Cell-free DNA ultra-low-pass whole genome sequencing to distinguish malignant peripheral nerve sheath tumor (MPNST) from its benign precursor lesion: A cross-sectional study is a research paper published in PLoS Medicine (2021). On theSindex it has a DataRank of 0.665. It has been cited 83 times.
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DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
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Base Score Contribution
0.665
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Institute of General Medical Sciences
Grant: 5T32GM007067
National Cancer Institute Center for Cancer Research Intramural Research Program
Grant: 1ZIABC011722-04
Genomic characterization and development of therapies for pediatric sarcoma
National Cancer Institute Center for Cancer Research Intramural Research Program
Grant: 1ZIABC010801-13
Therapy for NF1-Related Tumors and other Genetic Tumor Predisposition Syndromes
National Cancer Institute
Grant: 1K08CA238711
NIGMS NIH HHS
Grant: T32 GM007067
NIGMS NIH HHS
Grant: T32 GM007200
Intramural NIH HHS
Grant: ZIA BC010801
Intramural NIH HHS
Grant: ZIA BC011722
NCI NIH HHS
Grant: K08 CA238711
National Institutes of Health
Grant: 5T32GM007067-31
TRAINING PROGRAM IN CELLULAR AND MOLECULAR BIOLOGY
National Institutes of Health
Grant: 1K08CA238711-01
Cell-Free Nucleic Acids for Early Treatment Response Assessment of Hepatocellular Cancer
St. Louis Men’s Group Against Cancer
Cancer Research Young Investigator Award
Center for Cancer Research Flex Award
Children's Cancer Foundation
V Foundation for Cancer Research
Washington University Alvin J. Siteman Cancer Research Fund
Francis S. Collins Scholars Program in Neurofibromatosis Clinical and Translational Research
FWCI
7.46
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 1 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 2 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 2 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 3 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 3 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 4 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 4 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 6 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 5 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 5 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 6 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 7 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
Additional file 7 of Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)