A Structure-Based Analysis of Single Molecule Force Spectroscopy (SMFS) Data for Bacteriorhodopsin and Four Mutants. - Annalisa Marsico; K. Tanuj Sapra; Daniel J. Muller; Michael Schroeder 001; Dirk Labudde (CompLife, 2006)

Pattern Recognition of Single-Molecule Force Spectroscopy Data. - Dirk Labudde; Annalisa Marsico; K. Tanuj Sapra; Michael Schroeder 001 (ER Workshops, 2007)

2D Visualisation of SMFS Data on Membrane Proteins. - Annalisa Marsico; Kerstin Vanselow; Jing Wang; Dirk Labudde (ER (Tutorials, Posters, Panels & Industrial Contributions), 2007)

Understanding of SMFS Barriers by Means of Energy Profiles. - Frank Dressel; Annalisa Marsico; Anne Tuukkanen; Michael Schroeder 001; Dirk Labudde (German Conference on Bioinformatics, 2007)

A novel pattern recognition algorithm to classify membrane protein unfolding pathways with high-throughput single-molecule force spectroscopy. - Annalisa Marsico; Dirk Labudde; K. Tanuj Sapra; Daniel J. Muller; Michael Schroeder 001 (Bioinform., 2007)

Structural fragment clustering reveals novel structural and functional motifs in alpha-helical transmembrane proteins. - Annalisa Marsico; Andreas Henschel; Christof Winter; Anne Tuukkanen; Boris Vassilev; Kerstin Scheubert; Michael Schroeder 001 (BMC Bioinform., 2010)

MeMotif: a database of linear motifs in α-helical transmembrane proteins. - Annalisa Marsico; Kerstin Scheubert; Anne Tuukkanen; Andreas Henschel; Christof Winter; Rainer Winnenburg; Michael Schroeder 001 (Nucleic Acids Res., 2010)

pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks. - Stefan Budach; Annalisa Marsico (Bioinform., 2018)

Graph Convolutional Networks Improve the Prediction of Cancer Driver Genes. - Roman Schulte-Sasse; Stefan Budach; Denes Hnisz; Annalisa Marsico (ICANN (Workshop), 2019)

Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis. - Denise Thiel; Natasa Djurdjevac Conrad; Evgenia Ntini; Ria X. Peschutter; Heike Siebert; Annalisa Marsico (BMC Bioinform., 2019)

Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. - Roman Schulte-Sasse; Stefan Budach; Denes Hnisz; Annalisa Marsico (Nat. Mach. Intell., 2021)

A systematic benchmark of machine learning methods for protein-RNA interaction prediction. - Marc Horlacher; Giulia Cantini; Julian Hesse; Patrick Schinke; Nicolas Goedert; Shubhankar Londhe; Lambert Moyon; Annalisa Marsico (Briefings Bioinform., 2023)

Deep Learning for RNA Regulation and Multidimensional Transcriptomics (Dagstuhl Seminar 24491). - Annalisa Marsico; Uwe Ohler; Igor Ulitsky; Kathi Zarnack; Charlotte Capitanchik (Dagstuhl Reports, 2024)

Random Walk Diffusion for Efficient Large-Scale Graph Generation. - Tobias Bernecker; Ghalia Rehawi; Francesco Paolo Casale; Janine Knauer-Arloth; Annalisa Marsico (Trans. Mach. Learn. Res., 2025)