minerva -微生物组网络研究和可视化图谱:用于绘制微生物组-疾病关联的可扩展知识图谱。

IF 7.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Saul Langarica, Young-Tak Kim, Adham Alkhadrawi, Jung Bin Kim, Synho Do
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引用次数: 0

摘要

细菌性病原体对全球疾病负担有重大影响。了解它们与人类健康的复杂相互作用对于开发新的诊断、预防和治疗策略至关重要。虽然最近的突破彻底改变了我们对这些关系的理解,但微生物组研究的快速扩张提出了一个重大挑战:知识仍然分散在科学文献中,阻碍了全面分析和临床翻译。为了解决这个问题,我们引入了MINERVA(微生物组网络研究和可视化图集),这是一个创新的平台,利用一个微调的大型语言模型,在广泛的科学文献中系统地绘制微生物与疾病的关联。MINERVA构建了一个丰富的、本体驱动的知识图谱,优先考虑准确性和透明度,能够有效地探索和发现与临床决策相关的先前隐藏的关联。该平台具有专门的模块,允许研究人员分析个体微生物和疾病,可视化知识网络中的复杂关系,通过先进的图形算法和机器学习模型发现隐藏的联系,并执行个性化和种群水平的微生物组组成分析。这些功能有助于识别疾病风险、合并症和可操作的见解,支持研究和临床决策。通过弥合微生物组研究和实际应用之间的差距,MINERVA有可能改变我们对微生物-疾病相互作用的理解,加速发现和推进患者护理。MINERVA平台可在https://minervabio.org/上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MINERVA-microbiome network research and visualization atlas: a scalable knowledge graph for mapping microbiome-disease associations.

Bacterial pathogens contribute significantly to the global burden of disease. Understanding their complex interactions with human health is essential for developing new diagnostic, preventative, and therapeutic strategies. While recent breakthroughs have revolutionized our understanding of these relationships, the rapid expansion of microbiome research presents a significant challenge: knowledge remains scattered across scientific literature, hindering comprehensive analysis and clinical translation. To address this, we introduce MINERVA (Microbiome Network Research and Visualization Atlas), an innovative platform that leverages a fine-tuned large language model to systematically map microbe-disease associations across extensive scientific literature. MINERVA constructs a rich, ontology-driven knowledge graph that prioritizes accuracy and transparency, enabling efficient exploration and discovery of previously hidden associations relevant to clinical decision-making. The platform features specialized modules that allow researchers to analyze individual microbes and diseases, visualize complex relationships within the knowledge network, uncover hidden connections through advanced graph algorithms and machine-learning models, and perform personalized and population-level microbiome compositional analysis. These capabilities facilitate the identification of disease risks, comorbidities, and actionable insights, supporting both research and clinical decision-making. By bridging the gap between microbiome research and real-world applications, MINERVA has the potential to transform our understanding of microbe-disease interactions, accelerating discoveries and advancing patient care. The MINERVA platform is available at https://minervabio.org/.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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