Original Papers




Shutta KH, Weighill D, Burkholz R, Guebila MB, DeMeo DL, Zacharias HU, Quackenbush J, Altenbuchinger M.
DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks.
Nucleic Acids Res. 2022 Dec 19:gkac1157. doi: 10.1093/nar/gkac1157. Epub ahead of print. PMID: 36533448.

Kurz NS, Perera-Bel J, Höltermann C, Tucholski T, Yang J, Beissbarth T, Dönitz J.
Identifying Actionable Variants in Cancer - The Dual Web and Batch Processing Tool MTB-Report.
Stud Health Technol Inform. 2022 Aug 17;296:73-80. doi: 10.3233/SHTI220806. PMID: 36073491.

Blazquez R, Chuang HN, Wenske B, Trigueros L, Wlochowitz D, Liguori R, Ferrazzi F, Regen T, Proescholdt MA, Rohde V, Riemenschneider MJ, Stadelmann C, Bleckmann A, Beißbarth T, van Rossum D, Hanisch UK, Pukrop T.
Intralesional TLR4 agonist treatment strengthens the organ defense against colonizing cancer cells in the brain.
Oncogene. 2022 Nov;41(46):5008-5019. doi: 10.1038/s41388-022-02496-3. Epub 2022 Oct 12. PMID: 36224342; PMCID: PMC9652147.

Bockhop F, Zeldovich M, Cunitz K, Van Praag D, van der Vlegel M, Beissbarth T, Hagmayer Y, von Steinbuechel N, and CENTER-TBI participants and investigators:
Measurement invariance of six language versuions of the post-traumatic stress disorder checklist for DSM-5 in civilians after traumatic brain injury.
Sci Rep. 2022 Oct 4;12(1):16571. doi: 10.1038/s41598-022-20170-2.

Vinhoven L.; Stanke F.; Hafkemeyer S.; Nietert M.
Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis.
International Journal of Molecular Sciences, Section Biochemistry, Special Issue: Small Molecule Drug Design and Research (accepted October 2022). https://www.mdpi.com/1422-0067/23/20/12351

Voskamp  M.; Vinhoven L.; Stanke F.; Hafkemeyer S.; Nietert M.
Integrating Text Mining into the Curation of Disease Maps.

Biomolecules 12, no. 9 (September 2022): 1278. doi.org/10.3390/biom12091278.

Altenbuchinger M,  Berndt H, Kosch R, Lang I, Dönitz J, Oefner JP, Gronwald W, Zacharias UH: 
Bucket Fuser: statistical signal extraction for 1D 1H NMR metabolomics data
Metabolites 202212

(9), 812; doi.org/10.3390/metabo12090812 (registering DOI) - 29 Aug 2022

Kurz NS, Perera-Bel J, Höltermann C, Tucholski T, Yang J, Beissbarth T, Dönitz J: 
Identifying Actionable Variants in Cancer – The Dual Web and Batch Processing Tool MTB-Report
Volume 296: German Medical Data Sciences 2022 – Future Medicine: More Precise, More Integrative, More Sustainable

Emons G, Auslander N, Jo P, Kitz J, Azizian A, Hu Y, Hess CF, Roedel C, Sax U, Salinas G, Stroebel P, Kramer F, Beissbarth T, Grade M, Ghadimi M, Ruppin E, Ried T, Gaedcke J:
Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy.
Br J Cancer. 2022 Sep;127(4):766-775. doi: 10.1038/s41416-022-01842-2. Epub 2022 May 21.

Hakeemi MS, Ansari S, Teuscher M, Weißkopf M, Großmann D, Kessel T, Dönitz J, Siemanowski J, Wan X, Schultheis D, Frasch M, Roth S, Schoppmeier M, Klingler M, Bucher G:
Screens in fly and beetle reveal vastly divergent gene sets required for developmental processes.
BMC Biol. 2022 Feb 8;20(1):38. doi: 10.1186/s12915-022-01231-4.

Lueders A, Bleckmann A, Beißbarth T, Schildhaus HU:
CLO22-061:c-Met Alteration in Patients With Metastasized Colorectal Carcinoma - An Evaluation of Methods of Detection, Clinical Impact and Discussion of c-Met as Potential Therapeutic Target.
JNCCN, Vol 20; Issue 3.5; 2022; Doi: https://doi.org/10.6004/jnccn.2021.7191

Schrod S, Schäfer A, Solbrig S, Lohmayer R, Gronwald W, Oefner PJ, Beißbarth T, Spang R, Zacharias HU, Altenbuchinger M:
BITES: Balanced Individual Treatment Effect for Survival data
arXiv:2201.03448  [stat.ME]

Menck K, Wlochowitz D, Wachter A, Conradi L-C, Wolff A, Scheel AH, Korf U, Wiemann S, Schildhaus H-U, Bohnenberger H, Wingender E, Pukrop T, Homayounfar K, Beißbarth T, Bleckmann A:
High-Throughput Profiling of Colorectal Cancer Liver Metastases Reveals Intra- and Inter-Patient Heterogeneity in the EGFR and WNT Pathways Associated with Clinical Outcome. 
Cancers. 2022; 14(9):2084. doi.org/10.3390/cancers14092084

Menck K, Heinrichs S, Wlochowitz D, Sitte M, Noeding H, Janshoff A, Treiber H, Ruhwedel T, Schatlo B, von der Brelie C, Wiemann S, Pukrop T, Beißbarth T, Binder C, Bleckmann A:
WNT11/ROR2 signaling is associated with tumor invasion and poor survival in breast cancer.
J Exp Clin Cancer Res. 2021 Dec 15;40(1):395. doi: 10.1186/s13046-021-02187-z. PMID: 34911552; PMCID: PMC8672621.

Giacomelli C, Jung J, Wachter A, Ibing S, Will R, Uhlmann S, Mannsperger H, Sahin Ö, Yarden Y, Beißbarth T, Korf U, Körner C, Wiemann S:
Coordinated regulation of WNT/β-catenin, c-Met, and integrin signalling pathways by miR-193b controls triple negative breast cancer metastatic traits.
BMC Cancer. 2021 Dec 4;21(1):1296. doi: 10.1186/s12885-021-08955-6. PMID: 34863149; PMCID: PMC8642942.

Buentzel J, Klemp HG, Kraetzner R, Schulz M, Dihazi GH, Streit F, Bleckmann A, Menck K, Wlochowitz D, Binder C:
Metabolomic Profiling of Blood-Derived Microvesicles in Breast Cancer Patients.
Int. J. Mol. Sci. 2021, 22, 13540, doi: 10.3390/ijms222413540

Mewes C, Alexander T, Büttner B, Hinz J, Alpert A, Popov AF, Beißbarth T, Tzvetkov M, Grade M, Quintel M, Bergmann I, Mansur A:
Effect of the Lymphocyte Activation Gene 3 Polymorphism rs951818 on Mortality and Disease Progression in Patients with Sepsis-A Prospective Genetic Association Study.
J Clin Med. 2021 Nov 15;10(22):5302. doi: 10.3390/jcm10225302. PMID: 34830585; PMCID: PMC8621793.

Wang K, Stevens R, Alachram H, Li Y, Soldatova L, King R, Ananiadou S, Schoene AM, Li M, Christopoulou F, Ambite JL, Matthew J, Garg S, Hermjakob U, Marcu D, Sheng E, Beißbarth T, Wingender E, Galstyan A, Gao X, Chambers B, Pan W, Khomtchouk BB, Evans JA, Rzhetsky A:
NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.
NPJ Syst Biol Appl. 2021 Oct 20;7(1):38. doi: 10.1038/s41540-021-00200-x. PMID: 34671039; PMCID: PMC8528865.

Alachram H, Chereda H, Beißbarth T, Wingender E, Stegmaier P:
Text mining-based word representations for biomedical data analysis and protein-protein interaction networks in machine learning tasks.
PLoS One. 2021 Oct 15;16(10):e0258623. doi: 10.1371/journal.pone.0258623. PMID: 34653224; PMCID: PMC8519453.

Ammer-Herrmenau C, Pfisterer N, van den Berg T, Gavrilova I, Amanzada A, Singh SK, Khalil A, Alili R, Belda E, Clement K, Abd El Wahed A, Gady EE, Haubrock M, Beißbarth T, Ellenrieder V, Neesse A:
Comprehensive Wet-Bench and Bioinformatics Workflow for Complex Microbiota Using Oxford Nanopore Technologies.
mSystems. 2021 Aug 31;6(4):e0075021. doi: 10.1128/mSystems.00750-21. Epub 2021 Aug 24. PMID: 34427527; PMCID: PMC8407471.

Nietert M, Vinhoven L, Auer F, Hafkemeyer S, Stanke F:
Comprehensive analysis of chemical structures that have been tested as CFTR activating substances in a publicly available database CandActCFTR
Frontiers in Pharmacology, 2021. https://www.frontiersin.org/articles/10.3389/fphar.2021.689205

Pallenberg S, T, Junge S, Ringshausen C, F., Sauer-Heilborn A, Hansen G, Dittrich A M, Tümmler B, Nietert M.
CFTR modulation with elexacaftor-tezacaftor-ivacaftor in people with cystic fibrosis assessed by the β-adrenergic sweat rate assay
Journal of Cystic Fibrosis,2021. https://doi.org/10.1016/j.jcf.2021.10.005

Vinhoven L, Voskamp M, Nietert M M.
Mapping Compound Databases to Disease Maps—A MINERVA Plugin for CandActBase
J. Pers. Med. 2021, 11(11), 1072; https://doi.org/10.3390/jpm11111072

Häckl M, Tauber P, Schweda F, Zacharias HU, Altenbuchinger M, Oefner PJ, Gronwald W:
An R - Package for the Deconvolution and Integration of 1D NMR Data: MetaboDecon1D.
METABOLITES 11(7): 1-15, doi: 10.3390/metabo11070452

Jachimowicz RD, Klapper W, Glehr G, Müller H, Haverkamp H, Thorns C, Hansmann ML, Möller P, Stein H, Rehberg T, von Tresckow B, Reinhardt HC, Borchmann P, Chan FC, Spang R, Scott DW, Engert A, Steidl C, Altenbuchinger M, Rosenwald A:
Gene expression-based outcome prediction in advanced stage classical Hodgkin lymphoma treated with BEACOPP.
Leukemia. 2021 Dec;35(12):3589-3593. doi: 10.1038/s41375-021-01314-1. Epub 2021 Jun 10. PMID: 34112956; PMCID: PMC8632672.

Schlotzig V, Kornrumpf K, König A, Tucholski T, Hügel J, Overbeck TR, Beissbarth T, Koch R, Dönitz J.
Predicting the Effect of Variants of Unknown Significance in Molecular Tumor Boards with the VUS-Predict Pipeline.
Stud Health Technol Inform. 2021 Sep 21;283:209-216.
doi: 10.3233/SHTI210562.

Vinhoven, L, Stanke, F,  Hafkemeyer, S, Nietert, M M.:
CFTR Lifecycle Map—A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations.
Int. J. Mol. Sci. 2021, 22(14), 7590
doi: 10.3390/ijms22147590

Schultheiss UT, Kosch R, Kotsis F, Altenbuchinger M, Zacharias HU:
Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses.
Metabolites. 2021 Jul 16;11(7):460. doi: 10.3390/metabo11070460. PMID: 34357354; PMCID: PMC8304377.

Kalya M, Beißbarth T, Kel AE:
Master Regulators Associated with Poor Prognosis in Gliblastoma Multiforme.
Biochem. Mosc.-Suppl. Ser. B-Biomed. Chem. 2021, 15: 1-11
doi: 10.18097/PBMC20216703201.

Kalya M, Kel A, Wlochowitz D, Wingender E, Beißbarth T.:
IGFBP2 Is a Potential Master Regulator Driving the Dysregulated Gene Network Responsible for Short Survival in Glioblastoma Multiforme.
Front Genet. 2021 Jun 15;12:670240.
doi: 10.3389/fgene.2021.670240 

Chereda H, Bleckmann A, Menck K, Perera-Bel J, Stegmaier P, Auer F, Kramer F, Leha A, Beißbarth T.:
Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer.
Genome Med. 2021 Mar 11;13(1):42.
doi: 10.1186/s13073-021-00845-7

Dröge LH, Hennies S, Lorenzen S, Conradi LC, Quack H, Liersch T, Helms C, Frank MA, Schirmer MA, Rave-Fränk M, Beißbarth T, Wolff HA:
Prognostic value of the micronucleus assay for clinical endpoints in neoadjuvant radiochemotherapy for rectal cancer.
BMC Cancer. 2021 Mar 4;21(1):219.
doi: 10.1186/s12885-021-07914-5. PMID: 33663399; PMCID: PMC7931609.

Dusch N, Oldani M, Steffen T, Kitz J, Koenig U, Azizian A, König A, Ströbel P, Beissbarth T, Ghadimi M, Gaedcke J.:
Intensified Histopathological Work-Up after Pancreatic Head Resection Reveals Relevant Prognostic Markers.
Digestion. 2021;102(2):265-273.
doi: 10.1159/000504648

Oliveira T, Goldhardt T, Edelmann M, Rogge T, Rauch K, Kyuchukov ND, Menck K, Bleckman A, Kalucka J, Khan S, Gaedcke J, Haubrock M, Beissbarth T, Bohnenberger H, Planque M, Fendt SM, Ackermann L, Ghadimi M, Conradi LC: 
Effects of the Novel PFKFB3 Inhibitor KAN0438757 on Colorectal Cancer Cells and Its Systemic Toxicity Evaluation In Vivo.
Cancers (Basel). 2021 Feb 28;13(5):1011.
doi: 10.3390/cancers13051011. PMID: 33671096; PMCID: PMC7957803.

Grebener BL, Barth J, Anders S, Beißbarth T, Raupach T.:
A prediction-based method to estimate student learning outcome: Impact of response rate and gender differences on evaluation results.
Med Teach. 2021 Jan 27:1-12.
doi: 10.1080/0142159X.2020.1867714.

Koerdel K, Spitzner M, Meyer T, Engels N, Krause F, Gaedcke J, Conradi LC, Haubrock M, Beißbarth T, Leha A, Johnsen SA, Ghadimi BM, Rose-John S, Grade M, Wienands J.:
NOTCH Activation via gp130/STAT3 Signaling Confers Resistance to Chemoradiotherapy.
Cancers (Basel). 2021 Jan 26;13(3):455.
doi: 10.3390/cancers13030455

Nordmo C, Glehr G, Altenbuchinger M, Spang R, Ziepert M, Horn H, Staiger AM, Ott G, Schmitz N, Held G, Einsele H, Topp M, Rosenwald A, Rauert-Wunderlich H:
Identification of a miRNA based model to detect prognostic subgroups in patients with aggressive B-cell lymphoma.
LEUKEMIA LYMPHOMA 2020; 62(5): 1107-1115, doi: 10.1080/10428194.2020.1861268



Sprenger T, Beißbarth T, Sauer R, Tschmelitsch J, Fietkau R, Hohenberger W, Staib L, Raab HR, Rödel C, Ghadimi M.
The long-term influence of hospital and surgeon volume on local control and survival in the randomized German Rectal Cancer Trial CAO/ARO/AIO-94.
Surg Oncol. 2020 Dec;35:200-205.
doi: 10.1016/j.suronc.2020.08.021.

Mewes C, Alexander T, Büttner B, Hinz J, Alpert A, Popov AF, Ghadimi M, Beißbarth T, Tzvetkov M, Grade M, Quintel M, Bergmann I, Mansur A.:
TIM-3 Genetic Variants Are Associated with Altered Clinical Outcome and Susceptibility to Gram-Positive Infections in Patients with Sepsis.
Int J Mol Sci. 2020 Nov 6;21(21):8318.
doi: 10.3390/ijms21218318. 

Bleckmann A., Kirchner B., Nietert M., Peeck M., Balkenhol M., Egert D., Rohde T. V., Beißbarth T., Pukrop T.:
Impact of pre-OP independance in patients with limited brain metastases on long-term survival.
BMC Cancer 20, 973 (2020)
doi: 10.1186/s12885-020-07459-z

Wlochowitz, D., Wingender E., Beißbarth T., Kel A.:
IGFB2 is a Potential Master-Regulator Driving Dysregulated Gene Network responsible for Short Survival in Glioblastoma Mulitiforme.
Preprints 2020, 2020100046
doi: 10.20944/preprints202010.0046.v1

Chereda H., Bleckmann A., Menck K., Perera-Bel J., Stegmaier P., Auer F., Kramer F., Leha A., Beißbarth T.:
Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer.
Preprint: bioRxiv Aug. 2020
doi: 10.1101/2020.08.05.238519

Blazquez R, Rietkötter E, Wenske B, Wlochowitz D, Sparrer D, Vollmer E, Müller G, Seegerer J, Sun X, Dettmer K, Barrantes-Freer A, Stange L, Utpatel K, Bleckmann A, Treiber H, Bohnenberger H, Lenz C, Schulz M, Reimelt C, Hackl C, Grade M, Büyüktas D, Siam L, Balkenhol M, Stadelmann C, Kube D, Krahn MP, Proescholdt MA, Riemenschneider MJ, Evert M, Oefner PJ, Klein CA, Hanisch UK, Binder C, Pukrop T.:
LEF1 supports metastatic brain colonization by regulating glutathione metabolism and increasing ROS resistance in breast cancer.
Int J Cancer. 2020 Jun 1;146(11):3170-3183.
doi: 10.1002/ijc.32742

Jo P., Bernhardt M., Nietert M., König A., Azizian A., Schirmer A.M., Grade M., Kitz J., Reuter-Jessen K., Ghadimi M., Ströbel P., Schildhaus H.-U., Gaedecke J.:
Kras mutation status concordance between the primary tumor and the corresponding metastasis in patients with rectal cancer.
Plos One 2020 Oct; 15(19):e0239806.

Uhlig J., Biggemann L., Nietert M.M., Beißbarth T., Lotz J., Kim H.S., Trojan L., Uhlig A.:
Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach
Medicine (Baltimore) 2020 Apr;99(16).
doi: 10.1097/MD.0000000000019725

Daou R., Beißbarth T., Wingender E., Gültas M., Haubrock M.:
Constructing temporal regulatory cascades in the context of development and cell differentiation.
PLoS One. 2020 Apr 10;15(4):e0231326.
doi: 10.1371/journal.pone.0231326

Stork L., Ellenberger D., Rubrecht K., Reindl M., Beißbarth T., Friede T., Kümpfel T., Gerdes L.A., Gloth M., Liman T., Paul T., Brück W., Metz I.:
Antibody signatures in patients with histopathologically defined multiple sclerosis patterns.
Acta Neuropathol. 2020 Mar;139(3):547-564.
doi: 10.1007/s00401-019-02120-x

Mewes C., Böhnke C., Alexander T., Büttner B., Hinz J., Popov A.F., Ghadimi M., Beißbarth T., Raddatz D., Meissner K., Quintel M., Bergmann I., Mansur A.:
Favorable 90-Day Mortality in Obese Caucasian Patients with Septic Shock According to the Sepsis-3 Definition
J. Clin. Med. 2019 Dec. 24;9(1).
doi: 10.3390/jcm9010046

Umbach N., Beißbarth T., Bleckmann A., Duttge G., Flatau L., König A., Kuhn J., Perera-Bel J., Roschauer J., Schulze T.G. Schweda M., Urban A., Zimmermann A., Sax U.:
Clinical application of genomic high-throuput data: Infrastructural, ethical, legal and psychosocial aspects.
Eur. Neuropsychopharmacol. 2019 Dec. 19.
doi: 10.1016/j.euroneuro.2019.09.008

Rühlmann F., Windhof-Jaidhauser I.M., Menze C., Beißbarth T., Bohnenberger H., Ghadimi M., Dango S.,:
The prognostic capacities of CBP and p300 in locally advanced rectal cancer.
World J. Surg. Oncol. 2019 Dec. 19;17(1):224.
doi: 10.1186/s12957-019-1764-8

Wolff A., Bayerlová M., Gaedecke J., Kube D., Beißbarth T.:
Correction: A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells.
PLoS One. 2019 Oct. 24; 14(19): e0224062
doi: 10.1371/journal.pone.0224062

Valerius O., Asif A.R., Beißbarth T., Bohrer R., Dihazi H., Feussner K., Jahn O., Majcherczyk A., Schmidt B., Schmitt K., Urlaub H., Lenz C.:
Mapping Cellular Microenvironments: Proximity labeling and Complexome Profiling (Seventh Symposium of the Göttingen Proteomics Forum)
Cells. 2019 Oct 2;8(10). pii: E1192.
doi: 10.3390/cells8101192

Diefenhardt M., Hofheinz R.D., Martin D., Beißbarth T., Arnold D., hartmann A., von der Grün J., Grützmann R., Liersch T., Ströbel P., Grabenbauer G.G., Rieger M., Fietkau R., Graeven U., Weitz J., Folprecht G., Ghadimi M., Rödel F., Rödel C., Fokas E., German Rectal Cancer Study group:
Leukocytosis and neutrophilia as a independent prognostic immunological biomarkers for clinical outcome in the CAO/ARO/AIO-04 randomized phase 3 rectal cancer trial
Int. J. Cancer. 2019 Oct 15;145(8):2282-2291.

Conradi L.C., Spitzner M., Metzger A.L., Kisly M., Middel P., Bohnenberger H., Gaedcke J., Ghadimi M.B., Liersch T., Rüschoff J., Beißbarth T., König A., Grade M.:
Combined targeting of HER-2 and HER-3 represents a promising therapeutic strategy in colorectal cancer
BMC Cancer. 2019 Sep. 5;19(1):880.
doi: 10.1186/s12885-019-6051-0

Chereda H., Bleckmann A., Kramer F. Leham A. and Beissbarth T.:
Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer,
Stud. Health. Technol. Inform., 2019 Sep 3; 267:181-186
doi: 10.3233/SHTI190824

Sitte M., Menck K., Wächter A., Reinz E., Korf U., Wiemann S., Bleckmann A., Beissbarth T.:
Reconstruction of Different Modes of WNT Dependant Protein networks from Time Series Protein Quantification
Stud. Health Technol. Inform. 2019 Sep 3;267:175-180
doi: 103233/SHTI190823

Runzheimer J., Mewes C., Büttner B., Hinz J., Popov A.F., Ghadimi M., Kristof K., Beissbarth T., Schamroth J., Tzvetkov M., Schmack B., Quintel M., Bergmann I., Mansur A.:
Lack of Association between the Functional Polymorphism TREM-1 rs2234237 and the Clinical Course of sepsis among Critically III Causcasian Patients - A monocentric Prospective Genetic Association Study
J. Clin. med. 2019 Mar 3;8(3).
doi: 10.3390/jcm8030301

Mewes C., Büttner B., Hinz J., Alpert A., popov A.F., Ghadimi M., Beissbarth T., Tzetkov M., Jensen O., Runzheimer J., Quintel M., Shen-Orr S., bergmann I., Mansur A.:
CTLA-4 genetic Variants Predict Survival in Patients with Sepsis
J. Cin. Med. 2019 jan. 10;8(1).
doi: 10.3390/jcm8010070


Kristof K., Büttner B., Grimm A., Mewes C., Schmack B., Popov A.F., Ghadimi M., Beissbarth T., Hinz J., Bergmann I., Mansur A.:
Anaemia requiring red blood cell transfusion is associated with unfavourable 90-day survival in surgical patients with sepsis
BMC Res. Notes: 2018 Dec 11; 11(1):879
doi: 10.1186/s13104-018-3988-z link

Blazquez R., Wlochowitz D., Wolff A., Seitz S., Wachter A., Perera-Bel J., Bleckmann A., Beißbarth T., Salinas G., Riemenschneider M.J., Proescholdt M., Evert M., Utpatel K., Siam L., Schatlo B., Balkenhol M., Stadelmann C., Schildhaus H.U., Korf U., Reinz E., Wienmann S., Vollmer E., Schulz M., Ritter U., Hanisch U.K., Pukrop T.:
PI3K: A master regulator of brain metastasis-promoting macrophages/ microglia
Glia. 2018 Nov; 66(11):2438-2455
doi: 10.1002/glia.23485 link

Mewes C., Büttner B., Hinz J., Alpert A., Popov A.F., Ghadimi M., Beissbarth T., Tzvetkov M., Shen-Orr S., Bergmann, Masur A.:
The CTLA-4 rs231775 GG genotype is associated with favorable 90-day survival in Caucasian patients with sepsis
Sci. Rep. 2018 Oct. 11;8(1):15140
doi: 10.1038/s41598-018-33246-9 link

Fromme J.E., Schmitz K., Wachter A., Grzelinski M., Zielinski D., Koppel C., Conradi L.C., Homayounfar K., Hugo T., Hugo S., Lukat L., Rüschoff J., Ströbel P., Ghadimi M., Beißbarth T., Reuter-Jessen K., Bleckmann A., Schildhaus H.U.:
FGFR3 mRNA overexpression defines a subset oligometastatic colorectal cancers with worse prognosis
Oncotarget. 2018 Aug. 14;9(63):32204-32218
doi:10.18632/oncotarget.25941 link

Wolff A., Perera-Bel J., Schildhaus H.U., Homayounfar K., Schatlo B., Bleckmann A., Beißbarth T.:
Using RNA-Seq Data for the Detection of a Panel of Clinically relevant Mutations
Stud. Health Technol. Inform. 2018;253:217-221
doi: 10.3233/978-1-61499-896-9-217 link

Bohnenberger H., Kaderali L., Ströbel P., Yepes D., Plessmann U., Dharia N.V., Yao S., Heydt C., Merkelbach-Bruse S., Emmert A., Hoffmann J., Bodemeyer J., reuter-Jessen K., Lois A.M., Dröge L.H., Baumeister P., Walz C., Biggemann L., Walter R., Häupl B., Comoglio F., Pan K.T., Scheich S., Lenz C., Küffer S., Bremmer F., Kitz J., Sitte M., Beißbarth T., Hinterthaner M., Sebastian M., Lotz J., Schildhaus H.U., Wolf H., danner B.C., Brandts C., Büttner R., Canis M., Stegmaier K., Serve H., Urlaub H., Oellerich T.:
Comparative proteomics reveals a diagnostic signature for pulmonary head-and-neck cancer metastasis
EMBO Mol. Med. 2018 Sep; 10(9).
doi: 10.15252/emmm.201708428 link

Breunig C., Erdem N., Bott A., Greiwe J.F., reinz E., Bernhardt S., Giacomelli C., Wachter A., Kantelhardt E.J., Beißbart T., Vetter M., Wiemann S.:
TGF­ ?-1 regulates HGF-induced cell migration and hepatocyte growth factor receptor MET expression via C-ets and miR-128-3p in basal-like breast cancer
Mol. Oncol. 2018 Sep;12(9):1447-1463
doi: 10.1002/1878-0261.12355 link

Kitz J., Fokas E., Beissbarth T., Ströbel P, Wittekind C., Hartmann A., Rüschoff J., Papadopoulos T., Rösler E., Ortloff-Kittredge P., Kania U., Schlitt H., Link K.H., Bechstein W., Raab H.R., Staib L., Germer C.T., Liersch T., Sauer R., Rödel C., Ghadimi M., Hohenberger W., German Rectal Cancer Study Group:
Association of Plane of Total Mesorectal Excision With Prognosis of Rectal Cancer: Secondary Analysis of the CAO/ARO/AIO-04 Phase 3 Randomized Clinical Trial
JAMA Surg. 2018 Aug 1;153(8):e181607
doi: 10.1001/jamasurg.2018.1607 link

Sprenger T., Beißbarth T., Sauer R., Tschmelitsch J., Fietkau R., Liersch T., Hohenberger W., Staib L., Gaedcke J., Raab H.R., Rödel C., Ghadimi M.:
Long-term prognostic impact of surgical complications in the German Rectal Cancer Trial CAO/ARO/AIO-94
Br. J. Surg. 2018 Oct;105(11):1510-1518.
doi: 10.1002/bjs.10877 link

Uhlig J., Uhlig A., Kunze M., Beissbarth T., Fischer U., Lotz J., Wienbeck S.:
Novel Breast Imaging and mchine learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using machine Learning Techniques
AJR. Am. J. Roentgenol. 2018 Aug;211(2):W123-W131
doi: 10.2214/AJR.17.19298 link

Wolff A., Bayerlova M., Gaedcke J., Kube D., Beißbarth T.:
A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt-Lymphoma cells
PLoS One. 2018 May 16;13(5):e0197162
doi: 10.1371/journal.pone.0197162 link

Dihazi H., Asif A.R., Beißbarth T., Bohrer R., Feussner K., Jahn O., Lenz C., Majcherczyk A., Schmidt B., Schmitt K., Urlaub H., valerius O.:
Integrative omics-from data to biology
Expert Rev. Proteomics. 2018 Jun;15(6):463-466
doi: 10.1080/14789450.2018.1476143 link

Hünlich M., Lubos E., Beuthner B.E., Puls M., Bleckmann A., Beißbarth T., Tichelbäcker T., Rudolph V., Baldus S., Schäfer U., Treede H., Von Bardeleben R.S., Blankenberg S., Schillinger W.:
Acute and Long-Term Hemodynamic Effects of MitraClip Implantation on a Preexisting Secondary Right Herat Failure
Biomed. Res. Int. 2018 Mar 14;2018:6817832
doi: 10.1155/2018/6817832 link

Lüke F., Blazquez R., Yamaci R.F., Lu X., Pregler B., Hannus S., Menhart K., Hellwig D., Wester H.J., Kropf S., heudobler D., grosse J., Moosbauer J., Hutterer M., Hau P., Riemenschneider M.J., Bayerlova M., Bleckmann A., Polzer B., Beißbarth T., Klein C.A., Pukrop T.:
Isolated metastasis of an EGFR-L858R-mutated NSCLC of the meninges: the potential impact of CXCL12/CXCR4 axis in EGFRmut NSCLC in diagnosis, follow-up and treatment
Oncotarget. 2018 Apr 10(27):18844-18857
doi: 10.18632/oncotarget.24787 link

Perera-Bel J., Hutter B., heining C., Bleckmann A., Fröhlich M., Fröhling S., Glimm H., Brors B., Beißbarth T.:
From somatic variants towards precision oncology: Evidence-driven reporting of treatment options in molecular tumor boards
Genome Med. 2018 Mar 15;10(1):18
doi: 10.1186/s13073-018-0529-2 link

Apweiler R., Beissbarth T., Berthold M.R., Blüthgen N., Burmeister Y., Dammann O., Deutsch A., Feuerhake A., Franke A., Hasenauer J., Hoffmann S., Höfer T., Jansen P.L., Kaderali L., Klingmüller U., Koch I., Kohlbacher O., Kuepfer L., Lammert F., maier D., Pfeifer N., Radde N., Rehm M., Roeder I., Saez-Rodriguez J., Sax U., Schmeck B., Schuppert A., Seilheimer B., Theis F.J., Vera J., Wolkenhauer O.:
Whiter systems medicine?
Exp. Mol. med. 2018 Mar 2;50(3):e453
doi: 10.1038/emm.2017.290 link

Stork L., Ellenberger D., Beißbarth T., Friede T., Luccinetti C.F., Brück W., Metz I.:
Differences in the Responses to Apheresis Therapy of Patients With 3 Histopathologically Classified Immunopathological Patterns of Multiple Sclerosis
Jama Neurol. 2018 Apr 1;75(4):428-435.
doi: 10.1001/jamaneurol.2017.4842 link

Tomas-Roig J., Piscitelli F., Gil V., Quintana E., Ramio-Torrenta L.L., Del Rio J.A., Moore T.P., Agbemenyah H., Salinas G., Pommerenke C., Lorenzen S., Beißbarth T., Hoyer-Fender S., Di Marzo V., Havemann-Reinecke U.:
Effects of repeated long-term psychosocial stress and acute cannabinoid exposure on mouse corticostriatal circuitries: Implications for neuropsychiatric disorders
CNS Neurosci. Ther. 2018 Jun;24(6):528-538
doi: 10.1111/cns.12810 link

Hu Y., Gaedcke J., Emons G., Beissbarth T., Grade M., Jo P., Yeager M., Chanock S.J., Wolff H., Camps J., Ghadimi B.M., Ried T.
Colorectal cancer susceptibility loci as predictive markers of rectal cancer prognosis after surgery
Genes Chromosomes Cancer. 2018 Mar; 57(3):140-149
doi: 10.1002/gcc.22512 link

Meckbach, C., Wingender, E. and Gültas, M.:
Removing background co-occurrences of transcription factor binding sites greatly improves the prediction of specific transcription factor cooperations
Front. Genet. 9, 189 (2018)
doi: 10.3389/fgene.2018.00189  link

Hua, X., Tang, R., Xu, X., Wang, Z., Xu, Q., Chen, L., Wingender, E., Li, J., Zhang, C. and Wang, J.:
mirTrans: a resource of transcriptional regulation on microRNAs for human cell lines
Nucleic Acids Res. 46, D168-D174 (2018)
doi: 10.1093/nar/gkx996  link

Wingender, E., Schoeps, T., Haubrock, M., Krull, M. and Dönitz, J.:
TFClass: expanding the classification of human transcription factors to their mammalian orthologs
Nucleic Acids Res. 46, D343-D347 (2018)
doi: 10.1093/nar/gkx987  link

Dönitz, J., Gerischer, L., Hahnke, S., Pfeiffer, S. and Bucher, G.:
Expanded and updated data and a query pipeline for iBeetle-Base
Nucleic Acids Res. 46, D831–D835 (2018)
doi: 10.1093/nar/gkx984  link

Bernhardt S., Bayerlova M., Vetter M., Wachter A., Mitra D., Hanf V., Lantzsch T., Uleer C., Peschel S., John J., Buchmann J., Weigert E., Bürrig K.F., Thomassen C., Korf U., Beissbarth T., Wiemann S., Kantelhardt E.J.,
Proteomic profiling of breast cancer metabolism identifies SHMT2 and ASCT2 as prognostic factors.
Breast Cancer Res. 2017 Oct 11;19(1):112
doi: 101186/s13058-017-0905-7 link

Fledrich R., Mannil M., Leha A., Ehbrecht C., Solari A., Pelayo-Negro A.L., Berciano J., Schlotter-Weigl B., Schnitzer T.J., Prukop T., Garcia-Angarita N., Czesnik D., Haberlova J., Mazanec R., Paulus W., Beissbarth T., Walter M.C., Triaal C., Hogrel J.Y., Duboourg O., Schenone A., Baets J., De Jonghe P., Shy M.E., Horvath R., Pareyson D., Seemann P., Young P., Sereda M.W.:
Biomarkers predict outcome in Charcot-Marie-Tooth disease 1A
J. Neurol. Neurosurg. Psychiatry. 2017 Nov;88(11):941-952
doi: 10.1136/jnnp-2017-315721 link

Hinz J., Büttner B., Kriesel F., Steinau M., Frederik Popov A., Ghadimi M, Beissbarth T., Tzvetkov M., Bergmann I., Mansur A.:
The FER rs4957796 TT genotype is associated with unfavorable 90-day survival in Caucasian patients with severe ARDS due to pneumonia
Sci. Rep. 2017 Aug 29;7(1):9887
doi: 10.1038/s41598-017-085540-7 link

Emons G., Spitzner M., Reineke S., Möller J., Auslander N., Kramer F., Hu Y., Beissbarth T., Wolff H.A., Rave-Fränk M., Heßmann E., Gaedcke J., Ghadimi B.M., Johnson S.A., Ried T., Grade M.:
Chemoradiotherapy resistance in Colorectal Cancer Cells is Mediated by Wnt/ß-catenin Signaling
Mol. Cancer. Res. 2017 Nov;15(11):1381-1490
doi: 10.1158/1541-7786 link

Lowes M., Kleiss M., Lueck R., Detken S., Koenig A., Nietert M., Beissbarth T., Stanek K., Langer C., Ghadimi M., Conradi L.C., Homayounfar K.:
The utilization of of multidisciplinary tumor boards (MDT) in clinical routine: results of a health care research study focusing on patients with metastasized colorectal cancer
Int. J. Colorectal Dis. 2017 Oct;32(19):1463-1469
doi: 10.1007/s00384-017-2871-z link

Bayerlova M., menck K., Klemm F., Wolff A., Pukrop T., Binder C., Beißbarth T., Bleckmann A.:
Ror2 Signaling and Its Relevance in Breast Cancer Progression
Front. Oncol. 2017 Jun. 26;7:135
doi: 10.3389/fonc.2017.00135 link

Rühlmann F., Nietert M., Sprenger T., Wolff H.A., Homayoufar K., Middel P., Bohnenberger H., Beissbarth T., Ghadimi B.M., Liersch T., Conradi L.C.:
The Prognostic Value of Tyrosine Kinase SRC Expression in Locally Advanced Rectal Cancer
J. Cancer. 2017 Apr 10;8(7):1229-1237
doi: 10.7150/jca.16980 link

Jo P., Azizian A., Salendo J., Kramer F., Bernhardt M., Wolff H.A. Gruber J., Grade M., Beißbarth T., Ghadimi B.M., Gaedecke J.:
Changes of Microrna Levels in Plasma of Patients with Rectal Cancer during Chemoradiotherapy
Int. J. Mol Sci. 2017 May 27;18(6).
doi: 10.3390/ijms18061140 link

Bocuk D., Wolff A., Krause P., Salinas G., Bleckmann A., Hackl C., Beissbarth T., Koenig S.:
The adaption of colerectal cancer cells when forming metastases in the liver: expression of associated genes and pathways in a mouse model
BMC Cancer. 2017 May 19;17(1):342
doi: 10.1186/s12885/s12885-017-3342-1 link

Mohr S., Doebele C., Comoglio F., Berg T., Beck J., Bohnenberger H., Alexe G., Corso J., Ströbel P., Wachter A., Beissbarth T., Schnütgen F., Cremer A., Haetscher N., Göllner S., Rouhi A., Palmqvist L., Rieger M.A., Schroeder T., Bönig H., Müller-Tidow C., Kuchenbauer F., Schütz E., Green A.R., Urlaub H., Stegmaier K., Humphries R.K., Serve H, Oellerich T.:
Hoxa9 and Meis1 Cooperatively Induce Addiction to Syk Signaling by suppressing miR-146a in Acute Myeloid Leukemia
doi: 10.1016/j.ccell.2017.03.001 link

Sahlmann C.O., Homayounfar K., Niessner M., Dyczkowski J., Conradi L.C., Braulke F., Meller B., Beißbarth T., Ghadimi B.M., Meller J., Goldenberg D.M. Liersch T.:
Repeated adjuvant anti-CEA radioimmunotherapy after resection of colorectal liver metastases: Safety, feasibility, and long-term efficacy results of a prospective phase 2 study
Cancer. 2017 Feb;123(4):638-649
doi: 10.1002/cncr.30390 link

Stegmaier, P., Kel, A. and Wingender, E.:
geneXplainR: An R interface for the geneXplain platform
J. Open Source Softw. 2, 412 (2017)
doi: 10.21105/joss.00412  link

Tiburcy, M., Hudson, J. E., Balfanz, P., Schlick, S., Meyer, T., Chang Liao, M.-L., Levent, E., Raad, F., Zeidler, S., Wingender, E., Riegler, S., Wang, M., Gold, J. D., Kehat, I., Wettwer, E., Ravens, U., Dierickx, P., van Laake, L. W., Goumans, M. J., Khadjeh, S., Toischer, K., Hasenfuss, G., Couture, L. A., Unger, A., Linke, W. A., Araki, T., Neel, B., Keller, G., Gepstein, L., Wu, J. C. and Zimmermann, W.-H.:
Defined engineered human myocardium with advanced maturation for applications in heart failure modelling and repair
Circulation 135, 1832-1847 (2017)
doi:10.1161/CIRCULATIONAHA.116.024145  link

Jo P., Nietert M., Gusky L., Kitz J., Conradi L.C., Müller-Dornieden A., Schüler P., Wolff H.A., Rüschoff J., Ströbel P., Grade M., Liersch T., Beißbarth T., Ghadimi M.B., Sax U., Gaedcke J.:
Neoadjuvant Therapy in Rectal Cancer - Biobanking of Preoperative Tumor Biopsies
Sci. Rep. 2016 Oct 18;6:35589 link

Kruppa J., Kramer F., Beißbarth T., Jung K.:
A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments
Stat. Appl. genet. Mol. Biol. 2016 Oct 1;15(5):401-414
doi: 10.1515/sagmb-2015-0082 link

Azizian A., Epping I., Kramer F., Jo P., bernhardt M., Kitz J., Salinas G.,  Wolff H.A., Grade M., Beißbarth T., Ghadimi B.M., Gaedcke J.:
Prognostic Value of MicroRNAs in Preoperative Treated Rectal Cancer
Int. J. Mol. Sci. 2016 Apr 15; 17 (4):568
doi: 10.3390/ijms17040568 link

Sprenger T., Rothe H., Conradi L.C., Beissbarth T., Kauffels A., Kitz J., Homayounfar K., Wolff H., Ströbel P., Ghadimi M., Wittekind C., Sauer R., Liersch T.:
Stage-Dependent Frequency of Lymph Node Metastases in Patients With Rectal Carcinoma After Preoperative Chemoradiation: Results from the CAO/ARO/AIO-94 Trial and From a Comparative Prospective Evaluation With Extensive Pathological Workup
Dis. Colon Rectum. 2016 May;59(5):377-85
doi: 10.1097/DCR.0000000000000570 link

Metz I., Beißbarth T., Ellenberger D., Pache F., Stork L., Ringelstein M., Aktas O., Jarius S., Wildemann B., Dihazi H., Friede T., Brück W., Ruprecht K., Paul F.:
Serum peptide reactivities may distinguish neuromyelitis optica subgroups and multiple sclerosis
Neurol. Neuroimmunol Neuroinflamm. 2016 Feb. 2;3(2):e204
doi: 10.1212/NXI.0000000000000204 link

Bleckmann A., Conradi L.C., Menck K., Schmick N.A., Schubert A., Rietkötter E., Arackal J., Middel P., Schambony A., Liersch T., Homayounfar K., Beißbart T., Klemm F., Binder C., Pukrop T.:
ß-catenin-endependant WNT signaling and Ki67 in contrast to the estrogen receptor status are prognostic and associated with poor prognosis in breast cancer liver metastases
Clin. Exp. Metastasis. 2016 Apr. 33(4):309-23
doi: 10.1007/s10585-016-9780-3 link

Wachter A., Beißbarth T.:
Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration approach for Proteomics and Transcriptomics Data
Front. Genet. 2016 Jan 7;6:351.
doi: 10.3389/fgene.2015.00351 link

Rave-Fränk M., Tehrany N., Kitz J., Leu M., Weber H.E., Burfeind P., Schliephake H., Canis M., Beissbarth T., Reichardt H.M., Wolff H.A.
Prognostic value of CXCL12 and CXCR4 in inoperable head and neck squamous cell carcinoma
Strahlenther. Onkol. 2016 jan;192(1):47-54
doi: 10.1007/s00066-015-0892-5 link

Kel, A.E., Stegmaier, P., Valeev, T., Koschmann, J., Poroikov, V., Kel-Margoulis, O.V. and Wingender, E.:
Multi-omics “upstream analysis” of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer
EuPA Open Proteomics 13, 1-13 (2016)
doi:10.1016/j.euprot.2016.09.002  link

Dang, T.K.L., Meckbach, C., Tacke, R., Waack, S. and Gültas, M.:
A novel sequence-based feature for the identification of DNA-binding sites in proteins using Jensen–Shannon Divergence
Entropy 18, 379 (2016)
doi:10.3390/e18100379  link

Shelest, E., Wingender, E. and Linde, J. (eds.):
Systems Biology of Transcription Regulation
Frontiers Media, Lausanne 2016 (ISBN 978-2-88919-967-9).
doi: 10.3389/978-2-88919-967-9  link

Shelest, E. and Wingender, E.:
Editorial: Systems biology of transcription regulation
Front. Genet. 7, 124 (2016).
doi: 10.3389/fgene.2016.00124  link

Haubrock, M., Hartmann, F. and Wingender, E.:
NF-Y binding site architecture defines a c-Fos targeted promoter class
PLoS ONE 11, e0160803 (2016)
doi:10.1371/journal.pone.0160803  link

Hua, X., Chen, L., Wang, J., Li, J. and Wingender, E.:
Identifying cell-specific microRNA transcriptional start sites
Bioinformatics 32, 2403-2410 (2016)
doi: 10.1093/bioinformatics/btw171  link

Wlochowitz, D., Haubrock, M., Arackal, J., Bleckmann, A., Wolff, A., Beissbarth, T., Wingender, E. and Gültas, M.:
Computational identification of key regulators in two different colorectal cancer cell lines
Front. Genet. 7, 42 (2016).
doi: 10.3389/fgene.2016.00042  link

Zeidler, S., Meckbach, C., Tacke, R., Raad, F. S., Roa, A., Uchida, S., Zimmermann, W. H., Wingender, E. and Gültas, M.:
Computational detection of stage-specific transcription factor clusters during heart development
Front. Genet. 7, 33 (2016).
doi: 10.3389/fgene.2016.00033  link

Schirmer, M.A., Lüske, C., Roppel, S., Schaudinn, A., Zimmer, C., Pflüger, R., Haubrock, M., Rapp, J., Güngör, C., Bockhorn, M., Hackert, T., Hank, T., Strobel, O., Werner, J., Izbicki, J.R., Johnsen, S.A., Gaedcke, J., Brockmöller, J. and Ghadimi, B.M.:
Relevance of Sp binding site polymorphism in WWOX for treatment outcome in pancreatic cancer
J. Natl. Cancer Inst. 108, djv387 (2016).
doi: 10.1093/jnci/djv387  link


Meckbach, C., Tacke, R., Hua, X., Waack, S., Wingender, E. and Gültas, M.:
PC-TraFF: Identification of potentially collaborating transcription factors using pointwise mutual information
BMC Bioinformatics 16, 400 (2015).
doi: 10.1186/s12859-015-0827-2  link

Schmitt-Engel, C., Schultheis, D., Schwirz, J., Ströhlein, N., Troelenberg, N., Majumdar, U., Dao, V.A., Grossmann, D., Richter, T., Tech, M., Dönitz, J., Gerischer, L., Theis, M., Schild, I., Trauner, J., Koniszewski, N.D., Küster, E., Kittelmann, S., Hu, Y., Lehmann, S., Siemanowski, J., Ulrich, J., Panfilio, K.A., Schröder, R., Morgenstern, B., Stanke, M., Buchhholz, F., Frasch, M., Roth, S., Wimmer, E.A., Schoppmeier, M., Klingler, M. and Bucher, G.:
The iBeetle large-scale RNAi screen reveals gene functions for insect development and physiology
Nat. Commun. 6, 7882 (2015).
doi: 10.1038/ncomms8822  link

Bhar, A., Haubrock, M., Mukhopadhyay, A. and Wingender, E:
Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes
BMC Bioinformatics 16, 200 (2015).
doi:10.1186/s12859-015-0635-8  link

Koschmann, J., Bhar, A., Stegmaier,P., Kel, A.E. and Wingender, E.:
“Upstream Analysis”: An integrated promoter-pathway analysis approach to causal interpretation of microarray data
Microarrays 4, 270-286 (2015).
doi:10.3390/microarrays4020270  link

Dalila, N., Brockmöller, J., Tzvetkov, M.V., Schirmer, M., Haubrock, M., and Vormfelde, S.V.:
Impact of mineralocorticoid receptor polymorphisms on urinary electrolyte excretion with and without diuretic drugs
Pharmacogenomics 16, 115-127 (2015).
doi:10.2217/pgs.14.163  link

Wingender, E., Schoeps, T., Haubrock, M. and Dönitz, J.:
TFClass: a classification of human transcription factors and their rodent orthologs
Nucleic Acids Res. 43, D97-D102 (2015).
doi: 10.1093/nar/gku1064  link

Dong, Z., Wang, K., Dang, T.K.L., Gültas, M., Welter, M., Stanke, M. and Waack, S.:
CRF-based moels of protein surfaces improve protein-protein interaction site predictions
BMC Bioinformatics 15, 277 (2014).
doi:10.1186/1471-2105-15-277  link

Dönitz, J. and Wingender, E.:
EndoNet: An information resource about the intercellular signaling network
BMC Systems Biol. 8, 49 (2014).
doi:10.1186/1752-0509-8-49  link

Gültas, M., Düzügun, G., Herzog, S.J., Meckbach, C., Wingender, E. and Waack, S.:
Quantum Coupled Mutation Finder: predicting functionally or strucurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming
BMC Bioinformatics 15, 96 (2014).
doi:10.1186/1471-2105-15-96  link

Fuchs, M.:
Equivariant K-homology of Bianchi groups in the case of non-trivial class group
Münster Journal of Mathematics 7, 589 (2014). link


Eggeling, R., Gohr, A., Bourguignon, P.-Y., Wingender, E. and Grosse, I.:
Inhomogeneous Parsimonious Markov Models
Lecture Notes in Computer Science, LNAI 8188, 321-336 (2013). link

Deyneko, I. V., Kel, A. E., Kel-Margoulis, O. V., Deineko, E. V., Wingender, E. and Weiss, S.:
MatrixCatch – a novel tool for the recognition of composite regulatory elements in promoters
BMC Bioinformatics 14, 241 (2013). link

Sugii, M., Wingender, E. and Matsuno, H.:
Petri net modeling of oscillatory processes in the activation of cell cycle proteins
Proceedings of the 28th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 2013), June 30-July 3, 2013, Yeosu, Korea, pp. 76-79 (2013). pdf

Dönitz, J., Großmann, D., Schild, I., Schmitt-Engel, C., Bradler, S., Prpic, N.-M. and Bucher, G.:
TrOn: An anatomical ontology for the beetle Tribolium castaneum
PLoS ONE 8, e70695 (2013). link

Fuchs, M., Beissbarth, T., Wingender, E. and Jung, K.:
Connecting high-dimensional mRNA and miRNA expression data for  binary medical classification problems
Comput. Methods Programs Biomed. 111, 592-601 (2013). link

Bhar, A., Haubrock, M., Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S. and Wingender, E.:
Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell
Algorithms Mol. Biol. 8, 9 (2013). link

Stegmaier, P., Kel, A., Wingender, E. and Borlak, J.:
A discriminative approach for unsupervised clustering of DNA sequence motifs
PLoS Comput. Biol. 9, e1002958 (2013). link

Wingender, E.:
Criteria for an updated classification of human transcription factor DNA-binding domains
J. Bioinform. Comput. Biol. 11, 1340007 (2013). link

Wingender, E., Schoeps, T. and Dönitz, J.:
TFClass: An expandable hierarchical classification of human transcription factors
Nucleic Acids Res. 41, D165-D170 (2013). link

Haubrock, M., Li, J. and Wingender, E.:
Using potential master regulator sites and paralogous expansion to construct tissue-specific transcriptional networks
BMC Syst. Biol. 6 Suppl. 2, S15 (2012). link

Dönitz, J. and Wingender, E.:
The ontology-based answers (OBA) service: A connector for embedded usage of ontologies in applications
Front. Gene. 3, 197 (2012). link

Gültas, M., Haubrock, M., Tüysüz, N. and Waack, S.:
Coupled Mutation Finder: A new entropy-based method quantifying phylogenetic noise for the detection of compensatory mutations
BMC Bioinformatics 13, 225 (2012). link

Bhar, A., Haubrock, M., Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S. and Wingender, E.:
delta-TRIMAX: Extracting Triclusters and Analysing Coregulation in Time Series Gene Expression Data
Raphael, B. and Tang, J. (eds.) Algorithms in Bioinformatics, 12th International Workshop, WABI 2012, Ljubljana, Slovenia, September 10-12, 2012, Proceedings, LNBI 7534, 165-177 (2012). link

Li, J., Hua, X., Haubrock, M., Wang, J. and Wingender, E.:
The architecture of the gene regulatory networks of different tissues
Bioinformatics 28, i509-514 (2012). link


Bender C., Heyde vd S., henjes F., Wiemann S., Korf U., Beißbart T.:
Inferring signalling networks from longitudinal data using sampling based approaches in The R-packages'dedepn'.
BMC Bioniformatics 12, 291(2011)
doi: 10.1186/1471-2105-12-291

Brand-Saberi, B., Wingender, E., Rienhoff, O. and Viebahn, C.:
Presenting human embryology in an international open-access reference centre (HERC)
The Human Embryo, Yamada, S (ed.), Ch. 2, InTech (2011). link

Wang, J., Haubrock, M., Cao, K.-M., Hua, X., Zhang, C.-Y., Wingender, E. and Li, J.:
Regulatory coordination of clustered microRNAs based on microRNA-transcription factor regulatory network
BMC Syst. Biol. 5, 199 (2011). link

Ante, M., Wingender, E. and Fuchs, M.:
Integration of gene expression data with prior knowledge for network analysis and validation
BMC Res. Notes 4, 520 (2011). link

Goemann, B., Wingender, E. and Potapov, A. P.:
Topological peculiarities of mammalian networks with different functionalities: transcription, signal transduction and metabolic networks
Network Biol. 1, 134-148 (2011). link

Halacheva, V., Fuchs, M., Dönitz, J., Reupke, T., Püschel, B. and Viebahn, C.:
Planar cell movements and oriented cell division during early primitive streak formation in the mammalian embryo
Dev. Dyn. 240, 1905-1916 (2011). link

Stegmaier, P., Voss, N., Meier, T., Kel, A., Wingender, E. and Borlak, J.:
Advanced computational biology methods identifymolecular switches for malignancyin an EGF mouse model of liver cancer
PLoS ONE 6 (3), e17738 (2011). link

Rahm, A. and Fuchs, M.:
The integral homology of PSL2 of imaginary quadratic integers with non-trivial class group
Journal of Pure and Applied Algebra, 215 (iss. 6), 1443-1472 (2011). link

Crass, T.:
Modellierungs- und Analysemethoden in der Systembiologie
In: Körner, M.-C. und Schöbel, A. (Hrsg.), Gene, Graphen, Organismen.
Shaker Verlag, Aachen, 3-59 (2010). link contents

Demir, E., Cary, M.P., Paley, S., Fukuda, K., Lemer, C., Vastrik, I., Wu, G., D'Eustachio, P., Schaefer, C., Luciano, J., Schacherer, F., Martinez-Flores, I., Hu, Z., Jimenez-Jacinto, V., Joshi-Tope, G., Kandasamy, K., Lopez-Fuentes, A.C., Mi, H., Pichler, E., Rodchenkov, I., Splendiani, A., Tkachev, S., Zucker, J., Gopinath, G., Rajasimha, H., Ramakrishnan, R., Shah, I., Syed, M., Anwar, N., Babur, O., Blinov, M., Brauner, E., Corwin, D., Donaldson, S., Gibbons, F., Goldberg, R., Hornbeck, P., Luna, A., Murray-Rust, P., Neumann, E., Reubenacker, O., Samwald, M., van Iersel, M., Wimalaratne, S., Allen, K., Braun, B., Whirl-Carrillo, M., Cheung, K.H., Dahlquist, K., Finney, A., Gillespie, M., Glass, E., Gong, L., Haw, R., Honig, M., Hubaut, O., Kane, D., Krupa, S., Kutmon, M., Leonard, J., Marks, D., Merberg, D., Petri, V., Pico, A., Ravenscroft, D., Ren, L., Shah, N., Sunshine, M., Tang, R., Whaley, R., Letovksy, S., Buetow, K.H., Rzhetsky, A., Schachter, V., Sobral, B.S., Dogrusoz, U., McWeeney, S., Aladjem, M., Birney, E., Collado-Vides, J., Goto, S., Hucka, M., Le Novère, N., Maltsev, N., Pandey, A., Thomas, P., Wingender, E., Karp, P.D., Sander, C. and Bader, G.D.:
The BioPAX community standard for pathway data sharing.
Nat. Biotechnol. 28, 935-942 (2010). link

Stegmaier, P., Krull, M., Voss, N., Kel, A. and Wingender, E.:
Molecular mechanistic associations of human diseases
BMC Syst. Biol. 4, 1024 (2010). link

 Wingender, E. (editor):
Biological Petri Nets
Bioinformation Systems e.V., Göttingen & IOS Press, Amsterdam (2010). link


Goemann, B., Potapov, A. P., Ante, M. and Wingender, E.:
Comparative analysis of topological patterns in different mammalian networks
Genome Inf. Ser. 23, 32 (2009). link

Goemann, B., Wingender, E. and Potapov, A. P.:
An approach to evaluate the topological significance of motifs and other patterns in regulatory networks
BMC Syst. Biol. 3, 53 (2009). link

Kel, A., Voss, N., Valeev, T., Stegmaier, P., Kel-Margoulis, O. and Wingender,E.:
ExPlain: finding upstream drug targets in disease gene regulatory networks
SAR QSAR Environ Res. 19, 481-494 (2008). link

Michael, H., Hogan, J., Kel, A., Kel-Margoulis, O., Schacherer, F. and Wingender, E.:
Building a knowledge base for systems pathology
Brief. Bioinformatics 9, 518-31 (2008).
doi:10.1093/bib/bbn038 link

Potapov, A. P., Goemann, B. and Wingender, E.:
The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks
BMC Bioinformatics 9, 227 (2008). link

Potapov, A. P.:
Signal transduction and gene regulation networks
In: Analysis of Biological Networks (Eds Junker, B.H. and Schreiber, F.)
Hoboken, New Jersey: Wiley-Interscience. 183-206 (2008).

Wingender, E.:
TRANSFAC project as an example of framework technology that supports
the analysis of genomic regulation

Brief. Bioinformatics 9, 326-332 (2008). link

Zubarev, R. A., Nielsen, M. L., Fung, E. M., Savitski, M. M., Kel-Margoulis, O., Wingender, E. and Kel, A.:
Identification of dominant signaling pathways from proteomics expression data
J. Proteom. 71, 89-96 (2008). link

Dönitz, J., Goemann, B., Lizé, M., Michael, H., Sasse, N., Wingender, E. and Potapov, A. P.:
EndoNet: An information resource about regulatory networks of cell-to-cell communication
Nucleic Acids Res. 36, D689-D694 (2008). link


Wingender, E., Hogan, J., Schacherer, F., Potapov, A.P. and Kel-Margoulis, O.:
Integrating pathway data for systems pathology
In Silico Biol. 7 S1, 03 (2007). link

Degenhardt, J., Haubrock, M., Dönitz, J., Wingender, E. and Crass, T.:
DEEP--A tool for differential expression effector prediction
Nucleic Acids Res. 35, W619-W624 (2007). link

Wingender, E., Crass, T., Hogan, J. D, Kel, A. E., Kel-Margoulis, O. V. and Potapov, A. P.:
Integrative content-driven concepts for bioinformatics "beyond the cell"
J. Biosci. 32, 169-180 (2007). pdf

Seibel P.N., Kruger J., Hartmeier S., Schwarzer K., Löwenthal K., Mersch H., Dandekar T., Giegerich R.:
XML schemas for common bioinformatic data types and their application in
workflow systems

BMC Bioinformatics 7, 490 (2006). link

Kel, A., Voss, N., Jauregui, R., Kel-Margoulis, O. and Wingender, E.:
Beyond microarrays: Find key transcription factors controlling signal
transduction pathways

BMC Bioinformatics 7(Suppl. 2), S13 (2006). link

Sauer, T.:
Evaluierung des phylogenetischen Footprintings und dessen Anwendung zur
verbesserten Vorhersage von Transkriptionsfaktor-Bindestellen

PhD Thesis, Georg August University Göttingen (2006). link pdf

Waleev, T., Shtokalo, D., Konovalova, T., Voss, N., Cheremushkin, E., Stegmaier,
P., Kel-Margoulis, O., Wingender, E. and Kel, A.:
Composite Module Analyst: identification of transcription factor binding
site combinations using genetic algorithm

Nucleic Acids Res. 34, W541-W545 (2006). link

Shelest, E.:
Genetic networks of antibacterial responses of eukaryotic cells. Bioinformatics analysis and modeling
PhD Thesis, Technical University of Braunschweig (2006). link pdf

Potapov, A. P. and Wingender, E.:
Mining the genome and regulatory networks
Genome Biol. 7, 309 (2006). link

Kel, A., Konovalova, T., Waleev, T., Cheremushkin, E., Kel-Margoulis, O. and Wingender, E.:
Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations
Bioinformatics 22, 1190-1197 (2006). link

Sauer, T., Shelest, E. and Wingender, E.:
Evaluating phylogenetic footprinting for human-rodent comparisons
Bioinformatics 22, 430-437 (2006). link

Krull, M., Pistor, S., Voss, N., Kel, A., Reuter, I., Kronenberg, D., Michael, H., Schwarzer, K., Potapov, A., Choi, C., Kel-Margoulis, O. and Wingender, E.:
TRANSPATH®: An Information Resource for Storing and Visualizing Signaling Pathways and their Pathological Aberrations
Nucleic Acids Res. 34, D546-D551 (2006). link

Potapov, A., Liebich, I., Dönitz, J., Schwarzer, K., Sasse, N., Schoeps, T., Crass, T. and Wingender, E.:
EndoNet: An information resource about endocrine networks
Nucleic Acids Res. 34, D540-D545 (2006). link

Chen, X., Wu, J.-m., Hornischer, K., Kel, A. and Wingender, E.:
TiProD: The Tissue-specific Promoter Database
Nucleic Acids Res. 34, D104-D107 (2006). link


Potapov, A. P., Voss, N., Sasse, N. and Wingender, E.:
Topology of mammalian transcription networks
Genome Inf. Ser. 16, 270-278 (2005). pdf

Krull, M. and Wingender, E.:
Eukaryotic regulatory sequences.
Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics, Dunn, M. J., Jorde, L. B., Little, P. F. and Subramaniam S. (eds.), John Wiley & Sons, Ltd., UK (2005). link

Kel-Margoulis, O., Matys, V., Choi, C., Reuter, I., Krull, M., Potapov, A.P., Voss, N., Liebich, I., Kel, A., Wingender, E.:
Databases on Gene Regulation
Information Processing and Living Systems, Bajic, V. B. and Wee, T. T. (eds.), Imperial College Press, London, 709-727 (2005).

Kel, A., Kel-Margoulis, O., Borlak J., Tchekmenev, D., Wingender, E.:
Databases and tools for in silico analysis of regulation of gene expression
Handbook of Toxicogenomics, Borlak, J. (ed.), Wiley-VCH, Weinheim, 253-290 (2005). pdf

Guldener, U., Munsterkotter, M., Kastenmuller, G., Strack, N., van Helden, J., Lemer, C., Richelles, J., Wodak, S. J., Garcia-Martinez, J., Perez-Ortin, J. E., Michael, H., Kaps, A., Talla, E., Dujon, B., Andre, B., Souciet, J. L., De Montigny, J., Bon, E., Gaillardin, C. and Mewes, H. W.:
CYGD: the Comprehensive Yeast Genome Database.
Nucleic Acids Res. 33, D364-368 (2005). Medline

Kel, A., Tikunov, Y., Voss, N., Borlak, J. and Wingender, E.:
Application of kernel method to reveal subtypes of TF binding motifs. Causal analysis of gene expression data
Regulatory Genomics: RECOMB 2004 International Workshop, RRG 2004, San Diego, Ca, USA, March 26-27, 2004, Revised Selected Papers. Eleazar Eskin, Chris Workman (eds.), Lecture Notes in Computer Science 3318, 42-51 (2005). pdf

Shelest, E. and Wingender, E.:
Construction of predictive promoter models on the example of antibacterial response of human epithelial cells
Theor. Biol. Med. Model. 2, 2 (2005).link

Michael, H., Chen, X., Fricke, E., Haubrock, M., Ricanek, R. and Wingender, E.:
Deriving an ontology for human gene expression sources from the CYTOMER® database on human organs and cell types.
In Silico Biol. 5, 0007 (2004). link

Choi, C., Crass, T., Kel, A., Kel-Margoulis, O., Krull, M., Pistor, S., Potapov, A., Voss, N. and Wingender, E.:
Consistent re-modeling of signaling pathways and its implementation in the TRANSPATH database
Genome Inf. Ser. 15, 244-254 (2004). pdf

Stegmaier, P., Kel, A. E. and Wingender, E.:
Systematic DNA-binding domain classification of transcription factors
Genome Inf. Ser. 15, 276-286 (2004). pdf

Kel, A., Tikunov, Y., Voss, N. and Wingender, E.:
Recognition of multiple patterns in unaligned sets of sequences: comparison of kernel clustering method with other methods
Bioinformatics 20, 1512-1516 (2004). Medline

Choi, C., Krull, M., Kel-Margoulis, O., Pistor, S., Potapov, A., Voss, N. and Wingender, E.:
TRANSPATH® - a high quality database focused on signal transduction
Comparative Functional Genomics 5, 163-168 (2004). link

Hehl, R., Steffens, N. O. and Wingender, E.:
Isolation and analysis of gene regulatory sequences
Handbook of Plant Biotechnology, Christou, P. and Klee, H. (eds.), Wiley and Sons Ltd., 81-102 (2004).

Crass, T., Antes, I., Basekow, R., Bork, P., Buning, C., Christensen, M., Claussen, H., Ebeling, C., Ernst, P., Gailus-Durner, V., Glatting, K.H., Gohla, R., Gossling, F., Grote, K., Heidtke, K., Herrmann, A., O'Keeffe, S., Kiesslich, O., Kolibal, S., Korbel, J.O., Lengauer, T., Liebich, I., Van Der Linden, M., Luz, H., Meissner, K., Von Mering, C., Mevissen, H.T., Mewes, H.W., Michael, H., Mokrejs, M., Muller, T., Pospisil, H., Rarey, M., Reich, J.G., Schneider, R., Schomburg, D., Schulze-Kremer, S., Schwarzer, K.,Sommer, I., Springstubbe, S., Suhai, S., Thoppae, G., Vingron, M., Warfsmann, J., Werner, T., Wetzler, D., Wingender, E. and Zimmer, R.:
The Helmholtz Network for Bioinformatics: an integrative web portal for bioinformatics resources.
Bioinformatics 20, 268-270 (2004). Medline


Shelest, E., Kel, A., Gößling, E. and Wingender, E.:
Prediction of potential C/EBP/NF-kappaB composite elements using matrix-based search methods.
In Silico Biol. 3, 0007 (2003). link

Wingender, E.:
TRANSFAC®, TRANSPATH® and CYTOMER® as starting points for an ontology of regulatory networks.
In Silico Biology 4, 0006 (2003). link

Kel, A.E., Goessling, E., Reuter, I., Cheremushkin, E., Kel-Margoulis, O.V. and Wingender, E.:
MATCH(TM): a tool for searching transcription factor binding sites in DNA sequences
Nucleic Acids Res. 31, 3576-3579 (2003). Medline

Kel-Margoulis, O.V., Tchekmenev D., Kel, A.E., Goessling, E., Hornischer, K., Lewicki-Potapov, B. and Wingender, E.:
Composition-sensitive analysis of the human genome for regulatory signals
In Silico Biology 3, 0013 (2003). link

Matys, V., Fricke, E., Geffers, R., Gößling, E., Haubrock, M., Hehl, R., Hornischer, K., Karas, D., Kel, A. E., Kel-Margoulis, O. V., Kloos, D.-U., Land, S., Lewicki-Potapov, B., Michael, H., Münch, R., Reuter, I., Rotert, S., Saxel, H., Scheer, M., Thiele, S. and Wingender, E.:
TRANSFAC®: transcriptional regulation, from patterns to profiles
Nucleic Acids Res. 31, 374-378 (2003). Medline

Münch, R., Hiller, K., Barg, H., Heldt, D., Linz, S., Wingender, E. and Jahn, D.:
PRODORIC: prokaryotic database of gene regulation
Nucleic Acids Res. 31, 266-269 (2003) Medline

Krull, M., Voss, N., Choi, V., Pistor, S., Potapov, A. and Wingender, E.:
TRANSPATH®: an integrated database on signal transduction and a tool for array analysis
Nucleic Acids Res. 31, 97-100 (2003). Medline

Potapov, A. P. and Wingender, E.:
Representing the architecture of signal transduction networks in an algebraic form: protein target finding.
Ann. N. Y. Acad. Sci. 973, 1-2 (2002).Medline

Kel-Margoulis, O. V., Ivanova, T., G., Wingender, E. and Kel A. E.:
Automatic annotation of genomic regulatory sequences by searching for composite clusters
Pac. Symp. Biocomput. 7, 187-198 (2002). pdf

Wingender, E.:
Modeling regulatory pathways with the use of the transfac system
Gene Function & Disease 3, 3-11 (2002). link

Liebich, I., Bode, J., Reuter, I. and Wingender, E.:
Evaluation of sequence motifs found in scaffold / matrix atteached regions (S/MARs)
Nucleic Acids Res. 30, 3433-3442 (2002). Medline

Kloos, D.-U., Choi, C. and Wingender, E.:
The TGF-beta Smad network: introducing bioinformatic tools
Trends Genet. 18, 96-103 (2002). Medline

Kel-Margoulis, O. V., Kel, A. E., Reuter, I., Deineko, I. V. and Wingender, E.:
TRANSCompel® – a database on composite regulatory elements in eukaryotic genes.
Nucleic Acids Res. 30, 332-334 (2002). Medline

Frisch, M., Frech, K., Klingenhoff, A., Quandt, K., Liebich, I. and Werner, T.:
In silico prediction of scaffold/matrix attachment regions in large genomic sequences.
Genome Res. 12, 349-354 (2002).Medline

Liebich, I., Bode, J., Frisch,M. and Wingender, E.:
S/MARt DB - A database on scaffold / matrix attached regions.
Nucleic Acids Res. 30, 372-374 (2002). Medline

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