Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；School of Computer Science and Technology, Nanjing Tech University（南京工业大学计算机科学与技术学院）
Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Therefore, screening of microbe-drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe-drug associations. In this paper, we proposed a novel method, Graph2MDA, to predict microbe-drug associations by using variational graph autoencoder (VGAE). We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences, and function annotations. Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier was used to predict microbe-drug associations. The hyperparameter analysis and model ablation studies showed the sensitivity and robustness of our model. We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods. We also explored the meaningness of the learned latent representations of drugs and found that the drugs show obvious clustering patterns that are significantly consistent with drug ATC classification. Moreover, we conducted case studies on two microbes and two drugs and found 75\%-95\% predicted associations have been reported in PubMed literature. Our extensive performance evaluations validated the effectiveness of our proposed method.\
Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction
College of Informatics, Huazhong Agricultural University（华中农业大学信息学院）；Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University（华中农业大学农业生物信息学湖北省重点实验室）
Computational Approaches for Predicting Drug-Disease Associations: A Comprehensive Review
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）
BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions
SenseTime Research（商汤科技研究院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Qing yuan Research Institute, Shanghai Jiao Tong University（上海交通大学清源研究院）
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions
School of Electronics and Information Engineering, Xi’an Jiaotong University（西安交通大学电子与信息工程学院）；College of Geomatics, Xi’an University of Science and Technology（西安科技大学测绘学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Boosting drug-disease association prediction for drug repositioning via dual-feature extraction and cross-dual-domain decoding
Institute of Computing Science and Technology, Guangzhou University（广州大学计算科学与技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Electronics and Communication Sciences Unit, Indian Statistical Institute（印度统计研究所电子与通信科学部）
CrossADR: enhancing adverse drug reactions prediction for combination pharmacotherapy with cross-layer feature integration and cross-level associative learning
School of Mathematical Sciences（数学科学学院）；Shanghai Key Laboratory of PMMP（PMMP上海重点实验室）；East China Normal University（华东师范大学）；Shanghai（上海）；China（中国）
Node2Vec-DGI-EL: A Hierarchical Graph Representation Learning Model for Ingredient-Disease Association Prediction
Medical Engineering Technology and Data Mining Institute, Zhengzhou University（郑州大学医学工程技术与数据挖掘研究所）；Faculty of Innovation Engineering, Macau University of Science and Technology（澳门科技大学创新工程学院）；School of Pharmacy, Macau University of Science and Technology（澳门科技大学药学院）
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization
Helsinki Institute for Information Technology HIIT（赫尔辛基信息技术研究所）；Aalto University（阿尔托大学）；Institute for Molecular Medicine Finland FIMM（芬兰分子医学研究所）；University of Helsinki（赫尔辛基大学）；Science for Life Laboratory（生命科学实验室）；Karolinska Institutet（卡罗林斯卡学院）；University of Turku（图尔库大学）
GLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University（生物数据科学实验室，计算机工程系，哈切泰佩大学）；Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University（生物信息学系，健康科学研究生院，哈切泰佩大学）；Dept. of Health Informatics, Institute of Informatics, Hacettepe University（健康信息学系，信息学院，哈切泰佩大学）
SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19
新型人类冠状病毒COVID-19/SARS-CoV-2的新颖性以及缺乏有效的药物和疫苗，导致采用各种各样的策略来对抗这一全球大流行病。这些策略中的许多依赖于现有药物的重新定位，与从头药物发现相比，这可以缩短时间并降低成本。在这项研究中，我们提出了一种新的基于网络的药物重新定位算法，称为SAveRUNNER（Searching off-lAbel dRUg aNd NEtwoRk），它通过一种新的基于网络的相似性度量来量化药物靶标和人类相互作用组中疾病特异性蛋白质之间的相互作用，从而预测药物-疾病关联，该相似性度量优先考虑位于相同网络邻域中的药物和疾病之间的关联。具体而言，我们将SAveRUNNER应用于14种选定疾病的小组，这些疾病具有关于其致病基因的综合知识，并且已发现与COVID-19相关的遗传相似性，共病或与暂时重新用于治疗COVID-19的药物的关联。特别关注SARS子网络，我们确定了282种可重复使用的药物，包括一些最有传言的COVID-19治疗标签外药物，以及实际用于临床实践的5种药物的新组合疗法。此外，为了最大限度地提高推定的下游验证实验的效率，我们根据其基于网络的相似性值优先考虑了24种潜在的抗SARS-CoV可再利用药物。这些排名靠前的药物包括ACE抑制剂、单克隆抗体和凝血酶抑制剂。最后，我们的研究结果通过基因集富集分析进行了计算机验证，证实了大多数网络预测的可重…
PGCLODA: Prompt-Guided Graph Contrastive Learning for Oligopeptide-Infectious Disease Association Prediction
Institute of Physical Science and Information Technology（物理科学与信息技术研究所）；Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education（教育部智能计算与信号处理重点实验室）；School of Artificial Intelligence（人工智能学院）
Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
School of Computer Science and Engineering, Hunan University（湖南大学计算机科学与工程学院）；AWS Shanghai AI Lab（亚马逊云科技上海人工智能实验室）；Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic（克利夫兰诊所勒纳研究所基因组医学研究所）；New York University Shanghai（上海纽约大学）；AWS AI（亚马逊云科技人工智能部门）；University of Minnesota（明尼苏达大学）；Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University（凯斯西储大学克利夫兰诊所勒纳医学院分子医学系）；Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine（凯斯西储大学医学院凯斯综合癌症中心）
