Neural networks for pattern recognition
Neural networks for pattern recognition
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
VizCluster and its Application on Classifying Gene Expression Data
Distributed and Parallel Databases
Multidimensional Data Integration and Relationship Inference
IEEE Intelligent Systems
An integrated probabilistic model for functional prediction of proteins
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
On the potential of domain literature for clustering and Bayesian network learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis and visualization of gene expression data using self-organizing maps
Neural Networks - New developments in self-organizing maps
Prediction of Protein Function Using Protein-Protein Interaction Data
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Gene functional classification by semi-supervised learning from heterogeneous data
Proceedings of the 2003 ACM symposium on Applied computing
Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data
Neural Processing Letters
Machine learning in low-level microarray analysis
ACM SIGKDD Explorations Newsletter
A rank sum test method for informative gene discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
GE-CKO: A Method to Optimize Composite Kernels for Web Page Classification
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Semisupervised learning from different information sources
Knowledge and Information Systems
KDX: An Indexer for Support Vector Machines
IEEE Transactions on Knowledge and Data Engineering
Regulation probability method for gene selection
Pattern Recognition Letters
Protein classification using transductive learning on phylogenetic profiles
Proceedings of the 2006 ACM symposium on Applied computing
Nonstationary kernel combination
ICML '06 Proceedings of the 23rd international conference on Machine learning
Feature selection algorithm for mixed data with both nominal and continuous features
Pattern Recognition Letters
Machine learning methods for transcription data integration
IBM Journal of Research and Development - Systems biology
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Logistic support vector machines and their application to gene expression data
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications
Structural Risk Minimisation based gene expression profiling analysis
International Journal of Bioinformatics Research and Applications
Transductive learning with EM algorithm to classify proteins based on phylogenetic profiles
International Journal of Data Mining and Bioinformatics
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
International Journal of Bioinformatics Research and Applications
Fusion of feature selection methods for pairwise scoring SVM
Neurocomputing
Generative Kernels for Gene Function Prediction Through Probabilistic Tree Models of Evolution
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
Protein functional class prediction with a combined graph
Expert Systems with Applications: An International Journal
Modeling adaptive kernels from probabilistic phylogenetic trees
Artificial Intelligence in Medicine
Gene Clustering via Integrated Markov Models Combining Individual and Pairwise Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Comparison of feature selection techniques for viral DNA replication origin prediction
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Prediction of protein functions based on function-function correlation relations
Computers in Biology and Medicine
Cost-conscious multiple kernel learning
Pattern Recognition Letters
Audio-visual group recognition using diffusion maps
IEEE Transactions on Signal Processing
A greedy algorithm for gene selection based on SVM and correlation
International Journal of Bioinformatics Research and Applications
Regularizing multiple kernel learning using response surface methodology
Pattern Recognition
End-to-end quality of service seen by applications: A statistical learning approach
Computer Networks: The International Journal of Computer and Telecommunications Networking
Prediction of protein interaction with neural network-based feature association rule mining
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Multi-label correlated semi-supervised learning for protein function prediction
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Learning protein functions from bi-relational graph of proteins and function annotations
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
Automatic annotation of protein functional class from sparse and imbalanced data sets
VDMB'06 Proceedings of the First international conference on Data Mining and Bioinformatics
Adaptive neural network-based clustering of yeast protein: protein interactions
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
A bootstrapping method for learning from heterogeneous data
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
A Top-r Feature Selection Algorithm for Microarray Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Artificial Intelligence in Medicine
Discriminant analysis in pairwise kernel learning for SVM classification
International Journal of Bioinformatics Research and Applications
Localized algorithms for multiple kernel learning
Pattern Recognition
Simultaneous learning of localized multiple kernels and classifier with weighted regularization
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A local information-based feature-selection algorithm for data regression
Pattern Recognition
E2LSH based multiple kernel approach for object detection
Neurocomputing
A feature selection method using improved regularized linear discriminant analysis
Machine Vision and Applications
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In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene functional classifications from a heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence comparisons. We demonstrate the application of the support vector machine (SVM) learning algorithm to this functional inference task. Our results suggest the importance of exploiting prior information about the heterogeneity of the data. In particular, we propose an SVM kernel function that is explicitly heterogeneous. We also show how to use knowledge about heterogeneity to aid in feature selection.