Image-guided Biocuration of Disease Pathways From Scientific Literature

实现精准医疗理念需要前所未有的快速生物医学翻译步伐 临床实践的发现。然而，尽管许多非典型疾病途径和不常见的药物 行动，这是至关重要的了解个别病人的具体疾病途径， 在文献中积累的信息，大多数没有在数据库中组织。目前，这些知识是由 在非常有限的范围内手动或半自动地进行。与此同时， PubMed（目前有2800万篇出版物）每年以超过100万篇文章的速度增长， 需要更高效和有效的生物处理方法。 为了解决这一挑战，一种新的生物定位方法，用于自动提取疾病途径， 将编制生物医学文章的图表和文字。 具体目标1：开发集中的基准文章集，以评估生物制剂的性能 渠道. 具体目标2：开发一种从文章图中提取疾病路径成分的方法 基于深度学习技术。 具体目标3：开发通过富集重建疾病特异性途径的方法 图神经网络（GNN）方法。 具体目标4：对管道进行全面评估。 该项目的总体目标是开发一个基于计算机的自动生物固化生态系统， 将自由文本生物医学文献快速转换为可机器处理的医学格式 应用. 拟议项目的总体影响将是显著改善 通过有效地将最新的生物医学发现转化为精确的 医疗设置它将特别有利于癌症患者，因为他们对新的癌症的最新知识 发现分子机制和药物作用至关重要。

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.