Learning in graphical models
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Scalable Topic-Based Open Source Search Engine
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
A latent variable model for chemogenomic profiling
Bioinformatics
Introduction to Information Retrieval
Introduction to Information Retrieval
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
Journal of the ACM (JACM)
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Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret We bring in explicitly an indicator of which genes tie each cluster, changing the setup to biclustering Furthermore, we make the indicators hierarchical, resulting in a hierarchy of progressively more specific biclusters A non-parametric Bayesian formulation makes the model rigorous and yet flexible, and computations feasible The formulation additionally offers a natural information retrieval relevance measure that allows relating samples in a principled manner We show that the model outperforms other four biclustering procedures in a large miRNA data set We also demonstrate the model's added interpretability and information retrieval capability in a case study that highlights the potential and novel role of miR-224 in the association between melanoma and non-Hodgkin lymphoma Software is publicly available.