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1.
Ratajczak, F. ; Joblin, M.* ; Ringsquandl, M.* & Hildebrandt, M.*: Task-driven knowledge graph filtering improves prioritizing drugs for repurposing. BMC Bioinformatics 23:84 (2022)
2.
Amrhein, L. & Fuchs, C.: stochprofML: Stochastic profiling using maximum likelihood estimation in R. BMC Bioinformatics 22:123 (2021)
3.
Waibel, D.J.E. ; Shetab Boushehri, S. & Marr, C.: InstantDL: An easy-to-use deep learning pipeline for image segmentation and classification. BMC Bioinformatics 22:103 (2021)
4.
Dony, L. ; He, F.* & Stumpf, M.P.H.*: Parametric and non-parametric gradient matching for network inference: A comparison. BMC Bioinformatics 20:52 (2019)
5.
Thiel, D.* et al.: Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis. BMC Bioinformatics 20:292 (2019)
6.
Keilwagen, J.* ; Hartung, F.* ; Paulini, M.* ; Twardziok, S.O. & Grau, J.*: Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi. BMC Bioinformatics 19:189 (2018)
7.
Molnos, S. et al.: pulver: An R package for parallel ultra-rapid p-value computation for linear regression interaction terms. BMC Bioinformatics 18:429 (2017)
8.
Hahn, K.R. ; Massopust, P. & Prigarin, S.M.*: A new method to measure complexity in binary or weighted networks and applications to functional connectivity in the human brain. BMC Bioinformatics 17:87 (2016)
9.
Karapiperis, C.* et al.: Brain Radiation Information Data Exchange (BRIDE): Integration of experimental data from low-dose ionising radiation research for pathway discovery. BMC Bioinformatics 17:212 (2016)
10.
Liu, Y. et al.: MetICA: Independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics. BMC Bioinformatics 17:114 (2016)
11.
Weinhold, L.* ; Wahl, S. ; Pechlivanis, S.* ; Hoffmann, P.* & Schmid, M.*: A statistical model for the analysis of beta values in DNA methylation studies. BMC Bioinformatics 17:480 (2016)
12.
Kristensen, D.M.* ; Saeed, U. ; Frishman, D. & Koonin, E.V.*: A census of α-helical membrane proteins in double-stranded DNA viruses infecting bacteria and archaea. BMC Bioinformatics 16:380 (2015)
13.
Feigelman, J. ; Theis, F.J. & Marr, C.: MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data. BMC Bioinformatics 15:240 (2014)
14.
Wahl, S. et al.: On the potential of models for location and scale for genome-wide DNA methylation data. BMC Bioinformatics 15:232 (2014)
15.
Buggenthin, F. et al.: An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy. BMC Bioinformatics 14:297 (2013)
16.
Hock, S. ; Hasenauer, J. & Theis, F.J.: Modeling of 2D diffusion processes based on microscopy data: Parameter estimation and practical identifiability analysis. BMC Bioinformatics 14:S7 (2013)
17.
Vehlow, C.* et al.: iVUN: Interactive Visualization of Uncertain biochemical reaction Networks. BMC Bioinformatics 14:S2 (2013)
18.
Petersen, A.-K. et al.: On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies. BMC Bioinformatics 13:120 (2012)
19.
van Wieringen, W.N.* et al.: Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses. BMC Bioinformatics 13:80 (2012)
20.
Forer, L.* et al.: CONAN: Copy number variation analysis software for genome-wide association studies. BMC Bioinformatics 11:318 (2010)
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