ArticleBioinformatics (Oxford, England)2018
KMgene: a unified R package for gene-based association analysis for complex traits.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Chinmedomics: a potent tool for the evaluation of traditional Chinese medicine efficacy and identification of its active components.Chinese medicine · 2024Review
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- Searching for genetic modulators of the phenotypic heterogeneity in Brugada syndrome.PloS one · 2022Article
- Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.Frontiers in genetics · 2022Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Summary: In this report, we introduce an R package KMgene for performing gene-based association tests for familial, multivariate or longitudinal traits using kernel machine (KM) regression under a generalized linear mixed model framework. Extensive simulations were performed to evaluate the validity of the approaches implemented in KMgene. Availability and implementation: http://cran.r-project.org/web/packages/KMgene. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.