ArticleBioinformatics (Oxford, England)2014
Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 40 citations in OpenAlex.
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- A subcomponent-guided deep learning method for interpretable cancer drug response prediction.PLoS computational biology · 2023Article
- A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets.NeuroImage · 2022Article
- Computational analyses of mechanism of action (MoA): data, methods and integration.RSC chemical biology · 2022Review
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- Understanding the Molecular Drivers of Disease Heterogeneity in Crohn's Disease Using Multi-omic Data Integration and Network Analysis.Inflammatory bowel diseases · 2021Article
- Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings.Frontiers in neurology · 2021Review
- Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications.International journal of molecular sciences · 2020Article
- An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs.Royal Society open science · 2020Article
- Investigating Core Signaling Pathways of Hepatitis B Virus Pathogenesis for Biomarkers Identification and Drug Discovery via Systems Biology and Deep Learning Method.Biomedicines · 2020Article
- Predictive power of the Triticum root elongation test for the assessment of novel anti‑proliferative therapies.International journal of molecular medicine · 2019Article
- Development of Orthogonal Linear Separation Analysis (OLSA) to Decompose Drug Effects into Basic Components.Scientific reports · 2019Article
- Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.Molecular systems biology · 2018Article
- A review of connectivity map and computational approaches in pharmacogenomics.Briefings in bioinformatics · 2018Review
- Cell-specific prediction and application of drug-induced gene expression profiles.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- Comparing structural and transcriptional drug networks reveals signatures of drug activity and toxicity in transcriptional responses.NPJ systems biology and applications · 2017Article
- A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer.BMC medical genomics · 2016Article
- Bayesian group factor analysis with structured sparsity.Journal of machine learning research : JMLR · 2016Article
- From drug response profiling to target addiction scoring in cancer cell models.Disease models & mechanisms · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationAnalysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.
resultsIn this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids. AVAILABILITY AND IMPLEMENTATION: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparse. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.