Barbora Kozlíková

Vladimír Lazárik, Marco Agus, Barbora Kozlíková et al.

Analyzing how the publication records of scientists and research groups have evolved over the years is crucial for assessing their expertise since it can support the management of academic environments by assisting with career planning and evaluation. We introduce VizCV, a novel web-based end-to-end visual analytics framework that enables the interactive exploration of researchers' scientific trajectories. It incorporates AI-assisted analysis and supports automated reporting of career evolution. Our system aims to model career progression through three key dimensions: a) research topic evolution to detect and visualize shifts in scholarly focus over time, b) publication record and the corresponding impact, c) collaboration dynamics depicting the growth and transformation of a researcher's co-authorship network. AI-driven insights provide automated explanations of career transitions, detecting significant shifts in research direction, impact surges, or collaboration expansions. The system also supports comparative analysis between researchers, allowing users to compare topic trajectories and impact growth. Our interactive, multi-tab and multiview system allows for the exploratory analysis of career milestones under different perspectives, such as the most impactful articles, emerging research themes, or obtaining a detailed analysis of the contribution of the researcher in a subfield. The key contributions include AI/ML techniques for: a) topic analysis, b) dimensionality reduction for visualizing patterns and trends, c) the interactive creation of textual descriptions of facets of data through configurable prompt generation and large language models, that include key indicators, to help understanding the career development of individuals or groups.

Pedro Hermosilla, Marco Schäfer, Matěj Lang et al.

Proteins perform a large variety of functions in living organisms, thus playing a key role in biology. As of now, available learning algorithms to process protein data do not consider several particularities of such data and/or do not scale well for large protein conformations. To fill this gap, we propose two new learning operations enabling deep 3D analysis of large-scale protein data. First, we introduce a novel convolution operator which considers both, the intrinsic (invariant under protein folding) as well as extrinsic (invariant under bonding) structure, by using $n$-D convolutions defined on both the Euclidean distance, as well as multiple geodesic distances between atoms in a multi-graph. Second, we enable a multi-scale protein analysis by introducing hierarchical pooling operators, exploiting the fact that proteins are a recombination of a finite set of amino acids, which can be pooled using shared pooling matrices. Lastly, we evaluate the accuracy of our algorithms on several large-scale data sets for common protein analysis tasks, where we outperform state-of-the-art methods.

Katarína Furmanová, Adam Jurčík, Barbora Kozlíková et al.

When studying multi-body protein complexes, biochemists use computational tools that can suggest hundreds or thousands of their possible spatial configurations. However, it is not feasible to experimentally verify more than only a very small subset of them. In this paper, we propose a novel multiscale visual drilldown approach that was designed in tight collaboration with proteomic experts, enabling a systematic exploration of the configuration space. Our approach takes advantage of the hierarchical structure of the data -- from the whole ensemble of protein complex configurations to the individual configurations, their contact interfaces, and the interacting amino acids. Our new solution is based on interactively linked 2D and 3D views for individual hierarchy levels and at each level, we offer a set of selection and filtering operations enabling the user to narrow down the number of configurations that need to be manually scrutinized. Furthermore, we offer a dedicated filter interface, which provides the users with an overview of the applied filtering operations and enables them to examine their impact on the explored ensemble. This way, we maintain the history of the exploration process and thus enable the user to return to an earlier point of the exploration. We demonstrate the effectiveness of our approach on two case studies conducted by collaborating proteomic experts.