ArticleBMC bioinformatics2014
MCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification.
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 13 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
13 citing papers in PubMed.
- On exact Bayesian credible sets for discrete parameters.Statistics & probability letters · 2025Article
- Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- Gut-host Crosstalk: Methodological and Computational Challenges.Digestive diseases and sciences · 2020Review
- A Multi-Trait Approach Identified Genetic Variants Including a Rare Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function.Scientific reports · 2019Article
- A Nonmathematical Review of Optimal Operator and Experimental Design for Uncertain Scientific Models with Application to Genomics.Current genomics · 2019Review
- The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data.Cancer informatics · 2017Article
- Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations.EURASIP journal on bioinformatics & systems biology · 2016Article
- Bayesian Regression with Network Prior: Optimal Bayesian Filtering Perspective.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2016Article
- On optimal Bayesian classification and risk estimation under multiple classes.EURASIP journal on bioinformatics & systems biology · 2015Article
- Bayesian ABC-MCMC Classification of Liquid Chromatography-Mass Spectrometry Data.Cancer informatics · 2015Article
- Efficient experimental design for uncertainty reduction in gene regulatory networks.BMC bioinformatics · 2015Article
- Proteomic study of facial melasma.Anais brasileiros de dermatologiaArticle
- Detecting Multivariate Gene Interactions in RNA-Seq Data Using Optimal Bayesian Classification.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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
backgroundSequencing datasets consist of a finite number of reads which map to specific regions of a reference genome. Most effort in modeling these datasets focuses on the detection of univariate differentially expressed genes. However, for classification, we must consider multiple genes and their interactions.
resultsThus, we introduce a hierarchical multivariate Poisson model (MP) and the associated optimal Bayesian classifier (OBC) for classifying samples using sequencing data. Lacking closed-form solutions, we employ a Monte Carlo Markov Chain (MCMC) approach to perform classification. We demonstrate superior or equivalent classification performance compared to typical classifiers for two synthetic datasets and over a range of classification problem difficulties. We also introduce the Bayesian minimum mean squared error (MMSE) conditional error estimator and demonstrate its computation over the feature space. In addition, we demonstrate superior or leading class performance over an RNA-Seq dataset containing two lung cancer tumor types from The Cancer Genome Atlas (TCGA).
conclusionsThrough model-based, optimal Bayesian classification, we demonstrate superior classification performance for both synthetic and real RNA-Seq datasets. A tutorial video and Python source code is available under an open source license at http://bit.ly/1gimnss .
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.