ArticleScientific data2018
Sustainable data and metadata management at the BD2K-LINCS Data Coordination and Integration Center.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Article
- Comprehensive Proteomics Metadata and Integrative Web Portals Facilitate Sharing and Integration of LINCS Multiomics Data.Molecular & cellular proteomics : MCP · 2025Article
- Pan-Cancer Drug Sensitivity Prediction from Gene Expression using Deep Learning.bioRxiv : the preprint server for biology · 2024Article
- Artificial intelligence-driven new drug discovery targeting serine/threonine kinase 33 for cancer treatment.Cancer cell international · 2023Article
- CORN-Condition Orientated Regulatory Networks: bridging conditions to gene networks.Briefings in bioinformatics · 2022Article
- Modeling community standards for metadata as templates makes data FAIR.Scientific data · 2022Article
- Emerging Bioinformatics Methods and Resources in Drug Toxicology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Lessons learned from SARS-CoV-2 measurements in wastewater.The Science of the total environment · 2021Article
- The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation.JMIR medical informatics · 2021Article
- Development of a chemogenomics library for phenotypic screening.Journal of cheminformatics · 2021Article
- The role of metadata in reproducible computational research.Patterns (New York, N.Y.) · 2021Review
- Machine and deep learning approaches for cancer drug repurposing.Seminars in cancer biology · 2021Review
- Community Approaches for Integrating Environmental Exposures into Human Models of Disease.Environmental health perspectives · 2020Article
- LINCS Data Portal 2.0: next generation access point for perturbation-response signatures.Nucleic acids research · 2020Article
- Informatics Approaches for Harmonized Intelligent Integration of Stem Cell Research.Stem cells and cloning : advances and applications · 2020Review
- Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures.Molecules (Basel, Switzerland) · 2019Article
- Mining data and metadata from the gene expression omnibus.Biophysical reviews · 2019Review
- How to rekindle drug discovery process through integrative therapeutic targeting?Expert opinion on drug discovery · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
The NIH-funded LINCS Consortium is creating an extensive reference library of cell-based perturbation response signatures and sophisticated informatics tools incorporating a large number of perturbagens, model systems, and assays. To date, more than 350 datasets have been generated including transcriptomics, proteomics, epigenomics, cell phenotype and competitive binding profiling assays. The large volume and variety of data necessitate rigorous data standards and effective data management including modular data processing pipelines and end-user interfaces to facilitate accurate and reliable data exchange, curation, validation, standardization, aggregation, integration, and end user access. Deep metadata annotations and the use of qualified data standards enable integration with many external resources. Here we describe the end-to-end data processing and management at the DCIC to generate a high-quality and persistent product. Our data management and stewardship solutions enable a functioning Consortium and make LINCS a valuable scientific resource that aligns with big data initiatives such as the BD2K NIH Program and concords with emerging data science best practices including the findable, accessible, interoperable, and reusable (FAIR) principles.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.