13,583 research outputs found
Bland Altman plots.
No full text(A) Display Bland Altman plot observer 1 and 2 volumetric method (cc). (B) Display Bland Altman plot observer 1 and R (radiologist) volumetric method (cc). (C) Display Bland Altman plot observer 2 and R volumetric method (cc). (D) Display Bland Altman plot observer 1 and 2 manual method (mm). (E) Display Bland Altman plot observer 1 and R manual method (mm). (F) Display Bland Altman plot observer 2 and R manual method (mm). Mean difference and limits of agreement are displayed as reference lines.</p
Marriage record of Harrison, Samuel G. and Altman, Mamie R.
No full textMarriage license for Samuel G. Harrison and Mamie R. Altman. Joseph F. Bell was the officiant
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No Apparent Reason
No full textThis paper discusses five works the author created and the thought process that went into the creation of these works. Helen Altman discusses the fascination with temporary or process pieces and the use of commonplace materials to depict a message
Bland-Altman methods for comparing methods of measurement and response to criticisms
No full textIntroduced in 1983, Bland-Altman methods is now considered the standard approach for assessment of agreement between two methods of measurement. The method is widely used by researchers in various disciplines so that the Bland-Altman 1986 Lancet paper has been named as the 29th mostly highly cited paper ever, over all fields. However, two papers by Hopkins (2004) and Krouwer (2007) questioned the validity of the Bland-Altman analysis. We review the points of critical papers and provide responses to them. The discussions in the critical papers of the Bland-Altman method are scientifically delusive. Hopkins misused the Bland-Altman methodology for research question of model validation and also incorrectly used least-square regression when there is measurement error in the predictor. The problem with Krouwers' paper is making sweeping generalisation of a very narrow and somewhat unrealistic situation. The method proposed by Bland and Altman should be used when the research question is method comparison. © 202
Un système expert sur IBM/PC : A. B. R. I.
No full textRaman P., Altman J. Un système expert sur IBM/PC : A. B. R. I.. In: Le médiéviste et l'ordinateur, N°15, printemps 1986. Quand l'ordinateur devient intelligent. pp. 5-6
Time to organize the bioinformatics resourceome
No full textThe initial steps toward a bioinformatics resourceome are
clear. First, an overall ontology with the high-level concepts
(algorithms, databases, organizations, papers, people, etc.)
must be created, with a set of standard attributes and a
standard set of relations between these concepts (e.g., people
publish papers, papers describe algorithms or databases,
organizations house people, etc.). The initial ontology should
be compact and built for distributed collaborative extension.
Second, a mechanism for people to extend this ontology with
subconcepts in order to describe their own resources should
be designed. The precise location of a tool within a taxonomy
is not critical—the author will place it somewhere based on
the location of similar/competing resources or based on a
best-informed guess. Others may create links to the resource
from other appropriate locations in the taxonomy in order to
ensure that competing interpretations of the appropriate
conceptual location for the resource are accommodated.
Third, the formats for the ontologies and the resource
descriptions should be published so enterprising software
engineers can create interfaces for surfing, searching, and
viewing the resources. The resulting distributed system of
resource descriptions would be extensible, robust, and useful
to the entire biomedical research community
Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines
No full textBackground: Multiple imputation (MI) provides an effective approach to handle missing covariate
data within prognostic modelling studies, as it can properly account for the missing data
uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling
techniques to obtain the estimates of interest. The estimates from each imputed dataset are then
combined into one overall estimate and variance, incorporating both the within and between
imputation variability. Rubin's rules for combining these multiply imputed estimates are based on
asymptotic theory. The resulting combined estimates may be more accurate if the posterior
distribution of the population parameter of interest is better approximated by the normal
distribution. However, the normality assumption may not be appropriate for all the parameters of
interest when analysing prognostic modelling studies, such as predicted survival probabilities and
model performance measures.
Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling
studies are provided. A literature review is performed to identify current practice for combining
such estimates in prognostic modelling studies.
Results: Methods for combining all reported estimates after MI were not well reported in the
current literature. Rubin's rules without applying any transformations were the standard approach
used, when any method was stated.
Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider
and more appropriate use of MI in future prognostic modelling studies
Modified Bland-Altman analysis using linear mixed models in R.
No full textProvided is the code used in the R software to perform the modified Bland-Altman analysis, which accounts for repeated measures within our data through the use of linear mixed models. (PDF)</p
Data for "Training data composition affects performance of protein structure analysis algorithms" by A. Derry, K. A. Carpenter, & R. B. Altman
No full textDescription
This repository contains all data used in "Training data composition affects performance of protein structure analysis algorithms", published in the Pacific Symposium on Biocomputing 2022 by A. Derry, K. A. Carpenter, & R. B. Altman.
The data consists of the following files:
ema_zenodo_data.tar.gz: train, validation, and test splits for Estimation of Model Accuracy task, in LMDB format
design_zenodo_data.tar.gz: train, validation, and test splits for Protein Sequence Design task, in JSON format
enz_cat_res_zenodo_data.tar.gz: train, validation, and test splits for Catalytic Residue and Enzyme Prediction task, in TF record format
Details on dataset construction can be found in our paper and dataloaders can be found in our Github repo.
Reference
A. Derry*, K. A. Carpenter*, & R. B. Altman, "Training data composition affects performance of protein structure analysis algorithms", 2021.
Dataset References
Datasets used were derived from the following works:
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019). Critical assessment of methods of protein structure prediction (CASP)—Round XIII. In Proteins: Structure, Function and Bioinformatics (Vol. 87, Issue 12, pp. 1011–1020). https://doi.org/10.1002/prot.25823
Ingraham, J., Garg, V. K., Barzilay, R., & Jaakkola, T. (2019). Generative Models for Graph-Based Protein Design. https://openreview.net/pdf?id=SJgxrLLKOE
Furnham, N., Holliday, G. L., de Beer, T. A. P., Jacobsen, J. O. B., Pearson, W. R., & Thornton, J. M. (2014). The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes. Nucleic Acids Research, 42 (Database issue), D485–D489
Reliability between Holter and PolarS810i for R-R intervals at rest and movement using Bland-Altman plots.
No full textReliability between Holter and PolarS810i for R-R intervals at rest and movement using Bland-Altman plots.</p
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