ArticleBMC bioinformatics2014
Probabilistic drug connectivity mapping.
Article in BMC bioinformatics, 2014. 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.
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery.Genome medicine · 2025Article
- CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases.Briefings in bioinformatics · 2024Article
- Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.Chemical research in toxicology · 2022Review
- A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer.eLife · 2022Article
- The Advantages of Connectivity Map Applied in Traditional Chinese Medicine.Frontiers in pharmacology · 2021Review
- Article
- Drug Combinations: Mathematical Modeling and Networking Methods.Pharmaceutics · 2019Review
- A review of connectivity map and computational approaches in pharmacogenomics.Briefings in bioinformatics · 2018Review
- Predictive approaches for drug combination discovery in cancer.Briefings in bioinformatics · 2018Review
- Cell-specific prediction and application of drug-induced gene expression profiles.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury.Nature communications · 2017Article
- Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics.Scientific reports · 2017Article
- In silico methods for drug repurposing and pharmacology.Wiley interdisciplinary reviews. Systems biology and medicine · 2016Review
- A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network.BioMed research international · 2016Article
- A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.PLoS neglected tropical diseases · 2016Article
- Implications of the small number of distinct ligand binding pockets in proteins for drug discovery, evolution and biochemical function.Bioorganic & medicinal chemistry letters · 2015Review
- Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of connectivity mapping is to match drugs using drug-treatment gene expression profiles from multiple cell lines. This can be viewed as an information retrieval task, with the goal of finding the most relevant profiles for a given query drug. We infer the relevance for retrieval by data-driven probabilistic modeling of the drug responses, resulting in probabilistic connectivity mapping, and further consider the available cell lines as different data sources. We use a special type of probabilistic model to separate what is shared and specific between the sources, in contrast to earlier connectivity mapping methods that have intentionally aggregated all available data, neglecting information about the differences between the cell lines.
resultsWe show that the probabilistic multi-source connectivity mapping method is superior to alternatives in finding functionally and chemically similar drugs from the Connectivity Map data set. We also demonstrate that an extension of the method is capable of retrieving combinations of drugs that match different relevant parts of the query drug response profile.
conclusionsThe probabilistic modeling-based connectivity mapping method provides a promising alternative to earlier methods. Principled integration of data from different cell lines helps to identify relevant responses for specific drug repositioning applications.
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.