Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 2289387

Parallel line analysis: multifunctional software for the biomedical sciences.

P R Swank·M L Lewis·K L Damron·D R Morrison
View original source ↗Export metadata ↓

Abstract

An easy to use, interactive FORTRAN program for analyzing the results of parallel line assays is described. The program is menu driven and consists of five major components: data entry, data editing, manual analysis, manual plotting, and automatic analysis and plotting. Data can be entered from the terminal or from previously created data files. The data editing portion of the program is used to inspect and modify data and to statistically identify outliers. The manual analysis component is used to test the assumptions necessary for parallel line assays using analysis of covariance techniques and to determine potency ratios with confidence limits. The manual plotting component provides a graphic display of the data on the terminal screen or on a standard line printer. The automatic portion runs through multiple analyses without operator input. Data may be saved in a special file to expedite input at a future time.

Explore related subjects

Data Interpretation, StatisticalDose-Response Relationship, DrugMathematical ComputingProgramming LanguagesSoftwareSoftware DesignUser-Computer InterfaceNASA Center JSCNASA Discipline Cell BiologyJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P R Swank, M L Lewis, K L Damron, D R Morrison. 1990. Parallel line analysis: multifunctional software for the biomedical sciences.. https://doi.org/10.1016/0169-2607(90)90066-i

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2001

Tumor size on computed tomography scans: is one measurement enough?

BACKGROUND: Bidimensional tumor measurements are used routinely as surrogates for tumor volume. The purpose this study was to determine whether there is any added benefit in bidimensional or tridimensional measurements over a unidimensional measurement. METHODS: Sixty-nine colorectal hepatic metastases on 19 computed tomography scans (1-8 lesions per scan) from 9 patients were analyzed. Five patients contributed 2-4 scans each (mean, 3 scans). The standard volume of these lesions was determined by the "summation of areas" technique. The maximum axial dimension, the product of the greatest axial dimensions, and several volume estimates (based on the volumes of a sphere, an ellipsoid, and a cube) each were correlated with the standard volume. RESULTS: The maximum axial dimension and the product of the greatest axial dimensions correlated equally with tumor volume (correlation coefficient = 0.93). Surrogate measures based on the equations for a sphere and an ellipsoid underestimated tumor volume, whereas the equation for a cube overestimated volume. CONCLUSIONS: When reporting tumor size, there is no significant added benefit in reporting bidimensional or tridimensional measurements over the maximum axial dimension.

Data Interpretation, Statistical↗
PubMed2001

A comparison of non-linear non-parametric models for epilepsy data.

EEG spike and wave (SW) activity has been described through a non-parametric stochastic model estimated by the Nadaraya-Watson (NW) method. In this paper the performance of the NW, the local linear polynomial regression and support vector machines (SVM) methods were compared. The noise-free realizations obtained by the NW and SVM methods reproduced SW better than as reported in previous works. The tuning parameters had to be estimated manually. Adding dynamical noise, only the NW method was capable of generating SW similar to training data. The standard deviation of the dynamical noise was estimated by means of the correlation dimension.

Data Interpretation, Statistical↗