:: DESCRIPTION
PClust is a scalable parallel software for detecting dense subgraphs.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
C. Wu, A. Kalyanaraman.
An efficient parallel approach for identifying protein families in large-scale metagenomic data sets.
Proc. ACM/IEEE Supercomputing Conference (SC’08), Austin, TX, November 15-21. pp. 1-10. 2008
:: DESCRIPTION
AdaPatch searches for dense and spatially distinct clusters of sites under positive selection on the surface of proteins.
::DEVELOPER
Algorithmic Bioinformatics, Heinrich-Heine-Universität Düsseldorf
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Christina Tusche, Lars Steinbrück and Alice C. McHardy
Detecting Patches of Protein Sites of Influenza A Viruses under Positive Selection
Mol Biol Evol (2012) 29 (8): 2063-2071.
:: DESCRIPTION
fineSTRUCTURE is a fast and powerful algorithm for identifying population structure using dense sequencing data. By using the output of ChromoPainter as a (nearly) sufficient summary statistic, it is able to perform model-based Bayesian clustering on large datasets, including full resequencing data, and can handle up to 1000s of individuals.
::DEVELOPER
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Lawson, Hellenthal, Myers, and Falush (2012),
“Inference of population structure using dense haplotype data“,
PLoS Genetics, 8 (e1002453).
:: DESCRIPTION
SMK (SupMaxK , a family of anti-monotonic measures) aims at the efficient discovery of discriminative patterns from biological data with high density and high dimensionality (e.g. Gene Expression data, and SNP data), and especially for the discovery of those patterns with relatively low-support but high discriminative power (e.g. odds ratio, information gain, p-value etc), which complements existing discriminative pattern mining algorithms.
::DEVELOPER
Data mining for biomedical informatics at the UMN
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Gang Fang, Gaurav Pandey, Wen Wang, Manish Gupta, Michael Steinbach and Vipin Kumar
Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data
IEEE Transaction on Knowledge and Data Engineering (TKDE).2012 (vol. 24 no. 2)pp. 279-294
