Cénit M, Matzaraki V, Tigchelaar E, Zhernakova A. Rapidly expanding knowledge on the role of the gut microbiome in health and disease. Biochimica et Biophysica Acta -Molecular Basis of Disease. 2014;1842(10):1981–92.
Sommer F, Bäckhed F. The gut microbiota—masters of host development and physiology. Nat Rev Microbiol. 2013;11(4):227–38.
Article CAS PubMed Google Scholar
Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207–14.
Gill SR, Pop M, DeBoy RT, Eckburg PB, Turnbaugh PJ, Samuel BS, Gordon JI, Relman DA, Fraser-Liggett CM, Nelson KE. Metagenomic analysis of the human distal gut microbiome. Science. 2006;312(5778):1355–9.
Article CAS PubMed PubMed Central Google Scholar
Cross ML. Microbes versus microbes: immune signals generated by probiotic lactobacilli and their role in protection against microbial pathogens. FEMS Immunol Med Microbiol. 2002;34(4):245–53.
Article CAS PubMed Google Scholar
Henao-Mejia J, Elinav E, Thaiss CA, Licona-Limon P, Flavell RA. Role of the intestinal microbiome in liver disease. J Autoimmun. 2013;46:66–73.
Article CAS PubMed Google Scholar
Wen L, Ley RE, Volchkov PY, Stranges PB, Avanesyan L, Stonebraker AC, Hu C, Wong FS, Szot GL, Bluestone JA. Innate immunity and intestinal microbiota in the development of Type 1 diabetes. Nat Methods. 2008;455(7216):1109–13.
Huang YJ, Boushey HA. The microbiome in asthma. J Allergy Clin Immunol. 2015;135(1):25–30.
Article PubMed PubMed Central Google Scholar
Schwabe RF, Jobin C. The microbiome and cancer. Nat Rev Cancer. 2013;13(11):800–12.
Article CAS PubMed PubMed Central Google Scholar
Feng J, Wu S, Yang H, Ai C, Qiao J, Xu J, Guo F. Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion. Brief Bioinform. 2022;23(6):bbac423.
Wang L, Yang X, Kuang L, Zhang Z, Zeng B, Chen Z. Graph convolutional neural network with multi-layer attention mechanism for predicting potential microbe-disease associations. Curr Bioinform. 2023;18(6):497–508.
Wang L, Li H, Wang Y, Tan Y, Chen Z, Pei T, Zou Q. MDADP: a webserver integrating database and prediction tools for microbe-disease associations. IEEE J Biomed Health Inform. 2022;26(7):3427–34.
McCoubrey LE, Gaisford S, Orlu M, Basit AW. Predicting drug-microbiome interactions with machine learning. Biotechnol Adv. 2022;54: 107797.
Article CAS PubMed Google Scholar
Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, Goodman AL. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature. 2019;570(7762):462–7.
Article CAS PubMed PubMed Central Google Scholar
Panebianco C, Andriulli A, Pazienza V. Pharmacomicrobiomics: exploiting the drug-microbiota interactions in anticancer therapies. Microbiome. 2018;6:1–13.
Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A, Anderson EE, Brochado AR, Fernandez KC, Dose H, Mori H. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature. 2018;555(7698):623–8.
Article CAS PubMed PubMed Central Google Scholar
Wang R, Jiang Y, Jin J, Yin C, Yu H, Wang F, Feng J, Su R, Nakai K, Zou Q. DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis. Nucleic Acids Res. 2023;51(7):3017–29.
Article CAS PubMed PubMed Central Google Scholar
Wang L, Tan Y, Yang X, Kuang L, Ping P. Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. Brief Bioinform. 2022;23(3):bbac080.
Wen Z, Yan C, Duan G, Li S, Wu F-X, Wang J. A survey on predicting microbe-disease associations: biological data and computational methods. Brief Bioinform. 2021;22(3):bbaa157.
Grover, Aditya, and Jure Leskovec. node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2016. 2016. p. 855–64.
Lei X, Wang Y. Predicting microbe-disease association by learning graph representations and rule-based inference on the heterogeneous network. Front Microbiol. 2020;11:579.
Article CAS PubMed PubMed Central Google Scholar
Peng L, Shen L, Liao L, Liu G, Zhou L. RNMFMDA: a microbe-disease association identification method based on reliable negative sample selection and logistic matrix factorization with neighborhood regularization. Front Microbiol. 2020;11:592430.
Article PubMed PubMed Central Google Scholar
Xu D, Xu H, Zhang Y, Wang M, Chen W, Gao R. MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities. J Transl Med. 2021;19:1–12.
Long Y, Luo J, Zhang Y, Xia Y. Predicting human microbe–disease associations via graph attention networks with inductive matrix completion. Brief Bioinform. 2021;22(3):bbaa146.
Tao W, Liu Y, Lin X, Song B. Zeng XJBiB: prediction of multi-relational drug–gene interaction via dynamic hypergraph contrastive learning. Brief Bioinform. 2023;24(6):371.
Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics. 2018;34(23):4007–16.
Article CAS PubMed PubMed Central Google Scholar
Derr T, Ma Y, Tang J: Signed graph convolutional networks. In: 2018 IEEE International Conference on Data Mining (ICDM): 2018. IEEE: 929–934.
Huang J, Shen H, Hou L, Cheng X. Signed graph attention networks. In: Artificial Neural Networks and Machine Learning–ICANN 2019: Workshop and Special Sessions: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings 28. Berlin, Heidelberg: Springer-Verlag; 2019. p. 566–77.
Li Y, Tian Y, Zhang J, Chang Y. Learning signed network embedding via graph attention. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34. 2020. p. 4772–9.
Li Y, Qu M, Tang J, Chang Y. Signed laplacian graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37. 2023. p. 4444–52.
Zeng X, Wang F, Luo Y, Kang S-G, Tang J, Lightstone FC, Fang EF, Cornell W, Nussinov R, Cheng F. Deep generative molecular design reshapes drug discovery. Cell Rep Med. 2022;4:100794.
Zhu H, Hao H, Yu L. Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance. BMC Biol. 2023;21(1):294.
Article PubMed PubMed Central Google Scholar
Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, Haibe-Kains B, Goldenberg A. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11(3):333–7.
Article CAS PubMed Google Scholar
Chen T, Guestrin C. Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, vol. 2016. 2016. p. 785–94.
Zulfiqar H, Guo Z, Ahmad RM, Ahmed Z, Cai P, Chen X, Zhang Y, Lin H, Shi Z. Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings. Front Med. 2024;10:1291352.
Zou X, Ren L, Cai P, Zhang Y, Ding H, Deng K, Yu X, Lin H, Huang C. Accurately identifying hemagglutinin using sequence information and machine learning methods. Front Med (Lausanne). 2023;10:1281880.
Kim M-J, Lee Y-C, Kim S-W. TrustSGCN: learning trustworthiness on edge signs for effective signed graph convolutional networks. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, vol. 2023. 2023. p. 2451–5.
Liu Y, Zheng Y, Zhang D, Lee VC, Pan S. Beyond smoothing: unsupervised graph representation learning with edge heterophily discriminating. In: Proceedings of the AAAI conference on artificial intelligence, vol. 37. 2023. p. 4516–44.
Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(11):2579.
Mancuso C, Santangelo R. Alzheimer’s disease and gut microbiota modifications: the long way between preclinical studies and clinical evidence. Pharmacol Res. 2018;129:329–36.
留言 (0)