[med-svn] r2704 - trunk/packages/theseus/trunk/debian

[mok0-guest at alioth.debian.org]

Author: mok0-guest
Date: 2008-11-17 23:14:32 +0000 (Mon, 17 Nov 2008)
New Revision: 2704

Modified:
   trunk/packages/theseus/trunk/debian/citation.bib
Log:
abstracts removed from citation.bib, due to copyright issues

Modified: trunk/packages/theseus/trunk/debian/citation.bib
===================================================================
--- trunk/packages/theseus/trunk/debian/citation.bib	2008-11-17 23:07:09 UTC (rev 2703)
+++ trunk/packages/theseus/trunk/debian/citation.bib	2008-11-17 23:14:32 UTC (rev 2704)
@@ -7,8 +7,6 @@
 pages = {18521-18527},
 doi = {10.1073/pnas.0508445103},
 year = {2006},
-abstract = {Procrustes analysis involves finding the optimal superposition of two or more “forms” via rotations, translations, and scalings. Procrustes problems arise in a wide range of scientific disciplines, especially when the geometrical shapes of objects are compared, contrasted, and analyzed. Classically, the optimal transformations are found by minimizing the sum of the squared distances between corresponding points in the forms. Despite its widespread use, the ordinary unweighted least-squares (LS) criterion can give erroneous solutions when the errors have heterogeneous variances (heteroscedasticity) or the errors are correlated, both common occurrences with real data. In contrast, maximum likelihood (ML) estimation can provide accurate and consistent statistical estimates in the presence of both heteroscedasticity and correlation. Here we provide a complete solution to the nonisotropic ML Procrustes problem assuming a matrix Gaussian distribution with factored covariances. Our analysis generalizes, simplifies, and extends results from previous discussions of the ML Procrustes problem. An iterative algorithm is presented for the simultaneous, numerical determination of the ML solutions.
-},
 URL = {http://www.pnas.org/content/103/49/18521.abstract},
 eprint = {http://www.pnas.org/content/103/49/18521.full.pdf+html}
 }
@@ -22,8 +20,6 @@
 pages = {2171-2172},
 doi = {10.1093/bioinformatics/btl332},
 year = {2006},
-abstract = {Summary: THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble.  Availability: ANSI C source code and selected binaries for various computing platforms are available under the GNU open source license from http://monkshood.colorado.edu/theseus/ or http://www.theseus3d.org  Contact: douglas.theobald at colorado.edu  Supplementary Information: Supplementary data including details of the ML superpositioning algorithm are available at Bioinformatics online.
-},
 URL = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/22/17/2171},
 eprint = {http://bioinformatics.oxfordjournals.org/cgi/reprint/22/17/2171.pdf}
 }
@@ -38,7 +34,6 @@
     volume = {4},
     url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.0040043},
     pages = {e43},
-    abstract = {The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology. },
     number = {2},
     doi = {10.1371/journal.pcbi.0040043}
 }