PuSH - Publikationsserver des Helmholtz Zentrums München

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1.
Angerer, P. ; Fischer, D.S. ; Theis, F.J. ; Scialdone, A. & Marr, C.: Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data. Bioinformatics 36, 4291-4295 (2020)
2.
Haselimashhadi, H.* et al.: Soft windowing application to improve analysis of high-throughput phenotyping data. Bioinformatics 36, 1492-1500 (2020)
3.
Schälte, Y. & Hasenauer, J.: Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation. Bioinformatics 36, 1, 551-559 (2020)
4.
Schmiester, L. ; Schälte, Y. ; Fröhlich, F. ; Hasenauer, J. & Weindl, D.: Efficient parameterization of large-scale dynamic models based on relative measurements. Bioinformatics 36, 594-602 (2020)
5.
Solovey, M. & Scialdone, A.: COMUNET: A tool to explore and visualize intercellular communication. Bioinformatics 36, 4296-4300 (2020)
6.
Do, K.T. ; Rasp, D.J.N.P. ; Kastenmüller, G. ; Suhre, K. & Krumsiek, J.: MoDentify: Phenotype-driven module identification in metabolomics networks at different resolutions. Bioinformatics 35, 532-534 (2019)
7.
Gilly, A. et al.: Very low-depth whole-genome sequencing in complex trait association studies. Bioinformatics 35, 2555-2561 (2019)
8.
Hamad, S. et al.: HitPickV2: A web server to predict targets of chemical compounds. Bioinformatics 35, 1239-1240 (2019)
9.
Hass, H.* et al.: Benchmark problems for dynamic modeling of intracellular processes. Bioinformatics 35, 3073-3082 (2019)
10.
Jeske, T. et al.: DEUS: An R package for accurate small RNA profiling based on differential expression of unique sequences. Bioinformatics 35, 4834-4836 (2019)
11.
Porubsky, D.* et al.: breakpointR: An R/Bioconductor package to localize strand state changes in Strand-seq data. Bioinformatics, accepted (2019)
12.
Villaverde, A.F.* ; Fröhlich, F. ; Weindl, D. ; Hasenauer, J. & Banga, J.R.*: Benchmarking optimization methods for parameter estimation in large kinetic models. Bioinformatics 35, 830-838 (2019)
13.
Ballnus, B. ; Schaper, S.* ; Theis, F.J. & Hasenauer, J.: Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering. Bioinformatics 34, 494-501 (2018)
14.
Klinger, E. ; Rickert, D. & Hasenauer, J.: pyABC: Distributed, likelihood-free inference. Bioinformatics 34, 3591-3593 (2018)
15.
Loos, C. ; Krause, S. & Hasenauer, J.: Hierarchical optimization for the efficient parametrization of ODE models. Bioinformatics 34, 4266-4273 (2018)
16.
Stapor, P. et al.: PESTO: Parameter EStimation TOolbox. Bioinformatics 34, 705-707 (2018)
17.
Stapor, P. ; Fröhlich, F. & Hasenauer, J.: Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis. Bioinformatics 34, 151-159 (2018)
18.
Chlis, N.-K. ; Wolf, F.A. & Theis, F.J.: Model-based branching point detection in single-cell data by K-Branches clustering. Bioinformatics 33, 3211-3219 (2017)
19.
Dirmeier, S.* ; Fuchs, C. ; Müller, N.S. & Theis, F.J.: netReg: Network-regularized linear models for biological association studies. Bioinformatics 34, 896-898 (2017)
20.
Fröhlich, F. ; Theis, F.J. ; Rädler, J.O.* & Hasenauer, J.: Parameter estimation for dynamical systems with discrete events and logical operations. Bioinformatics 33, 1049-1056 (2017)