An Introduction to Variational Methods for Graphical Models
Machine Learning
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The Journal of Machine Learning Research
Robust DNA microarray image analysis
Machine Vision and Applications
The Latent Process Decomposition of cDNA Microarray Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Nonextensive Information Theoretic Kernels on Measures
The Journal of Machine Learning Research
Bayesian Multi-topic Microarray Analysis with Hyperparameter Reestimation
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Expression microarray classification using topic models
Proceedings of the 2010 ACM Symposium on Applied Computing
Biclustering of Expression Microarray Data with Topic Models
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Brain morphometry by probabilistic latent semantic analysis
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Biclustering of expression microarray data using affinity propagation
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
A comparison on score spaces for expression microarray data classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Investigating Topic Models' Capabilities in Expression Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Transfer learning using a nonparametric sparse topic model
Neurocomputing
Exploiting geometry in counting grids
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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Topic models have recently shown to be really useful tools for the analysis of microarray experiments. In particular they have been successfully applied to gene clustering and, very recently, also to samples classification. In this latter case, nevertheless, the basic assumption of functional independence between genes is limiting, since many other a priori information about genes' interactions may be available (co-regulation, spatial proximity or other a priori knowledge). In this paper a novel topic model is proposed, which enriches and extends the Latent Dirichlet Allocation (LDA) model by integrating such dependencies, encoded in a categorization of genes. The proposed topic model is used to derive a highly informative and discriminant representation for microarray experiments. Its usefulness, in comparison with standard topic models, has been demonstrated in two different classification tests.