Learning Cultured Neuronal Network Evolution Using False Discovery Rate Analysis

2013 ◽  
Author(s):  
A. Napoli ◽  
I. Obeid
Keyword(s):  
False Discovery Rate ◽  
Neuronal Network ◽  
Network Evolution ◽  
Rate Analysis ◽  
False Discovery ◽  
False Discovery Rate Analysis

scholarly journals Empirical null and false discovery rate analysis in neuroimaging

NeuroImage ◽  
2009 ◽  
Vol 44 (1) ◽  
pp. 71-82 ◽  
Author(s):  
A SCHWARTZMAN ◽  
R DOUGHERTY ◽  
J LEE ◽  
D GHAHREMANI ◽  
J TAYLOR
Keyword(s):  
False Discovery Rate ◽  
Rate Analysis ◽  
False Discovery ◽  
False Discovery Rate Analysis

scholarly journals False discovery rate analysis of brain diffusion direction maps

2008 ◽  
Vol 2 (1) ◽  
pp. 153-175 ◽  
Author(s):  
Armin Schwartzman ◽  
Robert F. Dougherty ◽  
Jonathan E. Taylor
Keyword(s):  
False Discovery Rate ◽  
Rate Analysis ◽  
False Discovery ◽  
False Discovery Rate Analysis

scholarly journals Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach

2021 ◽  
Vol 8 ◽  
Author(s):  
Mateusz Olek ◽  
Agnel Praveen Joseph
Keyword(s):  
False Discovery Rate ◽  
Model Building ◽  
Software Tool ◽  
Atomic Models ◽  
Rate Analysis ◽  
Iterative Model ◽  
False Discovery ◽  
Validation Score ◽  
Technological Developments ◽  
Robust Measures

Significant technological developments and increasing scientific interest in cryogenic electron microscopy (cryo-EM) has resulted in a rapid increase in the amount of data generated by these experiments and the derived atomic models. Robust measures for the validation of 3D reconstructions and atomic models are essential for appropriate interpretation of the data. The resolution of data and availability of software tools that work across a range of resolutions often limit the quality of derived models. Hence, the final atomic model is often incomplete or contains regions where atomic positions are less reliable or incorrectly built. Extensive manual pruning and local adjustments or rebuilding are usually required to address these issues. The presented research introduces a software tool for the validation of the backbone trace of atomic models built in the cryo-EM density maps. In this study, we use the false discovery rate analysis, which can be used to segregate molecular signals from the background. Each atomic position in the model can be associated with an FDR backbone validation score, which can be used to identify potential mistraced residues. We demonstrate that the proposed validation score is complementary to existing validation metrics and is useful especially in cases where the model is built in the maps having varying local resolution. We also discuss the application of the score for automated pruning of atomic models built ab-initio during the iterative model building process in Buccaneer. We have implemented this score in the CCP-EM software suite.

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