Rough set algorithms in classification problem
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A novel approach to determine normal variation in gene expression data
ACM SIGKDD Explorations Newsletter
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Integrating Rough Sets with Neural Networks for Weighting Road Safety Performance Indicators
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Adapted variable precision rough set approach for EEG analysis
Artificial Intelligence in Medicine
Transactions on rough sets VII
Gene selection and cancer classification: a rough sets based approach
Transactions on rough sets XII
Accuracy evaluation of the system of type 1 diabetes prediction
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Computational intelligence in bioinformatics
Transactions on Rough Sets III
Hi-index | 0.00 |
Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capability opens a wide range of research opportunities in biology, but the technology generates a vast amount of data that cannot be handled manually. Computational analysis is thus a prerequisite for the success of this technology, and research and development of computational tools for microarray analysis are of great importance.One application of microarray technology is cancer studies where supervised learning may be used for predicting tumor subtypes and clinical parameters. We present a general Rough Set approach for classification of tumor samples analyzed with microarrays. This approach is tested on a data set of gastric tumors, and we develop classifiers for six clinical parameters.One major obstacle in training classifiers from microarray data is that the number of objects is much smaller that the number of attributes. We therefore introduce a feature selection method based on bootstrapping for selecting genes that discriminate significantly between the classes, and study the performance of this method.Moreover, the efficacy of several learning and discretization methods implemented in the ROSETTA system [18] is examined. Their performance is compared to that of linear and quadratic discrimination analysis. The classifiers are also biologically validated. One of the best classifiers is selected for each clinical parameter, and the connection between the genes used in these classifiers and the parameters are compared to the establish knowledge in the biomedical literature.