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A Metastatic Cancer Expression Generator (MetGen): A Generative Contrastive Learning Framework for Metastatic Cancer Generation

Cancers(2024)10.3390/cancers16091653Source: DataRank Database

A Metastatic Cancer Expression Generator (MetGen): A Generative Contrastive Learning Framework for Metastatic Cancer Generation is a research paper published in Cancers (2024). On theSindex it has a DataRank of 0. It has been cited 2 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access2 citations

Cite:

Linked data & code

doi

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 (15 grants)Impact (FWCI 0.32)OA: goldTopics (20)IDs (PubMed, PMC, OpenAlex)Annotations (1)SDGs (2)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Yu‐Chiao Chiu ORCID,Yidong Chen ORCID,Yufei Huang ORCID,Zhentao Liu ORCID

Live enrichment

PubMed:38730604 PMC:PMC11083328 OpenAlex:https://openalex.org/W4395464949

Cancer Prevention and Research Institute of Texas

Grant: RP160732

Cancer Prevention and Research Institute of Texas

Grant: RP190346

Cancer Prevention and Research Institute of Texas

Grant: K99CA248944

Cancer Prevention and Research Institute of Texas

Grant: 3R00CA248944-04S1

Enhancing AI-readiness of multi-omics data for cancer pharmacogenomics

Cancer Prevention and Research Institute of Texas

Grant: R03OD036494

Cancer Prevention and Research Institute of Texas

Grant: U01CA279618

Cancer Prevention and Research Institute of Texas

Grant: R00CA248944

Cancer Prevention and Research Institute of Texas

Grant: P30CA047904

Cancer Prevention and Research Institute of Texas

Grant: P30DK120531

NIH HHS

Grant: S10 OD028483

National Institutes of Health

Grant: 3U01CA279618-02S1

m6A-suite: an informatics pipeline and resource for elucidating roles of m6A epitranscriptome in cancer

National Institutes of Health

Grant: 1R03OD036494-01

In silico screening for immune surveillance adaptation in cancer using Common Fund data resources

National Institutes of Health

Grant: 3P30CA047904-07S1

CANCER CENTER SUPPORT GRANT

National Institutes of Health

Grant: 1S10OD028483-01A1

High-Throughput Computing for Genomics and Bioinformatics Research

National Institutes of Health

Grant: 1P30DK120531-01

Pittsburgh Liver Research Center

FWCI

0.32

Citation Percentile

0.5%

Citation Trend

2025

goldLicense: cc-by

journalhttps://www.mdpi.com/2072-6694/16/9/1653/pdf?version=1714039270

publisherhttps://www.mdpi.com/2072-6694/16/9/1653/pdf?version=1714039270

publisherhttps://www.mdpi.com/2072-6694/16/9/1653/pdf

journalhttps://doi.org/10.3390/cancers16091653

repositoryhttps://pubmed.ncbi.nlm.nih.gov/38730604

Fields of Study

Protein Degradation and Inhibitors14-3-3 protein interactionsUbiquitin and proteasome pathways03 medical and health sciences0302 clinical medicine

Keywords

CancerGenerator (circuit theory)Metastatic tumorMedicineComputer scienceCancer researchMetastasisInternal medicineTumor MicroenvironmentMetastatic CancerDeep LearningContrastive LearningBiology and BiotechnologyBiology and Life SciencesArticle

Sustainable Development Goals

SDG 3: 3. Good healthSDG 0: Good health and well-being