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
Identification of a miRNA based model to detect prognostic subgroups in patients with aggressive B-cell lymphoma.
Carmen Nordmo,Gunther Glehr,Michael Altenbuchinger,Rainer Spang,Marita Ziepert,Heike Horn,Heike Horn,Annette M. Staiger,Annette M. Staiger,German Ott,Norbert Schmitz,Gerhard Held,Hermann Einsele,Max S. Topp,Andreas Rosenwald,Hilka Rauert-Wunderlich +15 more
TL;DR: In this paper, the expression of 800 miRNAs with the NanoString nCounter human miRNA assay on a cohort of 228 FFPE samples of patients enrolled in the RICOVER-60 and MegaCHOEP trials was analyzed to differentiate prognostic subgroups of patients with aggressive B-cell lymphoma.
Book ChapterDOI
High-Dimensional Profiling for Computational Diagnosis.
TL;DR: Fundamental issues from machine learning are reviewed and a procedure for the computational aspects of a clinical profiling study is recommended.
-negative high-grade B-cell lymphomas resembling Burkitt lymphoma MYC A recurrent 11q aberration pattern characterizes a subset of
Swen Wessendorf,Wolfram Klapper,Reiner Siebert Vater,Carsten Schwaenen,Rainer Spang,Monika Szczepanowski,Robert B. Russell,Grzegorz Rymkiewicz,Detlev Schindler,Matthias Schlesner,Markus Löffler,Roderick A. F. MacLeod,Inga Nagel,HG Drexler,Michael Hummel,Elaine S. Jaffe,Ralf Küppers,Christine Lefebvre,Barbara Pienkowska-Grela,Patrick Adam,Birgit Burkhardt,Alexander Claviez,Christine Itziar Salaverria,Idoia Martin-Guerrero,Rabea Wagener,Christian W. Kohler,Raul Ribeiro +26 more
Journal ArticleDOI
BITES: balanced individual treatment effect for survival data
Stefan Schrod,Andreas Schäfer,Stefan Solbrig,Robert Lohmayer,Wolfram Gronwald,Peter J. Oefner,Tim Beissbarth,Rainer Spang,Helena U. Zacharias,Michael Altenbuchinger +9 more
TL;DR: It is demonstrated in an application to a cohort of breast cancer patients that hormone treatment can be optimized based on six routine parameters using Integral Probability Metrics (IPM), and it is shown that this approach outperforms the state of the art.