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Interpretable models for high-risk neuroblastoma stratification with multi-cohort copy number profiles
Zhenqiu Liu
, Menglu Liang
, Christa N. Grant
,
Vladimir S. Spiegelman
,
Hong Gang Wang
Department of Public Health Sciences
Division of Biostatistics and Bioinformatics
Penn State Cancer Institute
Cancer Institute, Next-Generation Therapies
Department of Surgery
Division of Pediatric Surgery
Department of Pediatrics
Division of Hematology and Oncology
Penn State Neuroscience Institute
Department of Molecular and Precision Medicine
Department of Cell and Biological Systems
Research output
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Contribution to journal
›
Article
›
peer-review
1
Scopus citations
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Keyphrases
Model Interpretation
100%
Copy number Profiling
100%
High-risk Neuroblastoma
100%
MYCN Amplification
37%
Hazard Ratio
25%
Neuroblastoma
25%
Infant Patients
12%
Experimental Therapeutics
12%
Potential Risk Factors
12%
Risk Stratification
12%
Worse Prognosis
12%
Event-free Survival
12%
Better Prognosis
12%
Distinct Subtypes
12%
Four-factor Model
12%
Incremental Risk
12%
Molecular Factors
12%
Psychology
Hazard Ratio
100%
Clinical Implication
50%
Learning Model
50%
Medicine and Dentistry
Ganglioneuroblastoma
100%
Hazard Ratio
20%
Risk Stratification
10%
Event Free Survival
10%
Experimental Therapy
10%
Neuroscience
Neuroblastoma
100%
Immunology and Microbiology
Event Free Survival
100%
Biochemistry, Genetics and Molecular Biology
Event Free Survival
100%