ArticleMolecular systems biology2009
Harnessing gene expression to identify the genetic basis of drug resistance.
Article in Molecular systems biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations.PloS one · 2022Pooled it
- Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis-An application in renal clear cell carcinoma.Genetic epidemiology · 2024Article
- Article
- Epigenetic regulation of gene expression in cancer: techniques, resources and analysis.Briefings in functional genomics · 2018Article
- Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments.Scientific reports · 2017Article
- Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease.Frontiers in cardiovascular medicine · 2017Review
- Systems medicine of inflammaging.Briefings in bioinformatics · 2016Article
- Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Methods for the integration of multi-omics data: mathematical aspects.BMC bioinformatics · 2016Article
- Context Sensitive Modeling of Cancer Drug Sensitivity.PloS one · 2015Article
- Harnessing natural sequence variation to dissect posttranscriptional regulatory networks in yeast.G3 (Bethesda, Md.) · 2014Article
- Principles and methods of integrative genomic analyses in cancer.Nature reviews. Cancer · 2014Review
- Designing of promiscuous inhibitors against pancreatic cancer cell lines.Scientific reports · 2014Article
- A pathway-based data integration framework for prediction of disease progression.Bioinformatics (Oxford, England) · 2014Article
- Predicting the phenotypic values of physiological traits using SNP genotype and gene expression data in mice.PloS one · 2014Article
- Multiscale modeling of the causal functional roles of nsSNPs in a genome-wide association study: application to hypoxia.BMC genomics · 2013Article
- Integrative analysis using module-guided random forests reveals correlated genetic factors related to mouse weight.PLoS computational biology · 2013Article
- Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype.PLoS genetics · 2013Article
- Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties.PloS one · 2013Article
- Leveraging models of cell regulation and GWAS data in integrative network-based association studies.Nature genetics · 2012Article
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.
Funding
Abstract
The advent of cost-effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug-free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene-drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug.
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.