ArticleBMC medical genomics2015
Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner.
Article in BMC medical genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.Chemical research in toxicology · 2022Review
- Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection.Communications biology · 2022Article
- Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation.Oxidative medicine and cellular longevity · 2022Article
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning.International journal of molecular sciences · 2021Review
- Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action.Frontiers in cellular and infection microbiology · 2021Article
- Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity.Journal of cheminformatics · 2020Article
- A Bayesian machine learning approach for drug target identification using diverse data types.Nature communications · 2019Article
- Network-based method for drug target discovery at the isoform level.Scientific reports · 2019Article
- Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data.Pharmaceutics · 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
- A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder.Frontiers in pharmacology · 2019Article
- Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data.Molecular omics · 2018Review
- Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures.Scientific reports · 2018Article
- Article
- Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets.Scientific reports · 2017Article
- Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review.The AAPS journal · 2017Review
- Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics.Scientific reports · 2017Article
- Comparing structural and transcriptional drug networks reveals signatures of drug activity and toxicity in transcriptional responses.NPJ systems biology and applications · 2017Article
- HEDD: the human epigenetic drug database.Database : the journal of biological databases and curation · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPhenotype-based high-throughput screening is a useful technique for identifying drug candidate compounds that have a desired phenotype. However, the molecular mechanisms of the hit compounds remain unknown, and substantial effort is required to identify the target proteins associated with the phenotype.
methodsIn this study, we propose a new method to predict target proteins of drug candidate compounds based on drug-induced gene expression data in Connectivity Map and a machine learning classification technique, which we call the "transcriptomic approach."
resultsUnlike existing methods such as the chemogenomic approach, the transcriptomic approach enabled the prediction of target proteins without dependence on prior knowledge of compound chemical structures. The prediction accuracy of the chemogenomic approach was highly depended on compounds structure similarities in data sets. In contrast, the prediction accuracy of the transcriptomic approach was maintained at a sufficient level, even for benchmark data consisting of structurally diverse compounds.
conclusionsThe transcriptomic approach reported here is expected to be a useful tool for structure-independent prediction of target proteins for drug candidate compounds.
Indexed as
Identifiers
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.