Ahmed Yasmine, Telmer Cheryl A, Zhou Gaoxiang, Miskov-Zivanov Natasa
Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PA, United States.
Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, United States.
Front Syst Biol. 2024 Apr 18;4:1308292. doi: 10.3389/fsysb.2024.1308292. eCollection 2024.
New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (elerating and ptimizing model ecommenats), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: , retrieving relevant knowledge from a range of literature sources through machine reading engines; very , reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; , recommending only the most relevant, context-specific, and useful subset (15%-20%) of candidate knowledge in literature; , accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions.
每年每个科学领域都有成千上万篇已发表的论文总结新的发现和知识，这使得科学家难以了解与其研究相关的所有现有知识。在本文中，我们介绍了ACCORDION（加速和优化模型推荐），这是一种新颖的方法和专家系统，它从文献和数据库中检索并选择相关知识，以推荐具有正确结构和准确行为的模型，从而实现机理解释和预测，并增进理解。ACCORDION引入了一种整合知识检索、图算法、聚类、模拟和形式分析的方法。在这里，我们专注于生物系统，尽管所提出的方法也适用于其他领域。我们在九个基准案例研究中使用了ACCORDION，并将其性能与其他先前发表的工具进行了比较。我们表明ACCORDION具有以下特点：一是通过机器阅读引擎从一系列文献来源中检索相关知识；二是非常有效，将初始基线模型的误差降低了80%以上，推荐的模型能紧密重现期望的行为，并且优于先前发表的工具；三是仅推荐文献中最相关、特定于上下文且有用的候选知识子集（15%-20%）；四是考虑多个不同标准来推荐不止一个解决方案，从而实现替代解释或干预方向。
