Interpretability Of Clusters
Interpretability of Clusters is the extent to which the biological or functional meaning underlying cell subpopulations identified by clustering algorithms in single-cell data can be clearly understood and biologically validated by researchers.
50
Publications
4.1K
Citations
179
Authors
94
Institutions
Publications per year
2017–2026
25
Parents
50
Christian Hennig · Pattern Recognition Letters · 2015 · 269 citations · Full text
Humanities, Philosophy, Epistemology +10
Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
Tian Tian, Jie Zhang, Xiang Lin et al. · Nature Communications · 2021 · 148 citations · Full text
Interpretable clustering: an optimization approach
Dimitris Bertsimas, Agni Orfanoudaki, Holly Wiberg · Machine Learning · 2020 · 117 citations · Full text
Computing, Data Science, Data Mining +12
1–5 of 50
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Massachusetts Institute of Technology, United States
2 publications · 138 citations
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1–5 of 179
94

Centre National de la Recherche Scientifique
Paris, France
3 h-index
5 publications
210 citations
University of Bergen
Bergen, Norway
3 h-index
9 publications
170 citations

Northwestern Polytechnical University
Xi'an, China
2 h-index
8 publications
304 citations
New York University
New York, United States
2 h-index
4 publications
178 citations

TU Wien
Vienna, Austria
1 h-index
1 publications
15 citations

University of Groningen
Groningen, The Netherlands
1 h-index
3 publications
60 citations
Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier
Montpellier, France
1 h-index
1 publications
24 citations
Duke University Hospital
Durham, United States
1 h-index
4 publications
440 citations
1–8 of 94
Repository · 3 publications · 53 citations
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2 publications · 83 citations
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2 publications · 82 citations
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2 publications · 29 citations
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2 publications · 41 citations
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