves the reproducibility of gene set rankings
Computational Statistics & Data Analysis
Computers in Biology and Medicine
Hi-index | 3.84 |
Motivation: Predicting the metastatic potential of primary malignant tissues has direct bearing on the choice of therapy. Several microarray studies yielded gene sets whose expression profiles successfully predicted survival. Nevertheless, the overlap between these gene sets is almost zero. Such small overlaps were observed also in other complex diseases, and the variables that could account for the differences had evoked a wide interest. One of the main open questions in this context is whether the disparity can be attributed only to trivial reasons such as different technologies, different patients and different types of analyses. Results: To answer this question, we concentrated on a single breast cancer dataset, and analyzed it by a single method, the one which was used by van't Veer et al. to produce a set of outcome-predictive genes. We showed that, in fact, the resulting set of genes is not unique; it is strongly influenced by the subset of patients used for gene selection. Many equally predictive lists could have been produced from the same analysis. Three main properties of the data explain this sensitivity: (1) many genes are correlated with survival; (2) the differences between these correlations are small; (3) the correlations fluctuate strongly when measured over different subsets of patients. A possible biological explanation for these properties is discussed. Contact: eytan.domany@weizmann.ac.il Supplementary information: http://www.weizmann.ac.il/physics/complex/compphys/downloads/liate/