18 papers · ranked by Valyu relevance
Authors not listed
Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…
Natalia Andrienko, Gennady Andrienko, Jürgen Bernard, Michael Sedlmair
Visual analytics (VA) workflows are inherently complex, involving data transformation, feature engineering, visual representation, and human interpretation. They are typically described in unstructured prose, hindering systematic comparison, reuse of proven strategies, and training of novices. We present…
Shixiang Wang
Bioinformatics analyses depend on workflow engines to coordinate dozens of computational tools across complex dependency chains. The most widely adopted engines—Snakemake, Nextflow, the Common Workflow Language (CWL), and the Workflow Description Language (WDL)—run on interpreted or just-in-time (JIT) compiled language…
Francisco Abreu, Luís Cruz, Sérgio Guerreiro
Proprietary workflow modeling languages such as Smart Forms & Smart Flow hamper interoperability and reuse because they lock process knowledge into closed formats. To address this vendor lock-in and ease migration to open standards, we introduce an ontology-driven modelto-model pipeline that systematically translates…
Le Zhang, Daniela Cassol, Brendan Gongol, Thomas Girke
Workflow management systems (WMS) are essential for creating and automating multi-step data analyses and ensuring the reproducibility of biological insights. Although numerous WMS solutions exist, few provide deep integration of command-line software with the R and Bioconductor ecosystems, where a substantial portion…
Damien J. Mannion, Maria del Mar Quiroga, Jacob M. Paul, Marta I. Garrido
The processing of neuroimaging data typically involves a complicated set of operations, which often require different software packages and have intensive computational and storage demands. Although there are many options available for the neuroimaging researcher to establish their preferred set of processing…
Aref Talebzadeh Bardsiri, Alexandre Decan, Tom Mens
Developers often struggle with maintaining GitHub Actions workflow configurations in GitHub-hosted repositories, with recent studies showing frequent execution failures. This paper empirically explores how the adoption and evolution of GitHub Actions language constructs impacts workflow reliability and maintainability.…
Hyeon-Min Kim, Hwayeon Jeong, Abyot Melkamu Mekonnen, Yeongjun Kim + 4 more
Large language models (LLMs) are increasingly used to generate bioinformatics pipelines and to carry out analyses from natural-language prompts. However, the resulting analyses are often difficult to reproduce across sessions, owing to the non-deterministic nature of LLM-driven conversations and heterogeneity of local…
Panchal, Deven
—Generative Agentic AI systems are emerging as a powerful paradigm for automating complex, multi-step tasks. However, many existing frameworks for building these systems introduce significant complexity, a steep learning curve, and substantial boilerplate code, hindering rapid prototyping and deployment. This paper…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Daniel Pearson, Sidney Shapiro, Emiliano Sebastian Gonzalez Venegas, Sanad Al-Khatib + 1 more
This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes. Rather than treating LangGraph, a low-level orchestration framework for stateful agents, as a model-quality benchmark target, we present three executable recipes -- SQL…
Authors not listed
ThinkFactory 2025 was a virtual workshop jointly hosted by the Acceleration Consortium and CMAC to facilitate discussion on harmonizing and accelerating self-driving laboratories. The three-hour event brought together more than 50 participants for plenary and breakout discussion across four themed tracks: AI and…
Yutian Tang, Yuming Zhou, Huaming Chen
Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs. LLM agents are LLM systems that use LLMs as a core "brain" to reason, plan, and autonomously execute…
Emanuele Quinto, Carlo Andrea Rozzi, Francesco Zanitti
Large language model (LLM) applications increasingly use explicit workflows for tool use, retrieval, branching, checkpointing, and human approval. Existing workflow systems already address many execution concerns. This paper proposes a Lisp-inspired but language-independent conceptual model: symbolic forms, object…
Mateusz Gładysz, Fabrizio Fiumedinisi, Felice Burn, Nikki Rommers + 3 more
Background Medical documentation imposes a significant administrative burden on physicians and reduces time for direct patient care. Artificial intelligence (AI)-assisted tools such as automatic speech recognition and large language models (LLMs) promise to reduce this burden, but their performance in multilingual…
Zishuai Wang, Chongxiao Liang, Xiaoai Zhang, Wenkang Wei + 1 more
Genomic breeding has become increasingly data-intensive, yet the practical integration of heterogeneous bioinformatics tools into coherent analytical workflows remains a major bottleneck. To address this, we present BOLE, a knowledge-enhanced multi-agent AI framework for autonomous genomic breeding analysis. By…
Zehua Zeng, Xuehai Wang, Zhi Luo, Yawen Zheng + 3 more
Advances in bulk, single-cell and spatial omics have transformed biological discovery, yet analysis remains fragmented across packages with incompatible interfaces, heterogeneous dependencies and limited workflow reproducibility. Here, we present OmicClaw, an executable natural-language framework for multi-omics…
Jeffery Ye, Amy DeRocher, Monique Khim, Sandhya Subramanian + 3 more
Recent advances in Large Language Models (LLMs) present new opportunities for automating critical bottlenecks in scientific workflows such as literature reviews or protocol design. One such bottleneck is the purification of recombinant proteins, a vital aspect of biomedical research that frequently fails. To improve…