Biological Topology-Semantic Enhanced Heterogeneous Graph Representation Learning for Drug-Microbe Interactions
GUO Quanming1, GUO Yanbu1,2, SONG Shengli1, CHEN Zihao1, ZHU Haokun1
1. College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002
2. Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, Southeast University, Nanjing 211189
The interaction between microorganisms and drugs significantly impacts human health. In existing association prediction methods, the internal node information of heterogeneous graphs is not adequately modeled and the importance of different meta-path instances is overlooked. Hence, a biological topology-semantic enhanced heterogeneous graph representation learning for drug-microbe interactions(HGRL) method is proposed. High-order mixed neighborhood network embedding representations are extracted to infer microorganism-drug associations. Microorganism-drug similarity and association data are integrated to construct a weighted bidirectional heterogeneous network and a multi-view meta-path aware network. The transformer-gated graph network is combined with Bayesian Gaussian mixture weighted contrastive learning to extract topological semantics and embedding features of complex biological networks. Prediction based on adversarial negative sampling demonstrates that HGRL outperforms existing methods in microorganism-drug association prediction and is a reliable tool for inferring microorganisms associated with candidate drugs.
[1] LAI Y P, GALLO R L. AMPed up Immunity: How Antimicrobial Peptides Have Multiple Roles in Immune Defense. Trends in Immunology, 2009, 30(3): 131-141.
[2] MAIER L, PRUTEANU M, KUHN M, et al. Extensive Impact of Non-antibiotic Drugs on Human Gut Bacteria. Nature, 2018, 555(7698): 623-628.
[3] CHO J. Downstream Regulatory Element Antagonist Modulator(DREAM), A Target for Anti-Thrombotic Agents. Pharmacological Research, 2017, 117: 283-287.
[4] SUN Y Z, ZhANG D H, CAI S B, et al. MDAD: A Special Resource for Microbe-Drug Associations. Frontiers in Cellular and Infection Microbiology, 2018, 8. DOI: 10.3389/fcimb.2018.00424.
[5] RAJPUT A, THAKUR A, SHARMA S, et al. aBiofilm: A Resource of Anti-Biofilm Agents and Their Potential Implications in Targeting Antibiotic Drug Resistance. Nucleic Acids Research,2017, 46(D1): D894-D900.
[6] ANDERSEN P I, IANEVSKI A, LYSVAND H, et al. Discovery and Development of Safe-in-Man Broad-Spectrum Antiviral Agents. International Journal of Infectious Diseases, 2020, 93: 268-276.
[7] ZHU L Z, DUAN G H, YAN C, et al. Prediction of Microbe-Drug Associations Based on KATZ Measure // Proc of the IEEE International Conference on Bioinformatics and Biomedicine. Washington, USA: IEEE, 2019: 183-187.
[8] WANG B, MA F J, DU X X, et al. Prediction of Microbe-Drug Associations Based on a Modified Graph Attention Variational Autoencoder and Random Forest. Frontiers in Microbiology, 2024, 15. DOI: 10.3389/fmicb.2024.1394302.
[9] 谭好江,王 峻,余国先,等.基于个性化随机游走的基因-表型关联分析.电子学报, 2024, 52(5): 1619-1632.
(TAN H J, WANG J, YU G X, et al. Individual Random Walks for Gene-Phenotype Association Analysis. Acta Electronica Sinica, 2024, 52(5): 1619-1632.)
[10] 饶晓洁,张 通,孟献兵,等.基于多层注意力和消息传递网络的药物相互作用预测方法.自动化学报, 2023, 49 (12): 2507-2519.
(RAO X J, ZHANG T, MENG X B, et al. Drug-Drug Interaction Prediction Method Based on Multi-level Attention Mechanism and Message Passing Neural Network. Acta Automatica Sinica, 2023, 49(12): 2507-2519.)
[11] LONG Y H, WU M, KWOH C K, et al. Predicting Human Microbe-Drug Associations via Graph Convolutional Network with Conditional Random Field. Bioinformatics, 2020, 36(19): 4918-4927.
[12] LONG Y H, WU M, LIU Y, et al. Ensembling Graph Attention Networks for Human Microbe-Drug Association Prediction. Bioinformatics, 2020, 36(S): i779-i786.
[13] LONG Y H, LUO J W. Association Mining to Identify Microbe Drug Interactions Based on Heterogeneous Network Embedding Representation. IEEE Journal of Biomedical and Health Informa-tics, 2021, 25(1): 266-275.
[14] TIAN Z, YU Y, FANG H C, et al. Predicting Microbe-Drug Associations with Structure-Enhanced Contrastive Learning and Self-Paced Negative Sampling Strategy. Briefings in Bioinformatics, 2023, 24(2). DOI: 10.1093/bib/bbac634.
[15] DENG L, HUANG Y B, LIU X J, et al. Graph2MDA: A Multi-modal Variational Graph Embedding Model for Predicting Microbe-Drug Associations. Bioinformatics, 2022, 38(4): 1118-1125.
[16] KAMNEVA O K. Genome Composition and Phylogeny of Microbes Predict Their Co-occurrence in the Environment. PLoS Computational Biology, 2017, 13(2). DOI: 10.1371/journal.pcbi.1005366.
[17] HATTORI M, TANAKA N, KANEHISA M, et al. SIMCOMP/SUBCOMP: Chemical Structure Search Servers for Network Analyses. Nucleic Acids Research, 2010, 38(S2): W652-W656.
[18] VAN LAARHOVEN T, NABUURS S B, MARCHIORI E. Gaussian Interaction Profile Kernels for Predicting Drug-Target Interaction. Bioinformatics, 2011, 27(21): 3036-3043.
[19] 秦海盈,赵中英,李建晖,等.基于元路径与层次注意力的时序异质信息网络表示学习方法.模式识别与人工智能, 2021, 34(12): 1093-1102.
(QIN H Y, ZHAO Z Y, LI J H, et al. Meta-Path and Hierarchical Attention Based Temporal Heterogeneous Information Network Representation Learning. Pattern Recognition and Artificial Intelligence, 2021, 34(12): 1093-1102.)
[20] 张玉朋,李香菊,李 超,等.基于Transformer与异质图神经网络的新闻推荐模型.模式识别与人工智能, 2022, 35(9): 839-848.
(ZHANG Y P, LI X J, LI C, et al. News Recommendation Model Based on Transformer and Heterogeneous Graph Neural Network. Pattern Recognition and Artificial Intelligence, 2022, 35(9): 839-848.)
[21] BAI M L, ZHOU Z H, LI J J, et al. Deep Graph Gated Recurrent Unit Network-Based Spatial-Temporal Multi-task Learning for Inte-lligent Information Fusion of Multiple Sites with Application in Short-Term Spatial-Temporal Probabilistic Forecast of Photovoltaic Power. Expert Systems with Applications, 2024, 240. DOI: 10.1016/j.eswa.2023.122072.
[22] WU X Y, AJORLOU A, WANG Y F, et al. On the Role of Attention Masks and LayerNorm in Transformers[C/OL]. [2024-10-20]. https://arxiv.org/abs/2405.18781.
[23] GLOROT X, BENGIO Y. Understanding the Difficulty of Training Deep Feedforward Neural Networks // Proc of the 13th Internatio-nal Conference on Artificial Intelligence and Statistics. San Diego, USA: JMLR, 2010: 249-256.
[24] REYAD M, SARHAN A M, ARAFA M. A Modified Adam Algorithm for Deep Neural Network Optimization. Neural Computing and Applications, 2023, 35(23): 17095-17112.
[25] ZHANG C, SHAO Y C, SUN H J, et al. The WuC-Adam Algorithm Based on Joint Improvement of Warmup and Cosine Annealing Algorithms. Mathematical Biosciences and Engineering, 2024, 21(1): 1270-1285.
[26] KIPF T N, WELLING M. Semi-Supervised Classification with Graph Convolutional Networks[C/OL]. [2024-10-20]. https://arxiv.org/abs/1609.02907.
[27] VELICˇKOVIC＇ P, CUCURULL G, CASANOVA A, et al. Graph Attention Networks[C/OL]. [2024-10-20]. https://arxiv.org/abs/1710.10903.
[28] YU Z X, HUANG F, ZHAO X H, et al. Predicting Drug-Disease Associations through Layer Attention Graph Convolutional Network. Briefings in Bioinformatics, 2021, 22(4). DOI: 10.1093/bib/bbaa243.
[29] TAN Y Q, ZOU J, KUANG L N, et al. GSAMDA: A Computational Model for Predicting Potential Microbe-Drug Associations Based on Graph Attention Network and Sparse Autoencoder. BMC Bioinformatics, 2022, 23(1). DOI: 10.1186/s12859-022-05053-7.
[30] TANG X R, LUO J W, SHEN C, et al. Multi-view Multichannel Attention Graph Convolutional Network for miRNA-Disease Association Prediction. Briefings in Bioinformatics, 2021, 22(6). DOI: 10.1093/bib/bbab174.
[31] MA Q, TAN Y Q, WANG L. GACNNMDA: A Computational Model for Predicting Potential Human Microbe-Drug Associations Based on Graph Attention Network and CNN-Based Classifier. BMC Bioinformatics, 2023, 24(1). DOI: 10.1186/s12859-023-05158-7.
2024, Vol. 37 
