Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Oncogene-like addiction to aneuploidy in human cancers

Science(2023)10.1126/science.adg4521Source: DataRank Database

Oncogene-like addiction to aneuploidy in human cancers is a research paper published in Science (2023). On theSindex it has a DataRank of 0. It has been cited 162 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access162 citations

Download PDF

Cite:

Linked data & code

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (12 grants)Impact (FWCI 22.75)OA: bronzeTopics (46)Linked Datasets (16)DataCite Reuse (16)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (25)

Asad A. Lakhani ORCID,Sarah L. Thompson ORCID,Christine M. Scaduto ORCID,Leanne Brown ORCID,Ryan A. Hagenson ORCID

Live enrichment

PubMed:37410869 PMC:PMC10753973 OpenAlex:https://openalex.org/W4383313684

NCI NIH HHS

Grant: R01 CA276666

NCI NIH HHS

Grant: R01 CA237652

NIGMS NIH HHS

Grant: R01 GM137031

NCI NIH HHS

Grant: P30 CA045508

NCI NIH HHS

Grant: P30 CA016359

National Institutes of Health

Grant: 2P30CA016359-15

COMPREHENSIVE CANCER CENTER

National Institutes of Health

Grant: 3P30CA045508-08S1

CSHL CANCER CENTER SUPPORT GRANT

National Institutes of Health

Grant: 5R01CA237652-06

Discovering the mechanisms of-action-mistargeted anti-cancer agents

National Institutes of Health

Grant: 5R01CA276666-03

Genomic and functional approaches to characterize Chr1q gains in cancer

National Institutes of Health

Grant: 5R01GM137031-04

Understanding proteome remodeling in aneuploidy

Wellcome Trust

FWCI

22.75

Citation Percentile

1.0%

Citation Trend

2022

2023

2024

2025

2026

bronze

journalhttps://www.science.org/history/13c1dfb6-857c-4222-8fb8-ebee1b9a49f9/science.adg4521.v1.pdf

publisherhttps://www.science.org/history/13c1dfb6-857c-4222-8fb8-ebee1b9a49f9/science.adg4521.v1.pdf

publisherhttps://www.science.org/doi/pdf/10.1126/science.adg4521

journalhttps://doi.org/10.1126/science.adg4521

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37410869

Fields of Study

Microtubule and mitosis dynamicsCancer Genomics and DiagnosticsPancreatic and Hepatic Oncology Research03 medical and health sciences0302 clinical medicineHumansCell Cycle ProteinsMutationNeoplasmsOncogenesProto-Oncogene ProteinsTrisomyGene EditingTumor Suppressor Protein p53Carcinogenesis

MeSH Terms

Gene EditingHumansMutationNeoplasmsOncogenesProto-Oncogene ProteinsTrisomyTumor Suppressor Protein p53Cell Cycle ProteinsCarcinogenesis

Keywords

AneuploidyBiologyChromosomeTrisomyCRISPRCancer researchCancerOncogeneGeneticsPloidyGeneCell cycleGene EditingCarcinogenesisNeoplasmsProto-Oncogene ProteinsMutationHumansCell Cycle ProteinsOncogenesTumor Suppressor Protein p53

Sustainable Development Goals

SDG 0: Good health and well-being

Additional file 1 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.25608725.v1figshare

Additional file 1 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.25608725figshare

Additional file 8 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707017.v1figshare

Additional file 8 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707017figshare

Additional file 6 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707011.v1figshare

Additional file 4 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707005figshare

Additional file 5 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707008figshare

Additional file 7 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707014.v1figshare

Additional file 7 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707014figshare

Additional file 4 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707005.v1figshare

Additional file 5 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707008.v1figshare

Additional file 6 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707011figshare

Additional file 3 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707002.v1figshare

Additional file 2 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.25608861.v1figshare

Additional file 2 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.25608861figshare

Additional file 3 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

10.6084/m9.figshare.26707002figshare

Related Papers (10)

KaryoCreate: A CRISPR-based technology to study chromosome-specific aneuploidy by targeting human centromeres

Cell(2023)

co-cited

10.1016/j.cell.2023.03.029

The Clonal Evolution of Tumor Cell Populations

N/A

1.3DataRank · unranked

Science(1976)

co-citedsame journal

10.1126/science.959840

Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

N/A

8.1DataRank · unranked

Nature Genetics(2020)

co-cited

10.1038/s41588-019-0576-7

Down syndrome

Nature Reviews Disease Primers(2020)

co-cited

10.1038/s41572-019-0143-7

Chromothripsis drives the evolution of gene amplification in cancer

N/A

0.926DataRank · unranked

Nature(2020)

co-cited

10.1038/s41586-020-03064-z

Pervasive chromosomal instability and karyotype order in tumour evolution

N/A

7.1DataRank · unranked

Nature(2020)

co-cited

10.1038/s41586-020-2698-6

Genetic and epigenetic intratumor heterogeneity impacts prognosis of lung adenocarcinoma

N/A

0.738DataRank · unranked

Nature Communications(2020)

co-cited

10.1038/s41467-020-16295-5

Single-Chromosomal Gains Can Function as Metastasis Suppressors and Promoters in Colon Cancer

Developmental Cell(2020)

co-cited

10.1016/j.devcel.2020.01.034

A CINful way to overcome addiction: how chromosomal instability enables cancer to overcome its oncogene addiction

EMBO Molecular Medicine(2020)

co-cited

10.15252/emmm.202012017

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

N/A

7.3DataRank · unranked

Cell(2021)

co-cited

10.1016/j.cell.2021.03.009