Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A new classification model with simple decision rule for discovering optimal feature gene pairs
Computers in Biology and Medicine
Direct integration of microarrays for selecting informative genes and phenotype classification
Information Sciences: an International Journal
Cancer classification by gradient LDA technique using microarray gene expression data
Data & Knowledge Engineering
Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods
Journal of Biomedical Informatics
A novel ensemble of classifiers for microarray data classification
Applied Soft Computing
Ensemble Neural Networks with Novel Gene-Subsets for Multiclass Cancer Classification
Neural Information Processing
Novel Extension of k - TSP Algorithm for Microarray Classification
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Weighted Top Score Pair Method for Gene Selection and Classification
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Simple Bayesian binary framework for discovering significant genes and classifying cancer diagnosis
Computational Statistics & Data Analysis
Cancer classification using microarray and layered architecture genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A Supervised Learning Technique and Its Applications to Computational Biology
Computational Intelligence Methods for Bioinformatics and Biostatistics
International Journal of Bioinformatics Research and Applications
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Learning Nondeterministic Classifiers
The Journal of Machine Learning Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A novel cancer classifier based on differentially expressed gene network
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Robust relief-feature weighting, margin maximization, and fuzzy optimization
IEEE Transactions on Fuzzy Systems
A novel hybrid feature selection method for microarray data analysis
Applied Soft Computing
1-vs-others rough decision forest
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
TC-VGC: A Tumor Classification System using Variations in Genes' Correlation
Computer Methods and Programs in Biomedicine
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Global top-scoring pair decision tree for gene expression data analysis
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
An ensemble of SVM classifiers based on gene pairs
Computers in Biology and Medicine
Journal of Biomedical Informatics
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Computers in Biology and Medicine
Hi-index | 3.85 |
Motivation: Various studies have shown that cancer tissue samples can be successfully detected and classified by their gene expression patterns using machine learning approaches. One of the challenges in applying these techniques for classifying gene expression data is to extract accurate, readily interpretable rules providing biological insight as to how classification is performed. Current methods generate classifiers that are accurate but difficult to interpret. This is the trade-off between credibility and comprehensibility of the classifiers. Here, we introduce a new classifier in order to address these problems. It is referred to as k-TSP (k--Top Scoring Pairs) and is based on the concept of 'relative expression reversals'. This method generates simple and accurate decision rules that only involve a small number of gene-to-gene expression comparisons, thereby facilitating follow-up studies. Results: In this study, we have compared our approach to other machine learning techniques for class prediction in 19 binary and multi-class gene expression datasets involving human cancers. The k-TSP classifier performs as efficiently as Prediction Analysis of Microarray and support vector machine, and outperforms other learning methods (decision trees, k-nearest neighbour and naïve Bayes). Our approach is easy to interpret as the classifier involves only a small number of informative genes. For these reasons, we consider the k-TSP method to be a useful tool for cancer classification from microarray gene expression data. Availability: The software and datasets are available at http://www.ccbm.jhu.edu Contact: actan@jhu.edu