23 papers · ranked by Valyu relevance
Klaus‐Dieter Sommer, P M Harris, Sascha Eichstädt, Roland Füßl + 9 more
'Tanja Dorst' 'Andreas Schütze' 'Michael Heizmann' 'Nadine Schiering' 'Andreas Maier' 'Yuhui Luo' 'Christos Tachtatzis' 'Ivan Andonović' 'Gordon Gourlay'] - 1 Technische Universitaet Ilmenau, Germany - 2 National Physical Laboratory, Teddington, United Kingdom - 3 Physikalisch-Technische Bundesanstalt, Braunschweig and…
Linda-Sophie Schneider, Patrick Krauß, Nadine Schiering, Christopher Syben + 2 more
Mathematical models are vital to the field of metrology, playing a key role in the derivation of measurement results and the calculation of uncertainties from measurement data, informed by an understanding of the measurement process. These models generally represent the correlation between the quantity being measured…
Maja Rudolph, Stefan Kurz, Barbara Rakitsch
Design patterns provide a systematic way to convey solutions to recurring modeling challenges. This paper introduces design patterns for hybrid modeling, an approach that combines modeling based on first principles with data-driven modeling techniques. While both approaches have complementary advantages there are often…
Ibai Laña, Javier J. Sanchez-Medina, Eleni I. Vlahogianni, Javier Del Ser + 1 more
'Javier Del Ser' 'Rashid Mehmood'] Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and…
Georgios Makrygiorgos, Aaron J. Berliner, Fengzhe Shi, Douglas S. Clark + 2 more
Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed…
John Metzcar, Catherine R. Jutzeler, Paul Macklin, Alvaro Köhn‐Luque + 1 more
'Sarah C. Brüningk'] - * Corresponding author: sarah.brueningk@hest.ethz.ch - 1 Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, Indiana, USA - 2 Informatics, Luddy School of Informatics, Computing, and Engineering, Bloomington, Indiana, USA - 3 ETH Zürich…
John Metzcar, Catherine R. Jutzeler, Paul Macklin, Alvaro Köhn-Luque + 1 more
'Sarah C. Brüningk'] Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a…
Carl Julius Martensen, Niklas Korsbo, Vijay Ivaturi, Sebastian Sager
In pharmacometrics, developing and selecting models is crucial for quantitatively assessing drug-biological interactions, treatment planning, and gaining insights into underlying processes. These validated models are essential for predictive analytics and strategic decision-making in drug development and clinical…
Daniel Anadria, Roel Dobbe, Anastasia Giachanou, Ruurd J. A. Kuiper + 5 more
In this work, we reflect on the data-driven modeling paradigm that is gaining ground in AI-driven automation of patient care. We argue that the repurposing of existing real-world patient datasets for machine learning may not always represent an optimal approach to model development as it could lead to undesirable…
Sepehr Golriz Khatami, Christine Robinson, Colin Birkenbihl, Daniel Domingo-Fernández + 2 more
'Daniel Domingo-Fernández' 'Charles Tapley Hoyt' 'Martin Hofmann-Apitius'] Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention.…
Thomas Hartmann, Assaad Moawad, François Fouquet, Grégory Nain + 3 more
'Jacques Klein' 'Yves Le Traon' 'Jean-Marc Jézéquel'] Abstract—Gaining profound insights from collected data of today's application domains like IoT, cyber-physical systems, health care, or the financial sector is businesscritical and can create the next multi-billion dollar market. However, analyzing these data and…
Maria Garcia-Cremades, Zinnia P. Parra-Guillen, Victor Mangas-Sanjuan, Hugo Geerts
'Hugo Geerts'] A successful Drug Development is driven by many factors, but ultimately it is often dependent upon the weakest part. Therefore, it is necessary to utilize all tools available to support the complete journey for a successful development program. Model-informed drug development (MIDD) in which crucial…
Jürgen Bajorath
Computational groups are challenged to provide external and internal data in an easily accessible form. However, the problem is more general. In data science, community-wide standards and tools for data processing and knowledge extraction are available but in chemistry, such standards and infrastructures are lacking.…
Jessica S. Yu, Blair Lyons, Susanne Rafelski, Julie A. Theriot + 2 more
Iterating between data-driven research and generative computational models is a powerful approach for emulating biological systems, testing hypotheses, and gaining a deeper understanding of these systems. We developed a hybrid agent-based model (ABM) that integrates a Cellular Potts Model (CPM) designed to investigate…
Kate E. Dray, Joseph J. Muldoon, Niall M. Mangan, Neda Bagheri + 1 more
Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
Ricardo Stefani
The use of data science, artificial intelligence, and big data in the field of chemistry has recently grown to speed up the discovery of new materials, drugs, and synthetic substances and the identification of automated compounds. Machine learning and data science are commonly used in organic chemistry to predict…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Mark D. Danese, Marc Halperin, Jennifer Duryea, Ryan Duryea
Most healthcare data sources store information within their own unique schemas, making reliable and reproducible research challenging. Consequently, researchers have adopted various data models to improve the efficiency of research. Transforming and loading data into these models is a labor-intensive process that can…
Authors not listed
Range anxiety remains a major concern for electric vehicle (EV) drivers due to unpredictable charge usage influenced by terrain and user behavior variations. To address this issue, we propose a data-driven approach to provide accurate trip-specific battery consumption for EV drivers. First, we present a new…
Jürgen Bajorath, Ana L. Chávez-Hernández, Miquel Duran-Frigola, Eli Fernández-de Gortari + 13 more
Jürgen Bajorath 2 , Ana L. Chávez-Hernández 1 , Miquel Duran-Frigola 3 , Eli Fernández-de Gortari 4 , Johann Gasteiger 5 , Edgar López-López1,6 , Gerald M. Maggiora 15 , José L. Medina-Franco 1 , Oscar Méndez-Lucio 7 , Jordi Mestres8,9 , Ramón Alain Miranda-Quintana 10 , Tudor I. Oprea 16 , Fabien Plisson 11 , Fernando…
Marvin van Aalst, Oliver Ebenhöh, Anna Matuszyńska
Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Unfortunately, despite community efforts leading to the development of…