ArticlePLoS biology2005
Transcription-based prediction of response to IFNbeta using supervised computational methods.
Article in PLoS biology, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 175 citations in OpenAlex.
- Generative AI Models in Time-Varying Biomedical Data: Scoping Review.Journal of medical Internet research · 2025Article
- GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns.Computational and structural biotechnology journal · 2024Article
- Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare.Biomedicines · 2024Review
- Phenotype similarities in automatically grouped T2D patients by variation-based clustering of IL-1β gene expression.EJIFCC · 2023Article
- Multiway sparse distance weighted discrimination.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles.BMC bioinformatics · 2021Article
- A recursive framework for predicting the time-course of drug sensitivity.Scientific reports · 2020Article
- Evaluation of classification and forecasting methods on time series gene expression data.PloS one · 2020Article
- Orthogonal joint sparse NMF for microarray data analysis.Journal of mathematical biology · 2019Article
- Elastic net-based prediction of IFN-β treatment response of patients with multiple sclerosis using time series microarray gene expression profiles.Scientific reports · 2019Article
- Discriminating sample groups with multi-way data.Biostatistics (Oxford, England) · 2017Article
- Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human.Infectious Disease Modelling · 2016Article
- The emerging agenda of stratified medicine in neurology.Nature reviews. Neurology · 2014Review
- Transcriptomic profile reveals gender-specific molecular mechanisms driving multiple sclerosis progression.PloS one · 2014Article
- Common and specific signatures of gene expression and protein-protein interactions in autoimmune diseases.Genes and immunity · 2013Article
- Classification of patients from time-course gene expression.Biostatistics (Oxford, England) · 2013Article
- Classification of time series gene expression in clinical studies via integration of biological network.PloS one · 2013Article
- An RNA profile identifies two subsets of multiple sclerosis patients differing in disease activity.Science translational medicine · 2012Article
- Early classification of multivariate temporal observations by extraction of interpretable shapelets.BMC bioinformatics · 2012Article
- Studying and modelling dynamic biological processes using time-series gene expression data.Nature reviews. Genetics · 2012Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 3 countries.
Funding
Abstract
Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNbeta) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNbeta to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNbeta engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.
Indexed as
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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.