Protein Knowledge Networks and Semantic Computing for Disease Discovery

蛋白质知识网络和语义计算在疾病发现中的应用 来自科学文献和生物医学数据库的信息数量和广度不断增长 对研究界提出了挑战，以利用内容进行发现。这个MIRA补助金 应用程序将推进我们的知识挖掘和语义计算系统，以加速数据驱动 基因-疾病-药物关系的发现。我们使用自然语言 生物实体和关系的可推广框架中的处理和机器学习方法 从大规模文本中提取。我们的蛋白质本体支持以蛋白质为中心的语义集成， 人类理解和计算推理的生物医学数据。我们还开发了一个 支持蛋白质翻译后修饰的功能解释和分析的资源 （PTM）跨修饰类型和生物体。基于我们的计算算法， 生物信息学基础设施和社区互动，我们将进一步开发文献挖掘工具， 支持跨书目组的自动信息提取和开放链接数据模型， 整合来自异构资源的生物医学数据。我们的文本挖掘工具将接受培训， 使用深度学习方法的不同用例。我们将开发RDF（资源描述 框架）语义模型在一个日益可计算，可推断和可解释的知识 系统，以协助假设生成。我们将以文字文物的形式呈现证据， 语义模型，以确保公正的分析和解释的结果，以促进严格和 可复制的研究我们将开发科学的案例研究，以推动系统的开发。 实例包括用于药物靶点鉴定的PTM疾病变体和富集分析、基因型- 用于阿尔茨海默病理解的表型知识挖掘，以及基因-疾病-药物知识 为COVID-19药物再利用进行网络建设。为了促进社区参与，我们将举办 研讨会和黑客马拉松，以解决关键的基础研究问题和新出现的疾病 场景我们完全采用了FAIR（Findable，Interoperable，Interoperable，Reusable）原则， 资源共享项目的所有数据、工具和研究成果都将广泛传播 网站，可通过RESTful API编程访问，可通过SPARQL端点查询，以及 dockerized社区代码重用。这项研究的成功完成将有助于 可扩展的、综合的和协作的知识发现，以加速对疾病的理解， 药物靶点发现

Protein Knowledge Networks and Semantic Computing for Disease Discovery The growing volume and breadth of information from the scientific literature and biomedical databases pose challenges to the research community to exploit the content for discovery. This MIRA grant application will advance our knowledge mining and semantic computing system to accelerate data-driven discovery for understanding of gene-disease-drug relationships. We have employed natural language processing and machine learning approaches in a generalizable framework for bioentity and relation extraction from large-scale text. Our Protein Ontology supports protein-centric semantic integration of biomedical data for both human understanding and computational reasoning. We have also developed a resource to support functional interpretation and analysis of protein post-translational modifications (PTMs) across modification types and organisms. Building on our computational algorithms, bioinformatics infrastructure and community interactions, we will further develop literature mining tools to support automated information extraction across the bibliome and open linked data models for semantic integration of biomedical data from heterogeneous resources. Our text mining tools will be trained for different use cases using deep learning methods. We will develop RDF (Resource Description Framework) semantic models in an increasingly computable, inferable and explainable knowledge system to assist in hypothesis generation. We will present evidence in the form of textual artifacts and semantic models to ensure unbiased analysis and interpretation of results to promote rigorous and reproducible research. We will develop scientific case studies to drive the system development. Examples include PTM disease variant and enrichment analyses for drug target identification, genotype- phenotype knowledge mining for Alzheimer's Disease understanding, and gene-disease-drug knowledge network construction for COVID-19 drug repurposing. To foster community engagement, we will host workshops and hackathons to address critical fundamental research questions and emerging disease scenarios. We have fully adopted the FAIR (Findable, Accessible, Interoperable, Reusable) principles for resource sharing. All data, tools and research results will be broadly disseminated from the project website, accessible programmatically via RESTful API, queryable via SPARQL endpoints, and dockerized for community code reuse. The successful completion of this research will thus support scalable, integrative and collaborative knowledge discovery to accelerate disease understanding and drug target discovery.