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How did we get there? AI applications to biological networks and sequences

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Computers in Biology and Medicine 2025年 190卷 110064-110064页

作者： Anteghini, Marco Gualdi, Francesco Oliva, Baldo Structural Bioinformatics Lab Universitat Pompeu Fabra Barcelona Spain University of Bologna Bologna Italy Visual and Data-Centric Computing Zuse Institut Berlin Berlin Germany Lugano Switzerland

The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current state of AI-driven methodologies in genomics, proteomics, and systems biology. We discuss how machine learning algorithms, particularly deep learning models, have enhanced the accuracy and efficiency of embedding sequences, motif discovery, and the prediction of gene expression and protein structure. Additionally, we explore the integration of AI in the embedding and analysis of biological networks, including protein–protein interaction networks and multi-layered networks. By leveraging large-scale biological data, AI techniques have enabled unprecedented insights into complex biological processes and disease mechanisms. This work underlines the potential of applying AI to complex biological data, highlighting current applications and suggesting directions for future research to further explore AI in this rapidly evolving field. © 2025 The Authors

关键词： Adversarial machine learning

Improving the Identification of Layers in 3D Images of Ancient Papyrus Using Artificial Neural Networks

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Improving the Identification of Layers in 3D Images of Ancie...

IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Nicolas Klenert Finn Schwoerer Noushin Hajarolasvadi Siloé Bournez Tobias Arlt Heinz-Eberhard Mahnke Verena Lepper Daniel Baum Department of Visual and Data-Centric Computing Zuse Institute Berlin Berlin Germany Helmholtz-Zentrum Berlin für Materialien und Energie Berlin Germany Ägyptisches Museum und Papyrussammlung Staatliche Museen zu Berlin Berlin Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

关键词： Geometry Computer vision Three-dimensional displays Network topology Explainable AI Conferences Writing Reconstruction algorithms Topology Image reconstruction

A Stratification Matrix Viewer for Analysis of Neural Network data

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A Stratification Matrix Viewer for Analysis of Neural Networ...

2022 Eurographics Workshop on visual computing for Biology and Medicine, EG VCBM 2022

作者： Harth, Philipp Vohra, Sumit Udvary, Daniel Oberlaender, Marcel Hege, Hans-Christian Baum, Daniel Department of Visual and Data-Centric Computing Germany Max Planck Institute for Neurobiology of Behavior - Caesar Bonn Germany

ISBN: (纸本)9783038681779

The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network;(2) quantify connectivity features statistically;and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex. © 2022 The Author(s) © 2022 The Eurographics Association.

关键词： Matrix algebra

Automatic segmentation of tooth images: Optimization of multi-parameter image processing workflow

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Automatic segmentation of tooth images: Optimization of mult...

24th Eurographics Conference on visualization, EuroVis 2022

作者： Fogalli, Giovani Bressan Line, Sérgio Roberto Peres Baum, Daniel Department of Biosciences Piracicaba Dental School State University of Campinas Brazil Department of Visual and Data-Centric Computing Zuse Institute Berlin Germany

ISBN: (纸本)9783038681854

The development of specific algorithms in image processing are usually related to dataset characteristics. Those characteristics will influence the number of instructions required to solve a problem. Normally, the more complex a set of instructions is, the more parameters need to be set. Dealing with such degrees of freedom, sometimes leading to subjective decision making, is time-consuming and frequently leads to errors or sub-optimal results of the developed model. Here, we deal with a model for segmentation of masks of tooth images containing a pattern of bands called Hunter-Schreger Bands (HSB). They appear on tooth surface when lit from the side. This segmentation process is only one step of a pipeline whose overall goal is human biometric identification to be used, e.g., in forensics. The segmentation algorithm, which exploits the anisotropy of the image, uses several parameters and choosing the optimal combination of them is challenging. The aim of this work was to utilize visual data analysis tools to optimize the chosen parameters and to understand their influence on the performance of the algorithm. Our results reveal that a slightly better combination of parameter values can be found starting from the experimentally determined initial parameters. This approach can be repeatedly performed to achieve even better parameterizations. To more deeply understand the influence of the parameters on the final result, more sophisticated visual interaction tools will be explored in future work. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： Image segmentation

Geodesic B-score for improved assessment of knee osteoarthritis

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arXiv 2021年

作者： Ambellan, Felix Zachow, Stefan von Tycowicz, Christoph Visual and Data-centric Computing Zuse Institute Berlin Berlin Germany

Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions. Copyright © 2021, The Authors. All rights reserved.

关键词： Geodesy

Ridge Regression for Manifold-valued Time-Series with Application to Meteorological Forecast

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arXiv 2024年

作者： Nava-Yazdani, Esfandiar Department of Visual and Data-Centric Computing Zuse Institute Berlin Berlin Germany

We propose a natural intrinsic extension of the ridge regression from Euclidean spaces to general manifolds, which relies on Riemannian least-squares fitting, empirical covariance, and Mahalanobis distance. We utilize it for time-series prediction and apply the approach to forecast hurricane tracks and their wind *** Codes 53B, 53C (Primary), 62, 65D (Secondary) © 2024, CC BY.

关键词： Wind forecasting

FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems

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arXiv 2022年

作者： Weimann, Kuba Conrad, Tim O.F. Visual and Data-Centric Computing Zuse Institute Berlin Takustr. 7 Berlin14195 Germany

Recommender systems suffer from the cold-start problem whenever a new user joins the platform or a new item is added to the catalog. To address item cold-start, we propose to replace the embedding layer in sequential recommenders with a dynamic storage that has no learnable weights and can keep an arbitrary number of representations. In this paper, we present FELRec, a large embedding network that refines the existing representations of users and items in a recursive manner, as new information becomes available. In contrast to similar approaches, our model represents new users and items without side information and time-consuming finetuning, instead it runs a single forward pass over a sequence of existing representations. During item cold-start, our method outperforms similar method by 29.50%–47.45%. Further, our proposed model generalizes well to previously unseen datasets in zero-shot settings. The source code is publicly available at https://***/kweimann/FELRec. © 2022, CC BY.

关键词： Contrastive Learning

An Interactive Approach for Identifying Structure Definitions

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arXiv 2021年

作者： Mikula, Natalia Dörffel, Tom Baum, Daniel Hege, Hans-Christian Zuse Institute Berlin Visual and Data-Centric Computing Berlin14195 Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany

Our ability to grasp and understand complex phenomena is essentially based on recognizing structures and relating these to each other. For example, any meteorological description of a weather condition and explanation of its evolution recurs to meteorological structures, such as convection and circulation structures, cloud fields and rain fronts. All of these are spatiotemporal structures, defined by time-dependent patterns in the underlying fields. Typically, such a structure is defined by a verbal description that corresponds to the more or less uniform, often somewhat vague mental images of the experts. However, a precise, formal definition of the structures or, more generally, concepts is often desirable, e.g., to enable automated data analysis or the development of phenomenological models. Here, we present a systematic approach and an interactive tool to obtain formal definitions of spatiotemporal structures. The tool enables experts to evaluate and compare different structure definitions on the basis of data sets with time-dependent fields that contain the respective structure. Since structure definitions are typically parameterized, an essential part is to identify parameter ranges that lead to desired structures in all time steps. In addition, it is important to allow a quantitative assessment of the resulting structures simultaneously. We demonstrate the use of the tool by applying it to two meteorological examples: finding structure definitions for vortex cores and center lines of temporarily evolving tropical cyclones. Ideally, structure definitions should be objective and applicable to as many data sets as possible. However, finding such definitions, e.g., for the common atmospheric structures in meteorology, can only be a long-term goal. The proposed procedure, together with the presented tool, is just a first systematic approach aiming at facilitating this long and arduous *** Codes 68U10. © 2021, CC BY.

关键词： Tropics

VoxSculpt: An Open-Source Voxel Library for Tomographic Volume Sculpting in Virtual Reality 9

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VoxSculpt: An Open-Source Voxel Library for Tomographic Volu...

9th International Conference on Virtual Reality, ICVR 2023

作者： Rodrigues, Lucas Siqueira Riehm, Felix Zachow, Stefan Israel, Johann Habakuk Humboldt-Universität zu Berlin Cluster of Excellence 'Matters of Activity' Berlin Germany Angewandte Informatik Hochschule für Technik und Wirtschaft Berlin Berlin Germany Visual and Data-Centric Computing Zuse Institute Berlin Berlin Germany Forschungsgruppe Centis Hochschule für Technik und Wirtschaft Berlin Berlin Germany

ISBN: (纸本)9798350345810

Manual processing of tomographic data volumes, such as interactive image segmentation in medicine or paleontology, is considered a time-consuming and cumbersome endeavor. Immersive volume sculpting stands as a potential solution to improve its efficiency and intuitiveness. However, current open-source software solutions do not yield the required performance and functionalities. We address this issue by contributing a novel open-source game engine voxel library that supports real-time immersive volume sculpting. Our design leverages GPU instancing, parallel computing, and a chunk-based data structure to optimize collision detection and rendering. We have implemented features that enable fast voxel interaction and improve precision. Our benchmark evaluation indicates that our implementation offers a significant improvement over the state-of-the-art and can render and modify millions of visible voxels while maintaining stable performance for real-time interaction in virtual reality. © 2023 IEEE.

关键词： Virtual reality