Optimal design for high-throughput screening via false discovery rate control.
Sign Up to like & getrecommendations! Published in 2019 at "Statistics in medicine"
DOI: 10.1002/sim.8144
Abstract: High-throughput screening (HTS) is a large-scale hierarchical process in which a large number of chemicals are tested in multiple stages. Conventional statistical analyses of HTS studies often suffer from high testing error rates and soaring… read more here.
Keywords: false discovery; design; high throughput; rate ... See more keywords
Local structure learning of chain graphs with the false discovery rate control
Sign Up to like & getrecommendations! Published in 2018 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-018-9669-4
Abstract: Chain graphs (CGs) containing both directed and undirected edges, offer an elegant generalisation of both Markov networks and Bayesian networks. In this paper, we propose an algorithm for local structure learning of CGs. It works… read more here.
Keywords: false discovery; chain graphs; control; structure ... See more keywords
Controlling two-dimensional false discovery rates by combining two univariate multiple testing results with an application to mass spectral data
Sign Up to like & getrecommendations! Published in 2018 at "Chemometrics and Intelligent Laboratory Systems"
DOI: 10.1016/j.chemolab.2018.09.006
Abstract: Abstract Mass spectral data exhibit a small number of signals (true peaks) among many noisy observations (signals or true peaks) in a high-dimensional space. This unique aspect of mass spectral data necessitates solving the problem… read more here.
Keywords: spectral data; false discovery; procedure; two dimensional ... See more keywords
Peak p-values and false discovery rate inference in neuroimaging
Sign Up to like & getrecommendations! Published in 2019 at "NeuroImage"
DOI: 10.1016/j.neuroimage.2019.04.041
Abstract: Peaks are a mainstay of neuroimage analysis for reporting localization results. The current peak detection procedure in SPM12 requires a pre-threshold for approximating p-values and a false discovery rate (FDR) nominal level for inference. However,… read more here.
Keywords: values false; false discovery; inference; discovery rate ... See more keywords
Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of proteome research"
DOI: 10.1021/acs.jproteome.1c00600
Abstract: In shotgun proteomics, false discovery rate (FDR) estimation is a necessary step to ensure the quality of accepted peptide-spectrum matches (PSMs) from a database search. Popular statistical validation tools for FDR control tend to rely… read more here.
Keywords: false discovery; common decoy; estimation; discovery rate ... See more keywords
Significance estimation for large scale metabolomics annotations by spectral matching
Sign Up to like & getrecommendations! Published in 2017 at "Nature Communications"
DOI: 10.1038/s41467-017-01318-5
Abstract: The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false… read more here.
Keywords: large scale; spectral matching; false discovery; mass spectrometry ... See more keywords
Weighted False Discovery Rate Control in Large-Scale Multiple Testing
Sign Up to like & getrecommendations! Published in 2018 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2017.1336443
Abstract: ABSTRACT The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This article studies weighted multiple testing in a decision-theoretical framework. We develop oracle and data-driven procedures that aim… read more here.
Keywords: large scale; discovery rate; false discovery; rate ... See more keywords
Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control
Sign Up to like & getrecommendations! Published in 2022 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2022.2044333
Abstract: Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied with the goal of estimating the target GGM by utilizing the data from similar and related auxiliary studies. The similarity between the target graph and… read more here.
Keywords: gaussian graphical; false discovery; rate; graphical models ... See more keywords
Two-stage false discovery rate in microarray studies
Sign Up to like & getrecommendations! Published in 2019 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2018.1554122
Abstract: Abstract In microarray and other genomic studies, in view of an abundance of genes, one statistical approach is to hold the family wise error rate to a prescribed limit while controlling the false discovery rate… read more here.
Keywords: discovery rate; rate; two stage; false discovery ... See more keywords
Covariate-adjusted Gaussian graphical model estimation with false discovery rate control
Sign Up to like & getrecommendations! Published in 2020 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2020.1752385
Abstract: Abstract Recent genetic/genomic studies have shown that genetic markers can have potential effects on the dependence structure of genes. Motivated by such findings, we are interested in the estimation of covariate-adjusted Gaussian graphical model (CGGM).… read more here.
Keywords: adjusted gaussian; gaussian graphical; false discovery; graphical model ... See more keywords
Mechanisms and minimization of false discovery of metabolic bio-orthogonal non-canonical amino acid proteomics.
Sign Up to like & getrecommendations! Published in 2022 at "Rejuvenation research"
DOI: 10.1089/rej.2022.0019
Abstract: Metabolic proteomics has been widely used to characterize dynamic protein networks in many areas of biomedicine, including in the arena of tissue aging and rejuvenation. Bio-orthogonal non-canonical amino acid tagging (BONCAT) is based on mutant… read more here.
Keywords: bio orthogonal; orthogonal non; false discovery; non canonical ... See more keywords