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:
[EMAIL PROTECTED] 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}
}
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