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Daniel C. Chamrad

dblp:95/7069 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › metabolomics
retention time alignment
0.112009
Retention time alignment algorithms for LC/MS data must consider non-linear shifts · Bioinform. 2009
Bioinformatics and computational biology
liquid chromatography-mass spectrometry
0.012009
Retention time alignment algorithms for LC/MS data must consider non-linear shifts · Bioinform. 2009

Methods — techniques the papers use, named apart from their topics

shifting vectors · 0.1regression · 0.1
YearPublicationVenuePosition
2009 Retention time alignment algorithms for LC/MS data must consider non-linear shifts
abstract
MOTIVATION: Proteomics has particularly evolved to become of high interest for the field of biomarker discovery and drug development. Especially the combination of liquid chromatography and mass spectrometry (LC/MS) has proven to be a powerful technique for analyzing protein mixtures. Clinically orientated proteomic studies will have to compare hundreds of LC/MS runs at a time. In order to compare different runs, sophisticated preprocessing steps have to be performed. An important step is the retention time (rt) alignment of LC/MS runs. Especially non-linear shifts in the rt between pairs of LC/MS runs make this a crucial and non-trivial problem. RESULTS: For the purpose of demonstrating the particular importance of correcting non-linear rt shifts, we evaluate and compare different alignment algorithms. We present and analyze two versions of a new algorithm that is based on regression techniques, once assuming and estimating only linear shifts and once also allowing for the estimation of non-linear shifts. As an example for another type of alignment method we use an established alignment algorithm based on shifting vectors that we adapted to allow for correcting non-linear shifts also. In a simulation study, we show that rt alignment procedures that can estimate non-linear shifts yield clearly better alignments. This is even true under mild non-linear deviations. AVAILABILITY: R code for the regression-based alignment methods and simulated datasets are available at http://www.statistik.tu-dortmund.de/genetik-publikationen-alignment.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Katharina Podwojski, Arno Fritsch, Daniel C. Chamrad, Wolfgang Paul 0001, Barbara Sitek, Kai Stühler, Petra Mutzel, Christian Stephan, Helmut E. Meyer, Wolfgang Urfer, Katja Ickstadt, Jörg Rahnenführer
Bioinform.3