Drug Repurposing for COVID-19 via Knowledge Graph Completion
University of Minnesota（明尼苏达大学）；University of Ljubljana（卢布尔雅那大学）；Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Rui Zhang, Dimitar Hristovski, Dalton Schutte, Andrej Kastrin, Marcelo Fiszman, Halil Kilicoglu
备注：
47 pages, 3 figures, Accepted to Journal of Biomedical Informatics
Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. Methods: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from both PubMed and COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant, and used this subset to construct a knowledge graph. Five SOTA, neural knowledge graph completion algorithms were used to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach. Results: Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1=0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as some candidate drugs that have not yet been studied. Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, pyrrolidine dithiocarbamate, and butylated hydroxytoluene) with their mechanistic explanations were further discussed. Conclusion: We show that an LBD approach can be feasible for discovering drug candidates for COVID-19, and for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions.
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies
IBM Research（IBM研究院）；University of Melbourne（墨尔本大学）；Cures Within Reach for Cancer（癌症可及疗法组织）；Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
University of Texas Health Science Center at Houston（休斯顿德克萨斯大学健康科学中心）；University of Minnesota（明尼苏达大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Maryland School of Medicine（马里兰大学医学院）
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University（哥伦比亚大学）；Brandeis University（布兰迪斯大学）；University of Washington（华盛顿大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Colorado University（科罗拉多大学）；Army Research Lab（陆军研究实验室）；QS2；Department of Defense（国防部）
R-BERT-CNN: Drug-target interactions extraction from biomedical literature
University of Helsinki（赫尔辛基大学）
在这项研究中，我们提出了我们的工作参与BioCreative VII挑战的DrugProt任务。药物-靶点相互作用（DTI）是药物发现和再利用的关键，通常从实验文章中手动提取。PubMed上有超过3200万篇生物医学文章，从如此庞大的知识库中手动提取DTI具有挑战性。为了解决这个问题，我们为Track 1提供了一个解决方案，旨在提取药物和蛋白质实体之间的10种相互作用。我们应用了一个Ensemble分类器模型，该模型结合了BioMed-RoBERTa（一种最先进的语言模型）和卷积神经网络（CNN）来提取这些关系。尽管BioCreative VII DrugProt测试语料库中存在类别不平衡，但与挑战中其他提交的平均值相比，我们的模型实现了良好的性能，微F1得分为55.67%（BioCreative VI ChemProt测试语料库为63%）。结果显示了深度学习在提取各种类型的DTI方面的潜力。
UniD$^3$: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning
Department of Health Outcomes and Biomedical Informatics, University of Florida（佛罗里达大学健康成果与生物医学信息学系）；Department of Hematology, H. Lee Moffitt Cancer Center and Research Institute（血液科，H. Lee Moffitt癌症中心与研究院）；Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute（生物信息学与计算生物学中心，休斯顿方法主义研究学院）；Department of Cardiothoracic Surgery, Weill Cornell Medicine, Cornell University（心胸外科，Weill Cornell医学，康奈尔大学）；Department of Biostatistics and Health Data Science, Indiana University School of Medicine（生物统计学与健康数据科学系，印第安纳大学医学院）
Understanding COVID-19 News Coverage using Medical NLP
John Snow Labs inc.（约翰·斯诺实验室公司）
COVID-19疫情为全球性大流行病，受到全球媒体关注。在这项研究中，我们分析了CNN和卫报-世界上最有影响力的两个媒体组织的新闻出版物。该数据集包括36，000多篇文章，使用Spark NLP for Healthcare库中的临床和生物医学自然语言处理（NLP）模型进行分析，这使得对医学概念的分析比以前更深入。该分析涵盖了关键实体和短语，观察到的偏见，以及通过将挖掘的医疗症状，程序，药物和指导与常见的人口统计和职业群体相关联来改变新闻报道。另一项分析是关于药物和疫苗制造商的提取的药物不良事件，当主要新闻媒体报道时，会对疫苗犹豫产生影响。
