Image-guided Biocuration of Disease Pathways From Scientific Literature

科学文献中疾病途径的图像引导生物治疗

• 批准号: 10583552
• 负责人: Mihail Popescu
• 金额: $ 31.64万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10583552
• 关键词: Address; Architecture; Benchmarking; Biological; Cancer Patient; Communities; Computers; Databases; Deposition; Detection; Diagnosis; Dimensions; Disease; Disease Pathway; Ecosystem; Elements; Evaluation; Feedback; Genes; Goals; Graph; Health; Image; Informatics; Knowledge; Label; Language; Link; Literature; Malignant Neoplasms; Malignant neoplasm of lung; Manuals; Measures; Medical; Methods; Molecular; Molecular Analysis; Natural Language Processing pipeline; Ontology; Outcome; Oxidative Stress; Pathway interactions; Patients; Performance; Phenotype; PubMed; Publications; Regulation; Reporting; Research; Retrieval; Selection Criteria; Signal Pathway; Source; Structure; System; Techniques; Testing; Text; Training; Translations; Visual; Work; clinical practice; deep learning; design; detector; drug action; graph neural network; image guided; improved; individual patient; knowledge base; knowledge curation; multimodality; neural network architecture; novel; precision medicine; reconstruction; success; text searching; tool; usability

Realization of precision medicine ideas requires an unprecedented rapid pace of translation of biomedical discoveries into clinical practice. However, while many non-canonical disease pathways and uncommon drug actions, which are of vital importance for understanding individual patient-specific disease pathways, are accumulated in the literature, most are not organized in databases. Currently, such knowledge is curated manually or semi-automatically in a very limited scope. Meanwhile, the volume of biomedical information in PubMed (currently 28 million publications) keeps growing by more than a million articles per year, which demands more efficient and effective biocuration approaches. To address this challenge, a novel biocuration method for automatic extraction of disease pathways from figures and text of biomedical articles will be developed. Specific Aim 1: To develop focused benchmark sets of articles to assess the performance of the biocuration pipeline. Specific Aim 2: To develop a method for extraction of components of disease pathways from articles’ figures based on deep-learning techniques. Specific Aim 3: To develop a method for reconstruction of disease-specific pathways through enrichment and through graph neural network (GNN) approaches. Specific Aim 4: To conduct a comprehensive evaluation of the pipeline. The overarching goal of this project is to develop a computer-based automatic biocuration ecosystem for rapid transformation of free-text biomedical literature into a machine-processable format for medical applications. The overall impact of the proposed project will be to significantly improve health outcomes in individualized patient cases by efficiently bringing the latest biomedical discoveries into a precision medicine setting. It will especially benefit cancer patients for which up-to-date knowledge of newly discovered molecular mechanisms and drug actions is critical.

实现精准医学理念需要以前所未有的快节奏翻译生物医学 将这些发现转化为临床实践。然而，尽管许多非经典疾病的途径和罕见的药物 对于了解个别患者特定的疾病途径至关重要的操作包括 在文献中积累起来的，大多数都没有组织在数据库中。目前，这样的知识是经过精心策划的 在非常有限的范围内手动或半自动。与此同时，中国的生物医学信息量 PubMed(目前有2800万种出版物)保持着每年100多万篇文章的增长，这 需要更高效、更有效的生物浓缩方法。 为了解决这一挑战，一种新的生物定位方法用于自动提取来自 将开发生物医学文章的图表和文本。 具体目标1：制定一套有重点的基准条款，以评估生物固化的绩效 输油管道。 具体目标2：开发一种从文章图表中提取疾病途径成分的方法 基于深度学习技术。 具体目标3：开发一种通过浓缩重建疾病特异性途径的方法 并通过图神经网络(GNN)的方法。 具体目标4：对管道进行全面评价。 该项目的总体目标是开发一个基于计算机的自动生物定位生态系统，以 将自由文本生物医学文献快速转换为机器可处理的医学格式 申请。 拟议项目的总体影响将是显著改善#年的健康结果。 通过有效地将最新的生物医学发现精确化，实现患者个体化 药品摆设。它将特别有益于癌症患者，因为他们掌握了最新的 发现分子机制和药物作用至关重要。