Biomedical systems biology workflow orchestration and execution with PoSyMed

• URL: http://arxiv.org/abs/2604.20906v1
• Date: Tue, 21 Apr 2026 20:20:58 GMT
• Title: Biomedical systems biology workflow orchestration and execution with PoSyMed
• Abstract summary: PoSyMed is an open and modular platform for the controlled integration, composition, and execution of bioinformatics tools and tools.<n>PoSyMed combines a backend-centered platform architecture with formal tool descriptions, controlled container-based build and execution processes, persistent workflow state, and a dialogue-based user interface.
• Score: 3.2613377085614417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of scientific software has created practical barriers for bioinformatics research. Although powerful statistical, artificial intelligence (AI)-based methods are now widely available, their effective use is often hindered by fragmented distribution, inconsistent documentation, complex dependencies, and difficult-to-reproduce execution environments. As a result, reusing published tools and workflow adaptation to own date remains technically demanding and time-intensive, even for experienced users. Here, we present PoSyMed, an open and modular platform for the controlled integration, composition, and execution of bioinformatics tools and workflows. PoSyMed combines a backend-centered platform architecture with formal tool descriptions, controlled container-based build and execution processes, persistent workflow state, and a dialogue-based user interface. Large language models (LLM) are integrated not as autonomous decision-makers, but as human-computer interface with bounded semantic assistants that help identify tools, propose workflow steps, and support parameterization within a typed, validated, and human-supervised execution environment. PoSyMed is designed to improve reproducibility, traceability, and transparency in practical biomedical analysis within one platform. We describe the system architecture and evaluate its behavior across representative biological software scenarios with respect to workflow support, interaction design, and platform extensibility. PoSyMed is publicly available at https://apps.cosy.bio/posymed.

Related papers

• STEP: A Modular Silent Trial Engine for Operational Evaluation of Digital Pathology AI in Routine Workflow [1.6877591150411722]
  Silent trials provide a bridge between retrospective validation of AI models and their use in clinical care.<n>We developed a reusable software platform for orchestrating prospective silent trials of computational pathology AI models.
  arXiv  Detail & Related papers  (2026-08-27T20:33:39Z)
• Beyond Prompt-Based Planning: MCP-Native Graph Planning-based Biomedical Agent System [0.8857568447033265]
  We introduce BioManus, an MCP-native biomedical agent built on graph-scaffolded planning over structured biological capabilities.<n>We show that BioManus improves execution accuracy, workflow validity, and context efficiency over advanced biomedical agent baselines.
  arXiv  Detail & Related papers  (2026-06-03T06:19:25Z)
• Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research [2.4251063179584436]
  Mimosa is an evolving multi-agent framework that automatically synthesizes task-specific multi-agents and iteratively refines them.<n>Released as a fully open-source platform, Mimosa aims to provide an open foundation for community-driven ASR.
  arXiv  Detail & Related papers  (2026-03-30T20:35:57Z)
• The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration [58.61149924275458]
  Tool use enables large language models to access external information, invoke software systems, and act in digital environments beyond what can be solved from model parameters alone.<n>As agent systems evolve, the central problem has shifted from isolated invocation to multi-tool orchestration over long trajectories with intermediate state, execution feedback, changing environments, and practical constraints such as safety, cost, and verifiability.<n>We organize the literature around six core dimensions: inference-time planning and execution, training and trajectory construction, safety and control, efficiency under resource constraints, capability completeness in open environments, and benchmark design and evaluation.
  arXiv  Detail & Related papers  (2026-03-24T07:05:05Z)
• MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning [53.37068897861388]
  MedSAM-Agent is a framework that reformulates interactive segmentation as a multi-step autonomous decision-making process.<n>We develop a two-stage training pipeline that integrates multi-turn, end-to-end outcome verification.<n>Experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM-Agent achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-03T09:47:49Z)
• Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale [82.20980951765891]
  We argue that scaling agentic science requires an infrastructure-and-ecosystem approach, instantiated Bohrium+SciMaster.<n>Bohrium acts as a managed, traceable hub for AI4S assets that turns diverse scientific data, software, compute, and laboratory systems into agent-ready capabilities.<n>SciMaster orchestrates these capabilities into long-horizon scientific, on which scientific agents can be composed and executed.
  arXiv  Detail & Related papers  (2025-12-23T16:04:41Z)
• TongSIM: A General Platform for Simulating Intelligent Machines [59.27575233453533]
  Embodied intelligence focuses on training agents within realistic simulated environments.<n>TongSIM is a high-fidelity, general-purpose platform for training and evaluating embodied agents.
  arXiv  Detail & Related papers  (2025-12-23T10:00:43Z)
• Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization [69.36509281190662]
  Adapting production-level computer vision tools to bespoke scientific datasets is a critical "last mile" bottleneck.<n>We consider using AI agents to automate this manual coding, and focus on the open question of optimal agent design.<n>We demonstrate that a simple agent framework consistently generates adaptation code that outperforms human-expert solutions.
  arXiv  Detail & Related papers  (2025-12-02T18:42:26Z)
• Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges [13.53016942028838]
  Large language models (LLMs) are capable of using natural language to integrate information, follow instructions, and perform forms of "reasoning" and planning.<n>With its multimodal data streams and orchestrated spanning multiple systems, radiology is uniquely suited to benefit from agents that can adapt to context and automate repetitive yet complex tasks.<n>This review examines the design of such LLM agentic systems, highlights key applications, discusses evaluation methods for planning and tool use, and outlines challenges such as error cascades, tool-use efficiency, and health IT integration.
  arXiv  Detail & Related papers  (2025-10-10T13:56:27Z)
• A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms [0.03262230127283451]
  We introduce a model driven engineering (MDE) framework designed specifically for healthcare AI.<n>The framework relies on formal metamodels, domain-specific languages, and automated transformations to move from high level specifications to running software.<n>We evaluate this approach in a multi center cancer immunotherapy study.
  arXiv  Detail & Related papers  (2025-10-10T12:00:12Z)
• A co-evolving agentic AI system for medical imaging analysis [14.925000849408683]
  "TissueLab" is a co-evolving agentic AI system that allows researchers to ask direct questions, automatically plan and generate explainable results, and conduct real-time analyses.<n>By standardizing inputs, outputs, and capabilities of diverse tools, the system determines when and how to invoke them to address research and clinical questions.<n>TissueLab achieves state-of-the-art performance compared with end-to-end vision-language models (VLMs) and other agentic AI systems such as GPT-5.
  arXiv  Detail & Related papers  (2025-09-24T16:15:28Z)
• State and Memory is All You Need for Robust and Reliable AI Agents [29.259008600842517]
  Large language models (LLMs) have enabled powerful advances in natural language understanding and generation.<n>Yet their application to complex, real-world scientific remain limited by challenges in memory, planning, and tool integration.<n>Here, we introduce SciBORG, a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution.
  arXiv  Detail & Related papers  (2025-06-30T02:02:35Z)
• ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows [82.07367406991678]
  Large Language Models (LLMs) have extended their impact beyond Natural Language Processing.<n>Among these, computer-using agents are capable of interacting with operating systems as humans do.<n>We introduce ScienceBoard, which encompasses a realistic, multi-domain environment featuring dynamic and visually rich scientific software.
  arXiv  Detail & Related papers  (2025-05-26T12:27:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.