Statistical method for modeling sequencing data from different technologies in longitudinal studies with application to Huntington disease-Reference-Cited by-同舟云学术

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Statistical method for modeling sequencing data from different technologies in longitudinal studies with application to Huntington disease

Published:2020-12-22 Issue:4 Volume:63 Page:745-760
ISSN:0323-3847
Container-title:Biometrical Journal
language:en
Short-container-title:Biometrical Journal

Author:

Fuady Angga M.¹²^ORCID,van Roon‐Mom Willeke M. C.³,Kiełbasa Szymon M.¹,Uh Hae‐Won²,Houwing‐Duistermaat Jeanine J.²⁴

Affiliation:

1. Department of Biomedical Data Sciences Leiden University Medical Center Leiden the Netherlands

2. Department of Biostatistics and Research Support Div. Julius Centrum University Medical Center Utrecht Utrecht the Netherlands

3. Department Human Genetics Leiden University Medical Center Leiden the Netherlands

4. Department of Statistics and Alan Turing Institute University of Leeds Leeds United Kingdom

Funder

H2020 Marie Skłodowska-Curie Actions

FP7 Health

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.201900235

Reference36 articles.

1. Response error in a transformation model with an application to earnings‐equation estimation*

2. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

3. Evaluation of O2PLS in OMICS data integration;Bouhaddani S. e.;BMC Bioinformatics,2016

4. Probabilistic partial least squares model: Identifiability, estimation and application;Bouhaddani S. E.;Journal of Multivariate Analysis,2018

5. Independent filtering increases detection power for high-throughput experiments

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1. ESTIMATION OF THE EFFECT OF SURROGATE MULTI-OMIC BIOMARKERS;THEOR BIOL FORUM;2021

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