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Feasibility study on integrating morphological and transcriptomic data for identifying teratogenic molecular markers in zebrafish embryo toxicity testing

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COMPUTATIONAL TOXICOLOGY 2025年 34卷

作者： Wehr, Matthias M. Witters, Hilda Remy, Silvie Schultz, Bruce Jacobs, Marc Escher, Sylvia E. Fraunhofer Inst Toxicol & Expt Med ITEM Hannover Germany VITO Flemish Inst Technol Res NV HLTH Mol Belgium Fraunhofer Inst Algorithms & Sci Comp SCAI St Augustin Germany

To create a resource for the integration of developmental toxicity new approach methodologies (NAM) data, zebrafish embryo toxicity test (ZET) data were curated and the zeTera database was created. To capture observations of the morphological alteration potential of chemicals, the zeTera database contains experimental study designs and morphological observation data from the literature. Observations of alterations recorded in zeTera were mapped to ontologies and terms were harmonized. In addition, public transcriptomics repositories were mined for data on zebrafish embryos under chemically induced stress. The re-analyzed datasets were compiled into the zetOmics database for the identification of biomarkers of teratogenicity. To identify data-rich compounds, an overlap of both databases was formed, and compounds were grouped based on structural similarities. To identify the molecular drivers of teratogenic toxicants, Triadimefon was chosen as model compound for its well-documented teratogenic effects and known mode of action (MOA). We have compiled existing data about Triadimefon from zeTera and conducted additional testing using the ZET, with additional gene expression measurements for data gap filling. From the literature search we identified the adverse outcome pathway (AOP) of triadimefon leading to craniofacial malformations by disruption of retinoic acid metabolism. Transcriptomic response in a concentration dependent manner was observed as early as 24 h post fertilization (hpf) with consistent, statistically significant, differential expression spanning the later timepoints. A set of 5 genes (cyp26a1, dhrs3b, cyp26b1, cthrc1a, and cd248b) were selected for their differential expression pattern across time and concentration. These biomarkers were further confirmed using read across approach including data from related structures.

关键词： Zebrafish embryo toxicity Morphological alteration Teratogenicity Transcriptomics Data mining

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Llamol: a dynamic multi-conditional generative transformer for de novo molecular design

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JOURNAL OF CHEMINFORMATICS 2024年第1期16卷 73页

作者： Dobberstein, Niklas Maass, Astrid Hamaekers, Jan Fraunhofer Inst Algorithms & Sci Comp Virtual Mat Design Schloss Birlinghoven D-53757 St Augustin Germany

Generative models have demonstrated substantial promise in Natural Language Processing (NLP) and have found application in designing molecules, as seen in General Pretrained Transformer (GPT) models. In our efforts to develop such a tool for exploring the organic chemical space in search of potentially electro-active compounds, we present Llamol, a single novel generative transformer model based on the Llama 2 architecture, which was trained on a 12.5M superset of organic compounds drawn from diverse public sources. To allow for a maximum flexibility in usage and robustness in view of potentially incomplete data, we introduce Stochastic Context Learning (SCL) as a new training procedure. We demonstrate that the resulting model adeptly handles single- and multi-conditional organic molecule generation with up to four conditions, yet more are possible. The model generates valid molecular structures in SMILES notation while flexibly incorporating three numerical and/or one token sequence into the generative process, just as requested. The generated compounds are very satisfactory in all scenarios tested. In detail, we showcase the model's capability to utilize token sequences for conditioning, either individually or in combination with numerical properties, making Llamol a potent tool for de novo molecule design, easily expandable with new properties. Scientific contribution We developed a novel generative transformer model, Llamol, based on the Llama 2 architecture that was trained on a diverse set of 12.5 M organic compounds. It introduces Stochastic Context Learning (SCL) as a new training procedure, allowing for flexible and robust generation of valid organic molecules with up to multiple conditions that can be combined in various ways, making it a potent tool for de novo molecular design.

关键词： Molecular generation Machine learning Transformers De novo molecular design

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A Partition of Unity construction of the stabilization function in Nitsche's method for variational problems

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 426卷

作者： Recio, Pablo Jimenez Schweitzer, Marc Alexander Univ Bonn Inst Numer Simulat Friedrich Hirzebruch Allee 7 D-53115 Bonn Germany Fraunhofer Inst Algorithms & Sci Comp D-53757 St Augustin Germany

In this paper we develop a partition -of -unity construction of the stabilization function required in Nitsche's method, which can be seen as a generalization of the element -wise construction that is widely used in finite element methods. This allows for the use of Nitsche's method within the Partition of Unity Method with a stabilization function that is not simply a constant over the whole boundary. In addition to that, we introduce a patch -aggregation approach designed to avoid arbitrarily large values of the stabilization function and the associated ill -conditioned systems and deteriorated convergence rates. We present numerical results to validate the proposed methods, covering Dirichlet boundary conditions, interface constraints and higherorder problems. These results clearly show that our approach leads to optimal convergence rates.

关键词： Meshfree method Partition of unity method Essential boundary conditions Nitsche's method

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Increasing the resilience of the Texas power grid against extreme storms by hardening critical lines

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NATURE ENERGY 2024年第5期9卷 526-535页

作者： Stuermer, Julian Plietzsch, Anton Vogt, Thomas Hellmann, Frank Kurths, Juergen Otto, Christian Frieler, Katja Anvari, Mehrnaz Potsdam Inst Climate Impact Res Potsdam Germany TU Berlin Inst Theoret Phys Berlin Germany Fraunhofer Res Inst Energy Infrastruct & Geotherma Cottbus Germany Univ Potsdam Inst Phys & Astron Potsdam Germany Humboldt Univ Inst Phys Berlin Germany Fraunhofer Inst Algorithms & Sci Comp St Augustin Germany

The Texas power grid on the Gulf Coast of the United States is frequently hit by tropical cyclones (TCs) causing widespread power outages, a risk that is expected to substantially increase under global warming. Here we introduce a new approach that combines a probabilistic line failure model with a network model of the Texas grid to simulate the spatio-temporal co-evolution of wind-induced failures of high-voltage transmission lines and the resulting cascading power outages from seven major historical TCs. The approach allows reproducing observed supply failures. In addition, compared to existing static approaches, it provides a notable advantage in identifying critical lines whose failure can trigger large supply shortages. We show that hardening only 1% of total lines can reduce the likelihood of the most destructive type of outage by a factor of between 5 and 20. The proposed modelling approach could represent a so far missing tool for identifying effective options to strengthen power grids against future TC strikes, even under limited knowledge. Understanding how power systems fail-and the nature of cascading failures-as a result of hurricanes is important to increase future resilience. Here the authors present a co-evolution approach to modelling wind-induced power line failures caused by hurricanes and assess the potential impact of line hardening on grids.

关键词： Electric power transmission

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Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第1期35卷 614-633页

作者： von Rueden, Laura Mayer, Sebastian Beckh, Katharina Georgiev, Bogdan Giesselbach, Sven Heese, Raoul Kirsch, Birgit Pfrommer, Julius Pick, Annika Ramamurthy, Rajkumar Walczak, Michal Garcke, Jochen Bauckhage, Christian Schuecker, Jannis Fraunhofer IAIS Inst Intelligent Anal & Informat Syst D-53757 St Augustin Germany Fraunhofer SCAI Inst Algorithms & Sci Comp D-53757 St Augustin Germany Inst Ind Math Fraunhofer ITWM D-67663 Kaiserslautern Germany Inst Optron Syst Technol & Image Exploitat Fraunhofer IOSB D-76131 Karlsruhe Germany

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.

关键词： Machine learning prior knowledge expert knowledge informed hybrid neuro-symbolic survey taxonomy

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The Elements of Flexibility for Task-Performing Systems

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IEEE ACCESS 2023年 11卷 8029-8056页

作者： Mayer, Sebastian Sotto, Leo Francoso Dal Piccol Garcke, Jochen Schloss Birlinghoven Fraunhofer Inst Algorithms & Sci Comp SCAI D-53757 St Augustin Germany Univ Bonn Inst Numer Simulat D-53115 Bonn Germany

What makes living systems flexible so that they can react quickly and adapt easily to changing environments? This question has not only engaged biologists for decades but is also of great interest to computer scientists and engineers who seek inspiration from nature to increase the flexibility of task-performing systems such as machine learning systems, robots, or manufacturing systems. In this paper, we give a broad overview of design features of living systems that are known to promote flexibility. We call these design features the "elements of flexibility ". Moreover, to facilitate interdisciplinary, bio-inspired research that brings the elements of flexibility to man-made task-performing systems, we introduce a general formalism for system flexibility optimization. The formalism is intended to (i) provide a common language to communicate ideas about system flexibility among researchers with different backgrounds, (ii) help to understand and compare existing research on system flexibility, e.g., in transfer learning or manufacturing flexibility, and (iii) provide a basis for a general theory of system flexibility optimization.

关键词： Manufacturing Transfer learning Optimization Robots Mass production Computational modeling Adaptability computer-integrated manufacturing degeneracy evolvability few-shot learning hierarchy modularity manufacturing flexibility neutral spaces transfer learning

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ConfRank: Improving GFN-FF Conformer Ranking with Pairwise Training

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2024年第23期64卷 8909-8925页

作者： Hoelzer, Christian Oerder, Rick Grimme, Stefan Hamaekers, Jan Univ Bonn Mulliken Ctr Theoret Chem D-53115 Bonn Germany Inst Numer Simulat D-53115 Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI D-53757 St Augustin Germany

Conformer ranking is a crucial task for drug discovery, with methods for generating conformers often based on molecular (meta)dynamics or sophisticated sampling techniques. These methods are constrained by the underlying force computation regarding runtime and energy ranking accuracy, limiting their effectiveness for large-scale screening applications. To address these ranking limitations, we introduce ConfRank, a machine learning-based approach that enhances conformer ranking using pairwise training. We demonstrate its performance using GFN-FF-generated conformer ensembles, leveraging the DimeNet++ architecture trained on pairs of 159 760 uncharged organic compounds from the GEOM data set with r2SCAN-3c reference level. instead of predicting only on single molecules, this approach captures relative energy differences between conformers, leading to a significant improvement of the overall conformational ranking, outperforming GFN-FF and GFN2-xTB. Thereby, the pairwise RMSD of the relative energy difference of two conformers can be reduced from 5.65 to 0.71 kcal mol-1 on the test data set, allowing to correctly identify up to 81% of all lowest lying conformers correctly (GFN-FF: 10%, GFN2-xTB: 47%). The ConfRank approach is cost-effective, allowing for scalable deployment on both CPU and GPU, achieving runtime accelerations by up to 2 orders of magnitude compared to GFN2-xTB. Out-of-sample investigations on CREST-generated conformer ensembles from the QM9 data set and conformers taken from an extended GMTKN55 data set show promising results for the robustness of this approach. Thereby, ranking correlation coefficient such as Spearman can be improved to 0.90 (GFN-FF: 0.39, GFN2-xTB: 0.84) reducing the probability of an incorrect sign flip in pairwise energy comparison from 32 to 7%. On the extended GMTKN55 subsets the pairwise MAD (RMSD) could be reduced on almost all subsets by up to 62% (58%) with an average improvement of 30% (29%). Moreover, an exemplary case stud

关键词： Network security

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Foresight-generative pretrained transformer for the prediction of patient timelines

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LANCET DIGITAL HEALTH 2024年第4期6卷 e233-e234页

作者： Hofmann-Apitius, Martin Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53754 St Augustin Germany Univ Bonn Bonn Aachen Int Ctr IT Bonn Germany

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On the effects of biased quantum random numbers on the initialization of artificial neural networks

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MACHINE LEARNING 2024年第3期113卷 1189-1217页

作者： Heese, Raoul Wolter, Moritz Muecke, Sascha Franken, Lukas Piatkowski, Nico Fraunhofer Ctr Machine Learning Fraunhofer Pl 1 D-67663 Kaiserslautern Germany Fraunhofer Inst Ind Math ITWM Fraunhofer Pl 1 D-67663 Kaiserslautern Germany Fraunhofer Ctr Machine Learning Konrad Adenauer Str D-53757 St Augustin Germany Fraunhofer Inst Algorithms Sci Comp SCAI Schloss Birlinghoven Konrad Adenauer Str D-53757 St Augustin Germany TU Dortmund Univ Artificial Intelligence Grp Otto Hahn Str 12 D-44227 Dortmund Germany Fraunhofer Inst Intelligent Anal & Informat Syst I Schloss Birlinghoven Konrad Adenauer Str D-53757 St Augustin Germany

Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum devices. A common property of quantum computers is that they can exhibit instances of true randomness as opposed to pseudo-randomness obtained from classical systems. Investigating the effects of such true quantum randomness in the context of machine learning is appealing, and recent results vaguely suggest that benefits can indeed be achieved from the use of quantum random numbers. To shed some more light on this topic, we empirically study the effects of hardware-biased quantum random numbers on the initialization of artificial neural network weights in numerical experiments. We find no statistically significant difference in comparison with unbiased quantum random numbers as well as biased and unbiased random numbers from a classical pseudo-random number generator. The quantum random numbers for our experiments are obtained from real quantum hardware.

关键词： Quantum computing Random number generation Neural networks Machine learning

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Estimating the causal impact of non-pharmaceutical interventions on COVID-19 spread in seven EU countries via machine learning

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SCIENTIFIC REPORTS 2025年第1期15卷 1-16页

作者： Guski, Jannis Botz, Jonas Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53757 St Augustin Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT D-53115 Bonn Germany

During the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were imposed all over Europe with the intent to reduce infection spread. However, reports on the effectiveness of those measures across different European countries are inconclusive up to now. Moreover, attempts to predict the effect of NPIs in a prospective and dynamical manner with the aim to support decision makers in future global health emergencies are largely lacking. Here, we explore causal machine learning to isolate causal effects of NPIs in observational public health data from seven EU countries, taking into account specific challenges like their sequential nature, effect heterogeneity, time-dependent confounding and lack of robustness due to violated assumptions. In a pseudo-prospective scenario planning analysis, we investigate which recommendations our model would have made during the second wave of the pandemic in Germany, demonstrating its capacity to generalize to the near future and identifying effective NPIs. In retrospect, our approach indicates that a wide range of response measures curbed COVID-19 across countries, especially in the early phases of the pandemic. Interestingly, this includes controversial interventions like strict school and border closures, but also recommendation-based policies in Sweden. Finally, we discuss important data- and modeling-related considerations that may optimize causal effect estimation in future pandemics.

关键词： Causal machine learning Policy recommendation Time-dependent confounding

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