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DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

PLOS Computational Biology(2020)10.1371/journal.pcbi.1007616Source: DataRank Database

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning is a research paper published in PLOS Computational Biology (2020). On theSindex it has a DataRank of 2.3. It has been cited 88 times, with 89 citing works in its 1-hop citation network.

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2.3DataRank · unranked

Scored on demand from live citation data

Open Access88 citations · 89 citing works in its network

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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?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)OA: goldTopics (24)IDs (PubMed, PMC)Annotations (2)SDGs (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)

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DataRank Breakdown

Base Score 30%Citation Network 70%

Base Score Contribution

0.673

From this paper's citation signal

Citation Network Contribution

1.6

From 69 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 89 citers.

Cellotype–phenotype associations using ‘organoid villages’
Trends in Endocrinology and Metabolism20243 citations12 referencesContributes 0.116
An Optimal Stacked ResNet-BiLSTM-Based Accurate Detection and Classification of Genetic Disorders
Neural Processing Letters202313 citations26 referencesContributes 0.102
Epigenetic clock analysis reveals increased plasma cystatin C levels based on DNA methylation in major depressive disorder
Psychiatry Research202328 citations52 referencesContributes 0.065
GEPSi: A Python Library to Simulate GWAS Phenotype Data
bioRxiv (Cold Spring Harbor Laboratory)20213 citations23 referencesContributes 0.060
What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics
Human Genetics2021127 citations86 referencesContributes 0.056

Authors (31)

Till F. M. Andlauer ORCID,Jade Martins ORCID,Stella Iurato,Brigitte Kühnel,Melanie Waldenberger

Live enrichment

PubMed:32012148 PMC:PMC7043350

European Research Council

Grant: 281338

Gene x environment interactions in affective disorders - elucidating molecular mechanisms

European Commission

Grant: 733161

Multiple manifestations of genetic and non-genetic factors in Multiple Sclerosis disentangled with a multi-omics approach to accelerate personalised medicine

Influential Citations

goldLicense: cc-by

GOLDhttps://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007616&type=printable

publisherhttps://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007616&type=printable

publisherhttps://dx.plos.org/10.1371/journal.pcbi.1007616

repositoryhttps://doaj.org/article/76aaae874be14df5a3574fefa054b18e

repositoryhttps://epub.ub.uni-muenchen.de/73712/

Fields of Study

MedicineComputer ScienceBiology0301 basic medicine0303 health sciences03 medical and health sciences

MeSH Terms

HumansMultivariate AnalysisPolymorphism, Single NucleotideQuantitative Trait LociGenome-Wide Association StudyGenetic Association StudiesDeep Learning

Keywords

QH301-705.5Quantitative Trait LociPolymorphism, Single NucleotideDeep LearningMultivariate AnalysisHumansBiology (General)Function and Dysfunction of the Nervous SystemGenetic Association StudiesResearch ArticleGenome-Wide Association Study

Sustainable Development Goals

SDG 3: 3. Good health

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

FAIR Checklist

DataRank Breakdown

Top 5 citers driving the network score

Authors (31)

Funding (2 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)