Comparing the performance of biomedical clustering methods.

PubWeight™: 0.95‹?› | Rank: Top 15%

🔗 View Article (PMID 26389570)

Published in Nat Methods on September 21, 2015

Authors

Christian Wiwie1, Jan Baumbach1,2,3, Richard Röttger1

Author Affiliations

1: Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
2: Computational Systems Biology, Max Planck Institute for Informatics, Saarbrücken, Germany.
3: Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.

Articles cited by this

Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell (2010) 39.09

Clustering by passing messages between data points. Science (2007) 15.45

An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics (2003) 15.42

The ASTRAL compendium for protein structure and sequence analysis. Nucleic Acids Res (2000) 11.38

A cluster separation measure. IEEE Trans Pattern Anal Mach Intell (1979) 6.86

Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res (2008) 6.63

MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison. Protein Sci (2002) 5.47

Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics (2006) 4.85

Developments in automatic text retrieval. Science (1991) 4.66

The MIPS mammalian protein-protein interaction database. Bioinformatics (2004) 4.66

Computational cluster validation in post-genomic data analysis. Bioinformatics (2005) 4.46

Protein complex prediction via cost-based clustering. Bioinformatics (2004) 4.30

Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods (2012) 3.69

How does gene expression clustering work? Nat Biotechnol (2005) 3.61

Evaluation and comparison of gene clustering methods in microarray analysis. Bioinformatics (2006) 3.16

Machine learning. Clustering by fast search and find of density peaks. Science (2014) 2.28

A roadmap of clustering algorithms: finding a match for a biomedical application. Brief Bioinform (2009) 1.78

Large scale clustering of protein sequences with FORCE -A layout based heuristic for weighted cluster editing. BMC Bioinformatics (2007) 1.71

Techniques for clustering gene expression data. Comput Biol Med (2007) 1.67

Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data. Brief Bioinform (2005) 1.64

A gold standard set of mechanistically diverse enzyme superfamilies. Genome Biol (2006) 1.52

FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformatics (2007) 1.41

Comprehensive cluster analysis with Transitivity Clustering. Nat Protoc (2011) 1.28

Clustering methods for microarray gene expression data. OMICS (2006) 1.28

Partitioning biological data with transitivity clustering. Nat Methods (2010) 1.23

Clustering algorithms in biomedical research: a review. IEEE Rev Biomed Eng (2010) 1.12

Density parameter estimation for finding clusters of homologous proteins--tracing actinobacterial pathogenicity lifestyles. Bioinformatics (2012) 0.84