The Strength of Weak Learnability
Machine Learning
Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Direct optimization of margins improves generalization in combined classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Deconvolving sequence variation in mixed DNA populations
Proceedings of the sixth annual international conference on Computational biology
Using functional annotation to improve clusterings of gene expression patterns
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
Joint classifier and feature optimization for cancer diagnosis using gene expression data
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Rule Induction for Classification of Gene Expression Array Data
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
A Bayesian Approach to Joint Feature Selection and Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust and Accurate Cancer Classification with Gene Expression Profiling
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Innovative computational methods for transcriptomic data analysis
Proceedings of the 2006 ACM symposium on Applied computing
Random subspace method for multivariate feature selection
Pattern Recognition Letters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A hybrid SVM/DDBHMM decision fusion modeling for robust continuous digital speech recognition
Pattern Recognition Letters
Ovarian cancer diagnosis with complementary learning fuzzy neural network
Artificial Intelligence in Medicine
A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Artificial Intelligence in Medicine
A method of tumor classification based on wavelet packet transforms and neighborhood rough set
Computers in Biology and Medicine
A support vector machine ensemble for cancer classification using gene expression data
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Efficient gene selection with rough sets from gene expression data
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Pattern discovery for large mixed-mode database
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Robust approach for estimating probabilities in Naïve-Bayes Classifier for gene expression data
Expert Systems with Applications: An International Journal
Gene feature extraction using T-test statistics and kernel partial least squares
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A novel visualization classifier and its applications
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Gene selection and classification of human lymphoma from microarray data
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Artificial Intelligence in Medicine
Improving Tumor Identification by Using Tumor Markers Classification Strategy
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also expected to significantly and in the development of efficient cancer diagnosis and classification platforms. In this work we examine two sets of gene expression data measured across sets of tumor and normal clinical samples One set consists of 2,000 genes, measured in 62 epithelial colon samples [1]. The second consists of ≈ 100,000 clones, measured in 32 ovarian samples (unpublished, extension of data set described in [26]).We examine the use of scoring methods, measuring separation of tumors from normals using individual gene expression levels. These are then coupled with high dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the two data sets. employing SVM [8], AdaBoost [13] and a novel clustering based classification technique. As tumor samples can differ from normal samples in their cell-type composition we also perform LOOCV experiments using appropriately modified sets of genes, attempting to eliminate the resulting bias.We demonstrate success rate of at least 90% in tumor vs normal classification, using sets of selected genes, with as well as without cellular contamination related members. These results are insensitive to the exact selection mechanism, over a certain range.