ArticleCPT: pharmacometrics & systems pharmacology2016
Systematic Quality Control Analysis of LINCS Data.
Article in CPT: pharmacometrics & systems pharmacology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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Who cites it
14 citing papers in PubMed, 38 citations in OpenAlex.
- Article
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- Data Valuation with Gradient Similarity.ArXiv · 2024Article
- PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction.Journal of cheminformatics · 2024Article
- Graph Structured Neural Networks for Perturbation Biology.bioRxiv : the preprint server for biology · 2024Article
- Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.BMC bioinformatics · 2023Article
- Transcriptome‑based drug repositioning identifies TPCA‑1 as a potential selective inhibitor of esophagus squamous carcinoma cell viability.International journal of molecular medicine · 2022Article
- Repurposing of thalidomide and its derivatives for the treatment of SARS-coV-2 infections: Hints on molecular action.British journal of clinical pharmacology · 2021Article
- Prediction and identification of synergistic compound combinations against pancreatic cancer cells.iScience · 2021Article
- Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects.Nanomaterials (Basel, Switzerland) · 2020Review
- Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data.BMC bioinformatics · 2019Article
- Expression-based drug screening of neural progenitor cells from individuals with schizophrenia.Nature communications · 2018Article
- Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data.Molecular omics · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The Library of Integrated Cellular Signatures (LINCS) project provides comprehensive transcriptome profiling of human cell lines before and after chemical and genetic perturbations. Its L1000 platform utilizes 978 landmark genes to infer the transcript levels of 14,292 genes computationally. Here we conducted the L1000 data quality control analysis by using MCF7, PC3, and A375 cell lines as representative examples. Before perturbations, a promising 80% correlation in transcriptome was observed between L1000- and Affymetrix HU133A-platforms. After library-based shRNA perturbations, a moderate 30% of differentially expressed genes overlapped between any two selected controls viral vectors using the L1000 platform. The mitogen-activated protein kinase, vascular endothelial growth factor, and T-cell receptor pathways were identified as the most significantly shared pathways between chemical and genetic perturbations in cancer cells. In conclusion, L1000 platform is reliable in assessing transcriptome before perturbation. Its response to perturbagens needs to be interpreted with caution. A quality control analysis pipeline of L1000 is recommended before addressing biological questions.
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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.