Unsupervised Stability-Based Ensembles to Discover Reliable Structures in Complex Bio-molecular Data
Computational Intelligence Methods for Bioinformatics and Biostatistics
Discovering significant structures in clustered bio-molecular data through the bernstein inequality
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
Stability-based model selection for high throughput genomic data: an algorithmic paradigm
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Summary: The R package mosclust (model order selection for clustering problems) implements algorithms based on the concept of stability for discovering significant structures in bio-molecular data. The software library provides stability indices obtained through different data perturbations methods (resampling, random projections, noise injection), as well as statistical tests to assess the significance of multi-level structures singled out from the data. Availability: http://homes.dsi.unimi.it/~valenti/SW/mosclust/download/mosclust_1.0.tar.gz Contact: valentini@dsi.unimi.it Supplementary information: http://homes.dsi.unimi.it/~valenti/SW/mosclust