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Hierarchical multi-omics data integration and modeling predict cell-specific chemical proteomics and drug responses

Cell Reports Methods(2023)10.1016/j.crmeth.2023.100452Source: DataRank Database

Hierarchical multi-omics data integration and modeling predict cell-specific chemical proteomics and drug responses is a research paper published in Cell Reports Methods (2023). On theSindex it has a DataRank of 0.601. It has been cited 18 times, with 15 citing works in its 1-hop citation network.

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0.601DataRank · unranked

Scored on demand from live citation data

Open Access18 citations · 15 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)Impact (FWCI 2.50)OA: goldTopics (46)IDs (PubMed, PMC, OpenAlex)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 73%Citation Network 27%

Base Score Contribution

0.442

From this paper's citation signal

Citation Network Contribution

0.159

From 10 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 15 citers.

Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study
BMC Medical Informatics and Decision Making202414 citations61 referencesContributes 0.044
An Ensemble Cascade Forest‐Based Framework for Multi‐Omics Drug Response and Synergy Prediction
Advanced Intelligent Systems20242 citations43 referencesContributes 0.026
Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics
Analytical Chemistry202549 citations165 referencesContributes 0.024
A Map of the Lipid–Metabolite–Protein Network to Aid Multi-Omics Integration
Biomolecules20253 citations66 referencesContributes 0.021
Comparative case study on NAMs: towards enhancing specific target organ toxicity analysis
Archives of Toxicology20244 citations85 referencesContributes 0.019

Authors (3)

Qiao Liu ORCID,Lei Xie ORCID,You Wu ORCID

Live enrichment

PubMed:37159671 PMC:PMC10163019 OpenAlex:https://openalex.org/W4366086369

National Institutes of Health

Grant: R01GM122845

National Institute on Aging

Grant: R01AD057555

NIA NIH HHS

Grant: R01 AG057555

National Institutes of Health

Grant: 5R01AI057555-06

TpI2 Kinase in Macrophage Activation by Microbes

National Institutes of Health

Grant: 2R01GM122845-05

AI-powered chemical proteomics for drug discovery targeting orphan proteins

National Institute of General Medical Sciences

FWCI

2.50

Citation Percentile

0.9%

Citation Trend

2024

2025

2026

goldLicense: cc-by-nc-nd

journalhttps://doi.org/10.1016/j.crmeth.2023.100452

publisherhttps://doi.org/10.1016/j.crmeth.2023.100452

publisherhttps://api.elsevier.com/content/article/PII:S2667237523000711?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2667237523000711?httpAccept=text/plain

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37159671

Fields of Study

Computational Drug Discovery MethodsBioinformatics and Genomic NetworksMicrobial Metabolic Engineering and Bioproduction0206 medical engineering02 engineering and technologyHumansProteomicsMultiomicsProteomeMolecular BiologyDrug-Related Side Effects and Adverse Reactions

MeSH Terms

MultiomicsHumansMolecular BiologyProteomeProteomicsDrug-Related Side Effects and Adverse Reactions

Keywords

ProteomicsProteomeSystems biologyComputational biologyOmicsSystems pharmacologyData integrationDrug discoveryBiologyDrugComputer scienceBioinformaticsData miningPharmacologyGeneGeneticsMachine LearningPrecision MedicineMulti-task LearningDeep LearningChemical ProteomicsPhenotype ScreeningSide Effect PredictionDrug Efficacy PredictionDrug-Related Side Effects and Adverse ReactionsHumansMultiomicsMolecular BiologyArticle

Sustainable Development Goals

SDG 0: Good health and well-being