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1.
Ali, M. et al.: GraphCompass: Spatial metrics for differential analyses of cell organization across conditions. Bioinformatics 40, i548-i557 (2024)
2.
Astaburuaga-García, R.* et al.: RUCova: Removal of unwanted covariance in mass cytometry data. Bioinformatics 40:btae669 (2024)
3.
Dorešić, D. ; Grein, S.* & Hasenauer, J.: Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data. Bioinformatics 40, i558-i566 (2024)
4.
Hagenberg, J. et al.: Longmixr: A tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types. Bioinformatics 40:btae137 (2024)
5.
Heumos, S.* et al.: Cluster-efficient pangenome graph construction with nf-core/pangenome. Bioinformatics 40:btae609 (2024)
6.
Stock, M. ; Popp, N. ; Fiorentino, J. & Scialdone, A.: Topological benchmarking of algorithms to infer Gene Regulatory Networks from Single-Cell RNA-seq Data. Bioinformatics 40:btae267 (2024)
7.
Wang, J.* ; Horlacher, M. ; Cheng, L.* & Winther, O.*: DeepLocRNA: An interpretable deep learning model for predicting RNA subcellular localisation with domain-specific transfer-learning. Bioinformatics 40:btae065 (2024)
8.
Alamoudi, E.* et al.: FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes. Bioinformatics 39:btad674 (2023)
9.
Hecker, D.* ; Behjati Ardakani, F.* ; Karollus, A.* ; Gagneur, J. & Schulz, M.H.*: The adapted activity-by-contact model for enhancer-gene assignment and its application to single-cell data. Bioinformatics 39:btad062 (2023)
10.
Heumos, L. et al.: mlf-core: A framework for deterministic machine learning. Bioinformatics 39:8 (2023)
11.
Huth, M. et al.: Accessibility of covariance information creates vulnerability in Federated Learning frameworks. Bioinformatics 39:9 (2023)
12.
Kardell, O. ; Breimann, S.* & Hauck, S.M.: mpwR: An R package for comparing performance of mass spectrometry-based proteomic workflows. Bioinformatics 39:3 (2023)
13.
Schälte, Y. et al.: pyPESTO: A modular and scalable tool for parameter estimation for dynamic models. Bioinformatics 39:btad711 (2023)
14.
Vilov, S. & Heinig, M.: DeepSom: A CNN-based approach to somatic variant calling in WGS samples without a matched normal. Bioinformatics 39:9 (2023)
15.
Weidner, L. ; Hemmler, D. ; Rychlik, M.* & Schmitt-Kopplin, P.: DBDIpy: A Python library for processing of untargeted datasets from real-time plasma ionization mass spectrometry. Bioinformatics 39:2 (2023)
16.
Yuzeir, A.* et al.: IntestLine: A shiny-based application to map the rolled intestinal tissue onto a line. Bioinformatics 39:2 (2023)
17.
Chetnik, K.* et al.: maplet: An extensible R toolbox for modular and reproducible metabolomics pipelines. Bioinformatics 38, 1168-1170 (2022)
18.
Habermann, D.* et al.: HAMdetector: A Bayesian regression model that integrates information to detect HLA-associated mutations. Bioinformatics 38, 2428-2436 (2022)
19.
Ogris, C. ; Castresana-Aguirre, M.* & Sonnhammer, E.L.L.*: PathwAX II: Network-based pathway analysis with interactive visualization of network crosstalk. Bioinformatics 38, 2659-2660 (2022)
20.
Salzer, L. ; Witting, M. & Schmitt-Kopplin, P.: MobilityTransformR: An R package for effective mobility transformation of CE-MS data. Bioinformatics 38, 4044-4045 (2022)
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