ArticleStatistics in biosciences2018
A two-stage hidden Markov model design for biomarker detection, with application to microbiome research.
Article in Statistics in biosciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations.Bioengineering (Basel, Switzerland) · 2023Article
- Changes in vaginal community state types reflect major shifts in the microbiome.Microbial ecology in health and disease · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
It has been recognized that for appropriately ordered data, hidden Markov models (HMM) with local false discovery rate (FDR) control can increase the power to detect significant associations. For many high-throughput technologies, the cost still limits their application. Two-stage designs are attractive, in which a set of interesting features or biomarkers is identified in a first stage, and then followed up in a second stage. However, to our knowledge no two-stage FDR control with HMMs has been developed. In this paper, we study an efficient HMM-FDR based two-stage design, using a simple integrated analysis procedure across the stages. Numeric studies show its excellent performance when compared to available methods. A power analysis method is also proposed. We use examples from microbiome data to illustrate the methods.
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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.