[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.