IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving the quadratic programming problem arising in support vector classification
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
On the Decomposition of Polychotomies into Dichotomies
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Data Complexity Analysis for Classifier Combination
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Bias-Variance Analysis and Ensembles of SVM
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Parallel Non Linear Dichotomizers
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Classification of DNA microarray data with Random Projection Ensembles of Polynomial SVMs
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Artificial Intelligence in Medicine
Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
Artificial Intelligence in Medicine
Logical analysis of diffuse large B-cell lymphomas
Artificial Intelligence in Medicine
Electromechanical equipment state forecasting based on genetic algorithm - support vector regression
Expert Systems with Applications: An International Journal
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Gene selection and classification of human lymphoma from microarray data
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
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The large amount of data generated by DNA microarrays was originally analysed using unsupervised methods, such as clustering or self-organizing maps. Recently supervised methods such as decision trees, dot-product support vector machines (SVM) and multi-layer perceptrons (MLP) have been applied in order to classify normal and tumoural tissues. We propose methods based on non-linear SVM with polynomial and Gaussian kernels, and output coding (OC) ensembles of learning machines to separate normal from malignant tissues, to classify different types of lymphoma and to analyse the role of sets of coordinately expressed genes in carcinogenic processes of lymphoid tissues. Using gene expression data from ''Lymphochip'', a specialised DNA microarray developed at Stanford University School of Medicine, we show that SVM can correctly separate normal from tumoural tissues, and OC ensembles can be successfully used to classify different types of lymphoma. Moreover, we identify a group of coordinately expressed genes related to the separation of two distinct subgroups inside diffuse large B-cell lymphoma (DLBCL), validating a previous Alizadeh's hypothesis about the existence of two distinct diseases inside DLBCL.