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

Default risk prediction and feature extraction using a penalized deep neural network

Statistics and Computing(2022)10.1007/s11222-022-10140-zSource: DataRank Database

Default risk prediction and feature extraction using a penalized deep neural network is a research paper published in Statistics and Computing (2022). On theSindex it has a DataRank of 0. It has been cited 4 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access4 citations

Download PDF

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 (3 grants)Impact (FWCI 1.11)OA: bronzeTopics (23)IDs (PubMed, PMC, OpenAlex)Annotations (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

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 (5)

Nan Qiao ORCID,Wenli Zhang ORCID,Yang Li ORCID,Shuangge Ma ORCID,Cunjie Lin ORCID

Live enrichment

PubMed:36124203 PMC:PMC9476445 OpenAlex:https://openalex.org/W4296121634

National Natural Science Foundation of China

Grant: 71771211

National Natural Science Foundation of China

Grant: 11701561

National Institutes of Health

Grant: 5R01CA204120-03

Novel methods for identifying genetic interactions in cancer prognosis

FWCI

1.11

Citation Percentile

0.8%

Citation Trend

2022

2024

2025

bronzeLicense: Springer Nature TDM

journalhttps://link.springer.com/content/pdf/10.1007/s11222-022-10140-z.pdf

publisherhttps://link.springer.com/content/pdf/10.1007/s11222-022-10140-z.pdf

publisherhttps://link.springer.com/article/10.1007/s11222-022-10140-z/fulltext.html

journalhttps://doi.org/10.1007/s11222-022-10140-z

repositoryhttps://pubmed.ncbi.nlm.nih.gov/36124203

Fields of Study

Financial Distress and Bankruptcy PredictionMachine Learning in HealthcareImbalanced Data Classification Techniques02 engineering and technology01 natural sciences0202 electrical engineering, electronic engineering, information engineering0101 mathematics

Keywords

Artificial intelligenceArtificial neural networkPattern recognition (psychology)Computer scienceLasso (programming language)Feature extractionDeep neural networksNeural networkSurvival analysisRisk PredictionLoan DataReliability and life testingArticleComputational methods for problems pertaining to statisticsApplications of statistics to economicsArtificial neural networks and deep learning

Default risk prediction and feature extraction using a penalized deep neural network

FAIR Checklist

Authors (5)

Funding (3 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)