R
Rainer Spang
Researcher at University of Regensburg
Publications - 171
Citations - 11726
Rainer Spang is an academic researcher from University of Regensburg. The author has contributed to research in topics: Gene expression profiling & Diffuse large B-cell lymphoma. The author has an hindex of 48, co-authored 166 publications receiving 10400 citations. Previous affiliations of Rainer Spang include Max Planck Society & Duke University.
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Journal ArticleDOI
Molecular diagnosis. Classification, model selection and performance evaluation.
Florian Markowetz,Rainer Spang +1 more
TL;DR: This work discusses supervised classification techniques applied to medical diagnosis based on gene expression profiles and introduces likelihood-based methods, classification trees, support vector machines and regularized binary regression for regularization by dimension reduction.
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A Novel Approach to Remote Homology Detection: Jumping Alignments
TL;DR: A new algorithm for protein classification and the detection of remote homologs that is an extension of the Smith-Waterman algorithm and computes a local alignment of a single sequence and a multiple alignment.
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Data Normalization of (1)H NMR Metabolite Fingerprinting Data Sets in the Presence of Unbalanced Metabolite Regulation.
Jochen Hochrein,Helena U. Zacharias,Franziska Taruttis,Claudia Samol,Julia C. Engelmann,Rainer Spang,Peter J. Oefner,Wolfram Gronwald +7 more
TL;DR: The Shapiro-Wilk test was applied successfully to a real world example of unbalanced regulation, namely, a set of plasma specimens collected from patients with and without acute kidney injury after cardiac surgery with cardiopulmonary bypass use.
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Reference point insensitive molecular data analysis.
Michael Altenbuchinger,Thorsten Rehberg,Helena U. Zacharias,Frank Stämmler,Katja Dettmer,Daniela Weber,Andreas Hiergeist,André Gessner,Ernst Holler,Peter J. Oefner,Rainer Spang +10 more
TL;DR: It is shown that reference point discrepancies compromise the performance of regression models like the LASSO, and zero-sum regression is superior to the LassO in case of a poor choice of reference point both in simulations and in an application that integrates intestinal microbiome analysis with metabolomics.
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Performance Evaluation of Algorithms for the Classification of Metabolic 1H NMR Fingerprints
Jochen Hochrein,Matthias S. Klein,Helena U. Zacharias,Juan Li,Gene Wijffels,Horst Joachim Schirra,Rainer Spang,Peter J. Oefner,Wolfram Gronwald +8 more
TL;DR: Six binary classification algorithms in combination with different strategies for data-driven feature selection were systematically compared on five data sets of urine, serum, plasma, and milk one-dimensional fingerprints obtained by proton nuclear magnetic resonance (NMR) spectroscopy.