Protein Knowledge Networks and Semantic Computing for Disease Discovery

用于疾病发现的蛋白质知识网络和语义计算

• 批准号: 10698082
• 负责人: CATHY H. WU
• 金额: $ 43.34万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-25 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698082
• 关键词: Acceleration; Address; Adopted; Alzheimer' s Disease; Applications Grants; Biomedical Research; COVID-19; Case Study; Code; Communities; Computational algorithm; Computer Systems; Data; Databases; Disease; Drug Targeting; Educational workshop; Ensure; FAIR principles; Fostering; Generations; Genes; Genotype; Growth; Health; Human; Information Retrieval; Knowledge; Knowledge Discovery; Link; Literature; Machine Learning; Mining; Modeling; Modification; Morphologic artifacts; Natural Language Processing; Ontology; Organism; Pharmaceutical Preparations; Phenotype; Post-Translational Protein Processing; Protein Analysis; Proteins; Reproducibility; Research; Resource Description Framework; Resource Sharing; Resources; Scientist; Semantics; System; Systems Development; Text; Training; Variant; bioinformatics infrastructure; community engagement; computational reasoning; data modeling; deep learning; drug repurposing; fundamental research; hackathon; human disease; improved; learning strategy; pandemic disease; text searching; tool; web site

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.

蛋白质知识网络和语义计算在疾病发现中的应用 科学文献和生物医学数据库中不断增长的信息量和广度 向研究界提出挑战，以利用内容进行发现。这笔米拉奖金 应用将推进我们的知识挖掘和语义计算系统，以加速数据驱动 了解基因-疾病-药物关系的发现。我们使用了自然语言 生物实体和关系泛化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 用于人类理解和计算推理的生物医学数据。我们还开发了一种 支持蛋白质翻译后修饰的功能解释和分析的资源 (PTMS)跨修饰类型和生物体。以我们的计算算法为基础， 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，以 支持跨书目和开放链接数据模型的自动信息提取，以实现语义 整合来自不同来源的生物医学数据。我们的文本挖掘工具将针对 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)在日益可计算、可推断和可解释的知识中的语义模型 辅助假设生成的系统。我们将以文本文物的形式提供证据 语义模型确保对结果的无偏见分析和解释，以促进严谨和 可重复的研究。我们将开展科学的案例研究，以推动系统开发。 例如，用于药物靶标识别的PTM疾病变异和浓缩分析、基因分型-- 阿尔茨海默病的表型知识挖掘和基因-疾病-药物知识 新冠肺炎毒品再利用网络建设。为了促进社区参与，我们将主办 研讨会和黑客松，以解决关键的基础研究问题和新出现的疾病 场景。我们完全采用了公平(可发现、可访问、可互操作、可重复使用)的原则 资源共享。所有数据、工具和研究成果都将通过该项目广泛传播 网站，可通过REST风格的API以编程方式访问，可通过SPARQL端点查询，以及 停靠以实现社区代码重用。因此，这项研究的成功完成将支持 可扩展、集成和协作的知识发现，以加快对疾病的了解和 药物靶点的发现。

项目成果

Global analysis of switchgrass (Panicum virgatum L.) transcriptomes in response to interactive effects of drought and heat stresses.

• DOI: 10.1186/s12870-022-03477-0
• 发表时间: 2022-03-08
• 期刊: BMC plant biology
• 影响因子: 5.3
• 作者: Hayford RK;Serba DD;Xie S;Ayyappan V;Thimmapuram J;Saha MC;Wu CH;Kalavacharla VK
• 通讯作者: Kalavacharla VK

KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.

• DOI: 10.7717/peerj.16164
• 发表时间: 2023
• 期刊: PeerJ
• 影响因子: 2.7
• 作者: Anandakrishnan M;Ross KE;Chen C;Shanker V;Cowart J;Wu CH
• 通讯作者: Wu CH

Text mining of CHO bioprocess bibliome: Topic modeling and document classification.

• DOI: 10.1371/journal.pone.0274042
• 发表时间: 2023
• 期刊: PloS one
• 影响因子: 3.7

A knowledge graph representation learning approach to predict novel kinase-substrate interactions.

• DOI: 10.1039/d1mo00521a
• 发表时间: 2022-10-31
• 期刊: Molecular omics
• 影响因子: 2.9