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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Clinical and pathological features of Burkitt lymphoma showing expression of BCL2--an analysis including gene expression in formalin-fixed paraffin-embedded tissue.
Neus Masqué-Soler,Monika Szczepanowski,Christian W. Kohler,Sietse M. Aukema,Inga Nagel,Julia Richter,Reiner Siebert,Rainer Spang,Birgit Burkhardt,Wolfram Klapper +9 more
TL;DR: BL expressing BCL2 can be considered to be MYC/BCL2 co‐expressors, a feature that is associated with poorer outcome in DLBCL but that has not been correlated with outcome in BL so far.
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Modelling cancer progression using Mutual Hazard Networks
TL;DR: Mutual Hazard Networks (MHN), a new Machine Learning algorithm to infer cyclic progression models from cross-sectional data, is proposed and compared favourably to acyclic models in cross-validated model fit on four datasets tested.
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Ataxia Telangiectasia-Mutated Gene Is a Possible Biomarker for Discrimination of Infiltrative Deep Penetrating Nevi and Metastatic Vertical Growth Phase Melanoma
Alexander Roesch,Bernd Becker,Stefan Bentink,Rainer Spang,Annegret Vogl,Ilja Hagen,Michael Landthaler,Thomas Vogt +7 more
TL;DR: A genome-wide, microarray-based approach was used to systematically prescreen for possible molecular markers differentially expressed between selected cases of typical DPN and metastatic NMM and detected a highly significant up-regulation of ATM transcription in NMM, which was also mirrored by ATM protein up- regulation.
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Random gene sets in predicting survival of patients with hepatocellular carcinoma
Timo Itzel,Rainer Spang,Thorsten Maass,S Munker,Stephanie Roessler,Matthias P. Ebert,Hans J. Schlitt,Wolfgang Herr,Matthias Evert,Andreas Teufel +9 more
TL;DR: It is demonstrated that using “swarm intelligence” of multiple gene sets for prognosis prediction may not only be superior but also more robust for predictive purposes in HCC.
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Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints.
Helena U. Zacharias,Thorsten Rehberg,Sebastian Mehrl,Daniel Richtmann,Tilo Wettig,Peter J. Oefner,Rainer Spang,Wolfram Gronwald,Michael Altenbuchinger +8 more
TL;DR: This work extended linear zero-sum regression to the logistic regression framework and showed in two applications to 1H NMR-based metabolomics data how this approach overcomes the scaling problem.