National Genomics Data Center & Bio-Med Big Data Center, Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Shanghai Information Center for Life Sciences, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Database (Oxford). 2024 Sep 27;2024. doi: 10.1093/database/baae102.
Personalized medicine tailors treatments and dosages based on a patient's unique characteristics, particularly its genetic profile. Over the decades, stratified research and clinical trials have uncovered crucial drug-related information-such as dosage, effectiveness, and side effects-affecting specific individuals with particular genetic backgrounds. This genetic-specific knowledge, characterized by complex multirelationships and conditions, cannot be adequately represented or stored in conventional knowledge systems. To address these challenges, we developed CPMKG, a condition-based platform that enables comprehensive knowledge representation. Through information extraction and meticulous curation, we compiled 307 614 knowledge entries, encompassing thousands of drugs, diseases, phenotypes (complications/side effects), genes, and genomic variations across four key categories: drug side effects, drug sensitivity, drug mechanisms, and drug indications. CPMKG facilitates drug-centric exploration and enables condition-based multiknowledge inference, accelerating knowledge discovery through three pivotal applications. To enhance user experience, we seamlessly integrated a sophisticated large language model that provides textual interpretations for each subgraph, bridging the gap between structured graphs and language expressions. With its comprehensive knowledge graph and user-centric applications, CPMKG serves as a valuable resource for clinical research, offering drug information tailored to personalized genetic profiles, syndromes, and phenotypes. Database URL: https://www.biosino.org/cpmkg/.
个体化医学根据患者的独特特征，特别是其基因谱，为患者量身定制治疗和剂量。几十年来，分层研究和临床试验已经揭示了关键的药物相关信息，例如影响特定遗传背景个体的剂量、有效性和副作用。这种以复杂的多对多关系和条件为特征的特定于基因的知识，无法在传统知识系统中得到充分表示或存储。为了解决这些挑战，我们开发了 CPMKG，这是一个基于条件的平台，能够实现全面的知识表示。通过信息提取和精心策展，我们编译了 307614 个知识条目，涵盖了四个关键类别中的数千种药物、疾病、表型（并发症/副作用）、基因和基因组变异：药物副作用、药物敏感性、药物机制和药物适应症。CPMKG 促进以药物为中心的探索，并实现基于条件的多知识库推理，通过三个关键应用加速知识发现。为了增强用户体验，我们无缝集成了一个复杂的大型语言模型，该模型为每个子图提供文本解释，在结构化图和语言表达之间架起了桥梁。CPMKG 凭借其全面的知识图谱和以用户为中心的应用程序，为临床研究提供了宝贵的资源，为个性化基因谱、综合征和表型提供了量身定制的药物信息。数据库 URL：https://www.biosino.org/cpmkg/。
