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Dagstuhl Reports, Volume 14, Issue 12
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Dagstuhl Reports, Volume 14
Journal: Dagstuhl Reports (DagRep)
Event
Dagstuhl Seminars 24491, 24492 (Perspectives Workshop), 24511, 24512Publication Details
- published at: 2025-04-29
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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Complete Issue
Dagstuhl Reports, Volume 14, Issue 12, December 2024, Complete Issue
Abstract
Dagstuhl Reports, Volume 14, Issue 12, December 2024, Complete Issue
Cite as
Dagstuhl Reports, Volume 14, Issue 12, pp. 1-86, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{DagRep.14.12,
title = {{Dagstuhl Reports, Volume 14, Issue 12, December 2024, Complete Issue}},
pages = {1--86},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {12},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.12},
URN = {urn:nbn:de:0030-drops-230500},
doi = {10.4230/DagRep.14.12},
annote = {Keywords: Dagstuhl Reports, Volume 14, Issue 12, December 2024, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 14, Issue 12, 2024
Abstract
Dagstuhl Reports, Table of Contents, Volume 14, Issue 12, 2024
Cite as
Dagstuhl Reports, Volume 14, Issue 12, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{DagRep.14.12.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 14, Issue 12, 2024}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {12},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.12.i},
URN = {urn:nbn:de:0030-drops-230453},
doi = {10.4230/DagRep.14.12.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Deep Learning for RNA Regulation and Multidimensional Transcriptomics (Dagstuhl Seminar 24491)
Abstract
The Dagstuhl Seminar 24491 "Deep Learning for RNA Regulation and Multidimensional Transcriptomics" convened experts from computer science, computational biology, and experimental research to explore the intersection of artificial intelligence and RNA biology. The seminar facilitated discussions on the latest computational methods and experimental approaches that are reshaping our understanding of RNA-mediated gene regulation. With the rapid growth of transcriptomics data, deep learning methods are becoming essential tools for extracting insights from complex datasets, ranging from primary sequence information to intricate cellular dynamics.
A key theme of the seminar was the exploration of non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs, which play pivotal roles in regulating gene expression. High-throughput methods to profile these RNAs, combined with deep learning algorithms, are enabling the identification of novel regulatory mechanisms and the prediction of their cellular functions. The discussion underscored the challenges in classifying lncRNAs, deciphering their sequence features, and understanding their functional interactions.
The seminar also addressed the integration of deep learning in modeling RNA regulatory networks. Participants presented cutting-edge models for predicting RNA modifications, RNA-protein interactions, and the effects of genetic variants on RNA metabolism. Special attention was given to the interpretability of machine learning models, as understanding the biological significance of predictions remains a critical challenge. Advances in single-cell and spatial transcriptomics were highlighted as key drivers of future breakthroughs, offering unprecedented resolution of cellular heterogeneity and regulatory processes.
Another major focus was the role of deep learning in RNA-based therapeutic development. Discussions included the use of machine learning for designing RNA sequences in synthetic biology applications, predicting the efficacy of antisense oligonucleotides (ASOs), and identifying cancer-specific neoantigens. These applications demonstrate the potential of AI to accelerate the discovery of novel RNA-targeted therapies and improve precision medicine approaches.
In addition, the seminar emphasized the importance of community-driven initiatives to improve benchmarking, data curation, and collaborative model development. Participants highlighted the need for standardized datasets, transparent evaluation metrics, and shared computational resources to foster reproducibility and innovation. The discussions underscored the necessity of cross-disciplinary collaboration to ensure that machine learning methods address biologically meaningful questions and produce actionable insights.
Overall, the seminar illustrated how deep learning is transforming RNA biology by uncovering new layers of gene regulation and facilitating therapeutic discoveries. Moving forward, continued interdisciplinary collaboration and the development of scalable, interpretable models will be essential to unlock the full potential of AI in decoding RNA functions and advancing biomedical research.
Cite as
Annalisa Marsico, Uwe Ohler, Igor Ulitsky, Kathi Zarnack, and Charlotte Capitanchik. Deep Learning for RNA Regulation and Multidimensional Transcriptomics (Dagstuhl Seminar 24491). In Dagstuhl Reports, Volume 14, Issue 12, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{marsico_et_al:DagRep.14.12.1,
author = {Marsico, Annalisa and Ohler, Uwe and Ulitsky, Igor and Zarnack, Kathi and Capitanchik, Charlotte},
title = {{Deep Learning for RNA Regulation and Multidimensional Transcriptomics (Dagstuhl Seminar 24491)}},
pages = {1--27},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {12},
editor = {Marsico, Annalisa and Ohler, Uwe and Ulitsky, Igor and Zarnack, Kathi and Capitanchik, Charlotte},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.12.1},
URN = {urn:nbn:de:0030-drops-230497},
doi = {10.4230/DagRep.14.12.1},
annote = {Keywords: deep learning, epitranscriptomics, rna, single-cell transcriptomics}
}
Document
Human in the Loop Learning through Grounded Interaction in Games (Dagstuhl Perspectives Workshop 24492)
Abstract
Over the past few years, methods for learning from interaction have become a crucial paradigm in Artificial Intelligence, and we are now witnessing a growing interest in learning from grounded interaction, in particular through dialogue games. In the Dagstuhl Perspectives Workshop 24492, "Human-in-the-Loop Learning through Grounded Interaction in Games", we discussed these new developments, and identified a few crucial directions for this research. These directions were considering agent behavior in complex interaction; ensuring that games properly tested all aspects of an agent’s cognitive and communicative ability; considering the types of grounding required at all levels of interaction; and developing new training methods that could fully leverage these richer types of context and communication.
Cite as
Raffaella Bernardi, Julia Hockenmaier, Udo Kruschwitz, Prashant Jayannavar, and Massimo Poesio. Human in the Loop Learning through Grounded Interaction in Games (Dagstuhl Perspectives Workshop 24492). In Dagstuhl Reports, Volume 14, Issue 12, pp. 28-45, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{bernardi_et_al:DagRep.14.12.28,
author = {Bernardi, Raffaella and Hockenmaier, Julia and Kruschwitz, Udo and Jayannavar, Prashant and Poesio, Massimo},
title = {{Human in the Loop Learning through Grounded Interaction in Games (Dagstuhl Perspectives Workshop 24492)}},
pages = {28--45},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {12},
editor = {Bernardi, Raffaella and Hockenmaier, Julia and Kruschwitz, Udo and Jayannavar, Prashant and Poesio, Massimo},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.12.28},
URN = {urn:nbn:de:0030-drops-230487},
doi = {10.4230/DagRep.14.12.28},
annote = {Keywords: artificial intelligence, conversational agents in games, grounded dialogue and interaction, human-in-the-loop learning}
}
Document
Coding Theory and Algorithms for Emerging Technologies in Synthetic Biology (Dagstuhl Seminar 24511)
Abstract
The progress in understanding genes and genomes has given a boost to the use of synthetic DNA for biological and technological applications. Synthetic nucleic acids play a central role in synthetic biology and in emerging therapeutic paradigms, e.g., genome editing and nucleic acid vaccines. DNA-based data storage is making a significant progress, and thanks to its extreme data-density, its high durability and its timelessness, it is promising to be the next standard for data archival systems.
Synthetic biology and the use of synthetic DNA for information storage applications bring important algorithmic and data analysis challenges. In synthetic biology, reagent and assay design are often driven by algorithmic approaches. Novel synthesis technologies offer cost-reduction by several orders of magnitude at the cost of increased error-rate, raising new coding-theoretic questions.
This Dagstuhl Seminar brought together researchers working on different aspects of synthetic biology and applications in bio-informatics and informatics; the diverse crowd at the seminar included chemists, biologists, computer scientists, and communication engineers. It successfully honed in on the interplay between these fields, allowing participants a window into the insights of other disciplines, and bringing all of these together to bear on current challenges. Going forward, connections forged during the Dagstuhl Seminar will promote interdisciplinary collaboration between participants and their respective networks.
Cite as
R. B., Olgica Milenkovic, Zohar Yakhini, Yonatan Yehezkeally, Anisha Banerjee, and Frederik Walter. Coding Theory and Algorithms for Emerging Technologies in Synthetic Biology (Dagstuhl Seminar 24511). In Dagstuhl Reports, Volume 14, Issue 12, pp. 46-62, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{b._et_al:DagRep.14.12.46,
author = {B., R. and Milenkovic, Olgica and Yakhini, Zohar and Yehezkeally, Yonatan and Banerjee, Anisha and Walter, Frederik},
title = {{Coding Theory and Algorithms for Emerging Technologies in Synthetic Biology (Dagstuhl Seminar 24511)}},
pages = {46--62},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {12},
editor = {B., R. and Milenkovic, Olgica and Yakhini, Zohar and Yehezkeally, Yonatan and Banerjee, Anisha and Walter, Frederik},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.12.46},
URN = {urn:nbn:de:0030-drops-230476},
doi = {10.4230/DagRep.14.12.46},
annote = {Keywords: Bio-informatics, Error-correcting codes, synthetic biology}
}
Document
Quantum Software Engineering (Dagstuhl Seminar 24512)
Abstract
The Dagstuhl Seminar 24512 on "Quantum Software Engineering" was held from December 15 to 20, 2024. It brought together 26 participants from industry and academia from 13 different countries, including senior and junior researchers as well as practitioners in the field of Quantum Software Engineering. The aim of the seminar was to advance software engineering methods and tools for the engineering of hybrid quantum systems by promoting personal interaction and open discussion among researchers who are already working in this emerging area of knowledge. The first day of the seminar was devoted to the topic "When software engineering meets quantum mechanics", while the second day focused on "Quantum software engineering and its challenges." During both days, 16 invited presentations were given. The rest of the seminar was organized into three working groups to address the topics "Quantum Software Design, Modelling and Architecturing", "Adaptive Hybrid Quantum Systems", and "Quantum Software Quality Assurance". The seminar was a very fruitful experience for all participants both in terms of scientific outcomes and in terms of the personal relationships that were generated to jointly address future experiences.
Cite as
Shaukat Ali, Johanna Barzen, Andrea Delgado, Hausi A. Müller, and Juan Manuel Murillo. Quantum Software Engineering (Dagstuhl Seminar 24512). In Dagstuhl Reports, Volume 14, Issue 12, pp. 63-84, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{ali_et_al:DagRep.14.12.63,
author = {Ali, Shaukat and Barzen, Johanna and Delgado, Andrea and M\"{u}ller, Hausi A. and Murillo, Juan Manuel},
title = {{Quantum Software Engineering (Dagstuhl Seminar 24512)}},
pages = {63--84},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {14},
number = {12},
editor = {Ali, Shaukat and Barzen, Johanna and Delgado, Andrea and M\"{u}ller, Hausi A. and Murillo, Juan Manuel},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.12.63},
URN = {urn:nbn:de:0030-drops-230469},
doi = {10.4230/DagRep.14.12.63},
annote = {Keywords: adaptive hybrid quantum systems, Linear algebra, Quantum algorithms, Quantum architecture, Quantum circuit compilation, Quantum computing, Quantum machine learning, Quantum modeling, Quantum networking, Quantum optimization, Quantum runtime systems, Quantum simulation, quantum software design, Quantum software development, Quantum software engineering, quantum software quality assurance, Quantum software stack and platforms}
}