Machine learning modeling of genome-wide copy number alteration signatures reliably predicts IDH mutational status in adult diffuse glioma is a research paper published in Acta Neuropathologica Communications (2021). On theSindex it has a DataRank of 0. It has been cited 16 times.
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National Institute of Mental Health
Grant: K08CA245037
National Science Foundation
Grant: DGE-1762114
National Institutes of Health
Grant: 5K08CA245037-02
CHARACTERIZING AGGRESSIVE GLIOMA COPY NUMBER SUBTYPES
National Science Foundation
Grant: 1762114
Graduate Research Fellowship Program (GRFP)
Seattle Translational Tumor Research
FWCI
0.93
Citation Percentile
0.8%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 2 of Machine learning modeling of genome-wide copy number alteration signatures reliably predicts IDH mutational status in adult diffuse glioma
Additional file 2 of Machine learning modeling of genome-wide copy number alteration signatures reliably predicts IDH mutational status in adult diffuse glioma
Additional file 1 of Machine learning modeling of genome-wide copy number alteration signatures reliably predicts IDH mutational status in adult diffuse glioma
Additional file 1 of Machine learning modeling of genome-wide copy number alteration signatures reliably predicts IDH mutational status in adult diffuse glioma
Machine learning modeling of genome-wide copy number alteration signatures reliably predicts IDH mutational status in adult diffuse glioma
Machine learning modeling of genome-wide copy number alteration signatures reliably predicts IDH mutational status in adult diffuse glioma