Marco Schäfer

dblp:268/4422 · DBLP profile ↗
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4ranked-venue papers
1as first author
4since 2021 · last 2024
0000-0003-3854-6415ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 3 · 1 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › multi-view visualization
coordinated multiple views
0.812024
InVADo: Interactive Visual Analysis of Molecular Docking Data · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics › visual analytics
interactive visual analysis
0.812024
InVADo: Interactive Visual Analysis of Molecular Docking Data · IEEE Trans. Vis. Comput. Graph. 2024
Computer vision › 3D vision
geometric deep learning
0.512021
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures · ICLR 2021
Bioinformatics and computational biology › protein structure analysis
protein structure learning
0.512021
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures · ICLR 2021
Bioinformatics and computational biology
protein structure prediction
0.512021
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures · ICLR 2021
Bioinformatics and computational biology › structural bioinformatics
protein-ligand interaction analysis
0.212024
InVADo: Interactive Visual Analysis of Molecular Docking Data · IEEE Trans. Vis. Comput. Graph. 2024
Bioinformatics and computational biology
structural biology
0.212024
InVADo: Interactive Visual Analysis of Molecular Docking Data · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

spatial clustering · 1.5post-docking analysis · 1.5filtering · 1.5pooling · 1.0graph neural network · 1.0convolution · 1.0
YearPublicationVenuePosition
2024 InVADo: Interactive Visual Analysis of Molecular Docking Data
abstract
Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.
Marco Schäfer, Nicolas Brich, Jan Byska, Sérgio M. Marques, David Bednar, Philipp Thiel, Barbora Kozlíková, Michael Krone
IEEE Trans. Vis. Comput. Graph.1
2021 Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
Pedro Hermosilla, Marco Schäfer, Matej Lang, Gloria Fackelmann, Pere-Pau Vázquez, Barbora Kozlíková, Michael Krone, Tobias Ritschel 0001, Timo Ropinski
ICLR2
2021 Analyzing the similarity of protein domains by clustering Molecular Surface Maps
Karsten Schatz, Florian Frieß, Marco Schäfer, Patrick C. F. Buchholz, Jürgen Pleiss, Thomas Ertl, Michael Krone
Comput. Graph.3
2021 Visual Analysis of Large-Scale Protein-Ligand Interaction Data
abstract
Abstract When studying protein‐ligand interactions, many different factors can influence the behaviour of the protein as well as the ligands. Molecular visualisation tools typically concentrate on the movement of single ligand molecules; however, viewing only one molecule can merely provide a hint of the overall behaviour of the system. To tackle this issue, we do not focus on the visualisation of the local actions of individual ligand molecules but on the influence of a protein and their overall movement. Since the simulations required to study these problems can have millions of time steps, our presented system decouples visualisation and data preprocessing: our preprocessing pipeline aggregates the movement of ligand molecules relative to a receptor protein. For data analysis, we present a web‐based visualisation application that combines multiple linked 2D and 3D views that display the previously calculated data The central view, a novel enhanced sequence diagram that shows the calculated values, is linked to a traditional surface visualisation of the protein. This results in an interactive visualisation that is independent of the size of the underlying data, since the memory footprint of the aggregated data for visualisation is constant and very low, even if the raw input consisted of several terabytes.
Karsten Schatz, Juan José Franco-Moreno, Marco Schäfer, Alexander S. Rose, Valerio Ferrario, Jürgen Pleiss, Pere-Pau Vázquez, Thomas Ertl, Michael Krone
Comput. Graph. Forum3