ArticleBiostatistics (Oxford, England)2013
Classification of patients from time-course gene expression.
Article in Biostatistics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Transcriptome modulation by hydrocortisone in severe burn shock: ancillary analysis of a prospective randomized trial.Critical care (London, England) · 2017Trial
- Early epitope-specific IgE antibodies are predictive of childhood peanut allergy.The Journal of allergy and clinical immunology · 2020Article
- Meta-analysis of peptides to detect protein significance.Statistics and its interface · 2020Article
- Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time.BioMed research international · 2019Article
- Discriminating sample groups with multi-way data.Biostatistics (Oxford, England) · 2017Article
- Computational dynamic approaches for temporal omics data with applications to systems medicine.BioData mining · 2017Review
- Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data.Statistical applications in genetics and molecular biology · 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
3 authors.
Funding
Abstract
Classifying patients into different risk groups based on their genomic measurements can help clinicians design appropriate clinical treatment plans. To produce such a classification, gene expression data were collected on a cohort of burn patients, who were monitored across multiple time points. This led us to develop a new classification method using time-course gene expressions. Our results showed that making good use of time-course information of gene expression improved the performance of classification compared with using gene expression from individual time points only. Our method is implemented into an R-package: time-course prediction analysis using microarray.
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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.