ArticleAssay and drug development technologies2016
Transcriptional Characterization of Compounds: Lessons Learned from the Public LINCS Data.
Article in Assay and drug development technologies, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Searching for a Lifeline: Transcriptome Profiling Studies of Influenza Susceptibility and Resistance.Journal of innate immunity · 2017Pooled it
- Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction.Frontiers in toxicology · 2023Article
- A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer.eLife · 2022Article
- Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets.Frontiers in chemistry · 2020Article
- Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks.Scientific reports · 2019Article
- Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data.BMC bioinformatics · 2019Article
- Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research.Frontiers in pharmacology · 2019Review
- A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.BMC systems biology · 2017Article
- Opportunities and challenges in phenotypic drug discovery: an industry perspective.Nature reviews. Drug discovery · 2017Review
- Looking beyond the cancer cell for effective drug combinations.Genome medicine · 2016Article
- Systematic Quality Control Analysis of LINCS Data.CPT: pharmacometrics & systems pharmacology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The NIH-funded LINCS program has been initiated to generate a library of integrated, network-based, cellular signatures (LINCS). A novel high-throughput gene-expression profiling assay known as L1000 was the main technology used to generate more than a million transcriptional profiles. The profiles are based on the treatment of 14 cell lines with one of many perturbation agents of interest at a single concentration for 6 and 24 hours duration. In this study, we focus on the chemical compound treatments within the LINCS data set. The experimental variables available include number of replicates, cell lines, and time points. Our study reveals that compound characterization based on three cell lines at two time points results in more genes being affected than six cell lines at a single time point. Based on the available LINCS data, we conclude that the most optimal experimental design to characterize a large set of compounds is to test them in duplicate in three different cell lines. Our conclusions are constrained by the fact that the compounds were profiled at a single, relative high concentration, and the longer time point is likely to result in phenotypic rather than mechanistic effects being recorded.
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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.