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1.
El Omari, M.* ; El Maroufy, H.* & Fuchs, C.: Non parametric estimation for fractional diffusion processes with random effects. Statistics, accepted (2019)
2.
Krautenbacher, N. et al.: A strategy for high-dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological, and environmental factors. Allergy, accepted (2019)
3.
Wille, K.* et al.: High risk of recurrent venous thromboembolism in BCR-ABL-negative myeloproliferative neoplasms after termination of anticoagulation. Ann. Hematol. 98, 93-100 (2019)
4.
Neschen, S. et al.: Impact of brain fatty acid signaling on peripheral insulin action in mice. Exp. Clin. Endocrinol. Diabet. 126, accepted (2018)
5.
Dirmeier, S.* ; Fuchs, C. ; Müller, N.S. & Theis, F.J.: netReg: Network-regularized linear models for biological association studies. Bioinformatics 34, 896-898 (2017)
6.
Guinney, J.* et al.: Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: Development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol. 18, 132-142 (2017)
7.
Krautenbacher, N. ; Theis, F.J. & Fuchs, C.: Correcting classifiers for sample selection bias in two-phase case-control studies. Comput. Math. Methods Med. 2017:7847531 (2017)
8.
Seyednasrollah, F.* et al.: A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer. JCO Clin. Can. Inform. 1, 1-15 (2017)
9.
Köferle, A.* et al.: CORALINA: A universal method for the generation of gRNA libraries for CRISPR-based screening. BMC Genomics 17:917 (2016)
10.
Kondofersky, I. ; Theis, F.J. & Fuchs, C.: Inferring catalysis in biological systems. IET Syst. Biol. 10, 210-218 (2016)
11.
Kondofersky, I. et al.: Three general concepts to improve risk prediction: Good data, wisdom of the crowd, recalibration. F1000 Res. 5:2671 (2016)
12.
Illner, K. et al.: Model selection using limiting distributions of second-order blind source separation algorithms. Signal Process. 113, 95-103 (2015)
13.
Kaufmann, S.* et al.: Inter-chromosomal contact networks provide insights into mammalian chromatin organization. PLoS ONE 10:e0126125 (2015)
14.
Kondofersky, I. ; Fuchs, C. & Theis, F.J.: Identifying latent dynamic components in biological systems. IET Syst. Biol. 9, 193-203 (2015)
15.
Sadic, D.* et al.: Atrx promotes heterochromatin formation at retrotransposons. EMBO Rep. 16, 836-850 (2015)
16.
Albrecht, E. et al.: Metabolite profiling reveals new insights into the regulation of serum urate in humans. Metabolomics 10, 141-151 (2014)
17.
Bajikar, S.S.* ; Fuchs, C. ; Roller, A. ; Theis, F.J. & Janes, K.A.*: Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles. Proc. Natl. Acad. Sci. U.S.A. 111, E626-E635 (2014)
18.
Fuchs, C.: Mische und herrsche. Laborpraxis 38, 20-22 (2014)
19.
Illner, K. ; Fuchs, C. & Theis, F.J.: Bayesian blind source separation for data with network structure. J. Comput. Biol. 21, 855-865 (2014)
20.
Illner, K. ; Fuchs, C. & Theis, F.J.: Bayesian blind source separation applied to the lymphocyte pathway. In: Gillli, M.* ; Gonzalez-Rodriguez, G.* ; Nieto-Reyes, A.* [Eds.]: Proccedings (21st International Conference on Computational Statistics (COMPSTAT2014), 19-22 August 2014, Geneva, Switzerland). 2014. 625-632