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）
Yi Nian, Xinyue Hu, Rui Zhang, Jingna Feng, Jingcheng Du, Fang Li, Yong Chen, Cui Tao
To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. Among three knowledge graph completion models, TransE outperformed the other two (MR = 13.45, Hits@1 = 0.306). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.
We report development of a data infrastructure for drug repurposing that takes advantage of two currently available chemical ontologies. The data infrastructure includes a database of compound- target associations augmented with molecular ontological labels. It also contains two computational tools for prediction of new associations. We describe two drug-repurposing systems: one, Nascent Ontological Information Retrieval for Drug Repurposing (NOIR-DR), based on an information retrieval strategy, and another, based on non-negative matrix factorization together with compound similarity, that was…
Drug repurposing is more relevant than ever due to drug development's rising costs and the need to respond to emerging diseases quickly. Knowledge graph embedding enables drug repurposing using heterogeneous data sources combined with state-of-the-art machine learning models to predict new drug-disease links in the knowledge graph. As in many machine learning applications, significant work is still required to understand the predictive models' behavior. We propose a structured methodology to understand better machine learning models' results for drug repurposing, suggesting key elements of the…
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）
Knowledge graph (KG) is used to represent data in terms of entities and structural relations between the entities. This representation can be used to solve complex problems such as recommendation systems and question answering. In this study, a set of candidate drugs for COVID-19 are proposed by using Drug repurposing knowledge graph (DRKG). DRKG is a biological knowledge graph constructed using a vast amount of open source biomedical knowledge to understand the mechanism of compounds and the related biological functions. Node and relation embeddings are learned using knowledge graph embedding…
DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Stevens Institute of Technology（史蒂文斯理工学院）；Emory University（埃默里大学）；Massachusetts Institute of Technology（麻省理工学院）
AstraZeneca（阿斯利康）；University of Cambridge（剑桥大学）；Harvard Medical School（哈佛医学院）；McGill University（麦吉尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）
Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritisation. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, whilst relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data is …
An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication
Milner Therapeutics Institute, University of Cambridge（剑桥大学米尔纳治疗学研究所）；LifeArc；KAIST（韩国科学技术院）；University College Dublin（都柏林大学学院）；Dataiku；University of London（伦敦大学）；University of Surrey（萨里大学）；Imperial College London（伦敦帝国学院）；Thalesians Ltd（Thalesians有限公司）；The University of Manchester（曼彻斯特大学）
Siemens AG（西门子股份公司）；Ludwig Maximilian University of Munich（慕尼黑大学）
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range dependencies, which can be captured by patterns described as logical rules. We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning. We conduct an empirical study based on the real-world task of drug repurposing by formulating this task as a link prediction problem. We apply our method to the biomedical knowledge graph Hetionet and show that our approac…
DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search
City University of Hong Kong, Hong Kong, PR China（香港城市大学）；City University of Hong Kong Shenzhen Research Institute, Shenzhen, PR China（香港城市大学深圳研究院）；The Hong Kong University of Science and Technology (Guangzhou), Guangzhou PR China（香港科学与技术大学（广州））；GreenDynamics, Sydney, NSW, Australia（GreenDynamics公司）；University of New South Wales, Kensington, NSW, Australia（新南威尔士大学）
RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Information Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息中心）；College of Computer Science, Nankai University（南开大学计算机学院）；Department of Computer Science, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Université de Paris（巴黎大学）；Inserm（法国国家健康与医学研究院）；Université Lille（里尔大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Centrale Lille（里尔中央理工学院）；Université Sorbonne Paris Nord（巴黎第十三大学）；Assistance Publique des Hôpitaux de Paris（巴黎公共医疗援助医院）
Motivated by an application to drug repurposing, we propose the first algorithms to tackle the identification of the m $\ge$ 1 arms with largest means in a linear bandit model, in the fixed-confidence setting. These algorithms belong to the generic family of Gap-Index Focused Algorithms (GIFA) that we introduce for Top-m identification in linear bandits. We propose a unified analysis of these algorithms, which shows how the use of features might decrease the sample complexity. We further validate these algorithms empirically on simulated data and on a simple drug repurposing task.
Inria Saclay（法国国家信息与自动化研究所萨克莱分所）；Univ. Paris Saclay（巴黎萨克雷大学）；IIIT - Delhi（德里英迪拉·甘地信息技术学院）
Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is pro…
Large Language Models Meet Biomedical Knowledge Graphs for Mechanistically Grounded Therapeutic Prioritization
National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆）；National Cancer Institute (NCI), National Institutes of Health (NIH)（美国国立卫生研究院国家癌症研究所）；Dartmouth Health（达特茅斯医疗系统）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
McMaster University（麦克马斯特大学）；Rotman Research Institute at Baycrest, University of Toronto（多伦多大学贝克莱斯特罗特曼研究所）；Ryerson University（瑞尔森大学）
Recently, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is a powerful machine learning algorithm in classification while extracting low to high-level features. In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain. The importance of classifying this kind of medical data …
DrugGen 2: A disease-aware language model for enhancing drug discovery
Regenerative Medicine Research Center（再生医学研究中心）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Isfahan Neuroscience Research Center（伊斯法罕神经科学研究中心）；School of Pharmacy（药学院）；Department of Medicinal Chemistry（药理化学系）；Cardiovascular Research Institute（心血管研究学院）；Department of Genetics and Molecular Biology（基因与分子生物学系）；Bioinformatics Research Center（生物信息学研究中心）
