ArticleNature methods2015
Comparing the performance of biomedical clustering methods.
Article in Nature methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.
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Who cites it
79 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Unsupervised Clustering Methods to Alzheimer's Disease.Frontiers in computational neuroscience · 2019Pooled it
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- A formal explanation space for the simultaneous clustering of neurologic diseases based on their signs and symptoms.BMC medical informatics and decision making · 2025Article
- DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma.BioData mining · 2025Article
- Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives.Current heart failure reports · 2024Review
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- Distributed feature representations of natural stimuli across parallel retinal pathways.Nature communications · 2024Article
- Crosstalk between chromatin and Shavenbaby defines transcriptional output along the Drosophila intestinal stem cell lineage.iScience · 2024Article
- AAclust:Bioinformatics advances · 2024Article
- Broad functional profiling of fission yeast proteins using phenomics and machine learning.eLife · 2023Article
- MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.Nature communications · 2023Article
- HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.Statistica Sinica · 2023Article
- Estimating the allocation of land to business.PloS one · 2023Article
- Hierarchical cancer heterogeneity analysis based on histopathological imaging features.Biometrics · 2022Article
- Echocardiographic phenotypes of Chinese patients with type 2 diabetes may indicate early diabetic myocardial disease.ESC heart failure · 2022Article
- Challenges in translational machine learning.Human genetics · 2022Review
- MoSBi: Automated signature mining for molecular stratification and subtyping.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Computational analyses of mechanism of action (MoA): data, methods and integration.RSC chemical biology · 2022Review
- Global changes in electricity consumption during COVID-19.iScience · 2022Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying groups of similar objects is a popular first step in biomedical data analysis, but it is error-prone and impossible to perform manually. Many computational methods have been developed to tackle this problem. Here we assessed 13 well-known methods using 24 data sets ranging from gene expression to protein domains. Performance was judged on the basis of 13 common cluster validity indices. We developed a clustering analysis platform, ClustEval (http://clusteval.mpi-inf.mpg.de), to promote streamlined evaluation, comparison and reproducibility of clustering results in the future. This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. We observed that there was no universal best performer, but on the basis of this wide-ranging comparison we were able to develop a short guideline for biomedical clustering tasks. ClustEval allows biomedical researchers to pick the appropriate tool for their data type and allows method developers to compare their tool to the state of the art.
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