A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data.

Abstract read

In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Thierry ChekouoDivision of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minnesota, USA.ORCID 0000-0002-3594-9182

Himadri MukherjeeDepartment of Mathematics and Statistics, University of Minnesota Duluth, Minnesota, USA.

Funding

Interpretable Bayesian Non-linear statistical learning models for multi-omics data integrationR35GM150537 · NIGMS · UNIVERSITY OF MINNESOTA · PI Thierry Chekouo Tekougang · 2023 to 2026

$1.5M

Natural Sciences and Engineering Research Council of Canada RGPIN-2019-04810NIGMS NIH HHS R35 GM150537NIH HHS 1R35GM150537-01University of Minnesota

6 · The paper itself

Abstract

We introduce a Bayesian approach for biclustering that accounts for the prior functional dependence between genes using hidden Markov models (HMMs). We utilize biological knowledge gathered from gene ontologies and the hidden Markov structure to capture the potential coexpression of neighboring genes. Our interpretable model-based clustering characterized each cluster of samples by three groups of features: overexpressed, underexpressed, and irrelevant features. The proposed methods have been implemented in an R package and are used to analyze both the simulated data and The Cancer Genome Atlas kidney cancer data.

Indexed as

Bayes TheoremKidney NeoplasmsMarkov ChainsBiometryCluster AnalysisGene Expression ProfilingGene Expression Regulation, NeoplasticHumansBayesian hidden Markov modelbiclusteringbiological prior knowledgekidney cancer

Identifiers

PMID38817110

DOI10.1002/bimj.202300173

PMCPMC11239327

What OpenQuestion holds

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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.