Supervised Tumor Classification
Supervised Tumor Classification is a machine learning approach in cancer genomics that belongs to the genus of classification methods and is differentiated by its reliance on labeled molecular profiles to train predictive models for assigning tumor samples to predefined biological or clinical categories.
117
Publications
12.8K
Citations
612
Authors
312
Institutions
Publications per year
2017–2026
63
117
Gene Selection for Cancer Classification using Support Vector Machines
Isabelle Guyon, Jason Weston, S. Barnhill et al. · Machine Learning · 2002 · 9.8K citations · Full text
Molecular classification of human carcinomas by use of gene expression signatures.
Andrew I. Su, John Welsh, Lisa M. Sapinoso et al. · PubMed · 2001 · 692 citations
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German Cancer Research Center
Heidelberg, Germany
4 h-index
9 publications
6.2K citations
Heidelberg University
Heidelberg, Germany
4 h-index
9 publications
6.2K citations
Inserm
Paris, France
3 h-index
5 publications
146 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
5 publications
146 citations
Charité - Universitätsmedizin Berlin
Berlin, Germany
3 h-index
22 publications
1.2K citations
University Hospital of Basel
Basel, Switzerland
2 h-index
6 publications
285 citations
Yale University
New Haven, United States
2 h-index
15 publications
1.8K citations
Indian Institute of Technology BHU
Varanasi, India
2 h-index
3 publications
77 citations
1–8 of 312
7 publications · 821 citations
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4 publications · 1.1K citations
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AIM · 4 publications · 235 citations
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4 publications · 298 citations
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3 publications · 402 citations
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