Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Feature Selection Algorithm with Bayesian l 1 Regularization
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A survey of prediction models for breast cancer survivability
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
CliDaPa: A new approach to combining clinical data with DNA microarrays
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
Robust relief-feature weighting, margin maximization, and fuzzy optimization
IEEE Transactions on Fuzzy Systems
Journal of Biomedical Informatics
The inference of breast cancer metastasis through gene regulatory networks
Journal of Biomedical Informatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A minimax probabilistic approach to feature transformation for multi-class data
Applied Soft Computing
Artificial Intelligence in Medicine
wFDT: weighted fuzzy decision trees for prognosis of breast cancer survivability
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Stable Feature Selection with Minimal Independent Dominating Sets
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Robust predictive model for evaluating breast cancer survivability
Engineering Applications of Artificial Intelligence
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Motivation: Accurate prognosis of breast cancer can spare a significant number of breast cancer patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical disease recurrence. However, these studies all attempt to develop genetic marker-based prognostic systems to replace the existing clinical criteria, while ignoring the rich information contained in established clinical markers. Given the complexity of breast cancer prognosis, a more practical strategy would be to utilize both clinical and genetic marker information that may be complementary. Methods: A computational study is performed on publicly available microarray data, which has spawned a 70-gene prognostic signature. The recently proposed I-RELIEF algorithm is used to identify a hybrid signature through the combination of both genetic and clinical markers. A rigorous experimental protocol is used to estimate the prognostic performance of the hybrid signature and other prognostic approaches. Survival data analyses is performed to compare different prognostic approaches. Results: The hybrid signature performs significantly better than other methods, including the 70-gene signature, clinical makers alone and the St. Gallen consensus criterion. At the 90% sensitivity level, the hybrid signature achieves 67% specificity, as compared to 47% for the 70-gene signature and 48% for the clinical makers. The odds ratio of the hybrid signature for developing distant metastases within five years between the patients with a good prognosis signature and the patients with a bad prognosis is 21.0 (95% CI:6.5--68.3), far higher than either genetic or clinical markers alone. Availability: The breast cancer dataset is available at www.nature.com and Matlab codes are available upon request. Contact: sun@dsp.ufl.edu Supplementary information: Supplementary data are available at Bioinformatics online.