REFERENCES
1. Coban, O.; De Deyn, G. B.; van der Ploeg, M. Soil microbiota as game-changers in restoration of degraded land. Science 2022, 375, abe0725.
2. Philippot, L.; Chenu, C.; Kappler, A.; Rillig, M. C.; Fierer, N. The interplay between microbial communities and soil properties. Nat. Rev. Microbiol. 2024, 22, 226-39.
3. Yang, X.; Cheng, J.; Franks, A. E.; et al. Loss of microbial diversity weakens specific soil functions, but increases soil ecosystem stability. Soil. Biol. Biochem. 2023, 177, 108916.
4. Liao, J.; Dou, Y.; Yang, X.; An, S. Soil microbial community and their functional genes during grassland restoration. J. Environ. Manage. 2023, 325, 116488.
5. Wang, Y.; Yan, X.; Su, M.; et al. Isolation of potassium solubilizing bacteria in soil and preparation of liquid bacteria fertilizer from food wastewater. Biochem. Eng. J. 2022, 181, 108378.
6. Javed, Z.; Tripathi, G. D.; Mishra, M.; Dashora, K. Actinomycetes – The microbial machinery for the organic-cycling, plant growth, and sustainable soil health. Biocatal. Agric. Biotechnol. 2021, 31, 101893.
7. Bai, B.; Liu, C.; Zhang, C.; et al. Trichoderma species from plant and soil: an excellent resource for biosynthesis of terpenoids with versatile bioactivities. J. Adv. Res. 2023, 49, 81-102.
8. Sokol, N. W.; Slessarev, E.; Marschmann, G. L.; et al. Life and death in the soil microbiome: how ecological processes influence biogeochemistry. Nat. Rev. Microbiol. 2022, 20, 415-30.
9. Yonatan, Y.; Amit, G.; Friedman, J.; Bashan, A. Complexity–stability trade-off in empirical microbial ecosystems. Nat. Ecol. Evol. 2022, 6, 693-700.
10. Chandra, B.; Gupta, M. Robust approach for estimating probabilities in Naïve–Bayes Classifier for gene expression data. Expert. Syst. Appl. 2011, 38, 1293-8.
11. Deng, H.; Runger, G. Gene selection with guided regularized random forest. Pattern. Recognit. 2013, 46, 3483-9.
12. Brady, A.; Salzberg, S. L. Phymm and phymmBL: metagenomic phylogenetic classification with interpolated markov models. Nat. Methods. 2009, 6, 673-6.
13. Wang, Q.; Garrity, G. M.; Tiedje, J. M.; Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 2007, 73, 5261-7.
14. Haque Mohammed, M.; Ghosh, T. S.; Singh, N. K.; Mande, S. S. SPHINX - an algorithm for taxonomic binning of metagenomic sequences. Bioinformatics 2011, 27, 22-30.
15. Díaz, D.; Esteban, F. J.; Hernández, P.; Caballero, J. A.; Dorado, G.; Gálvez, S. Parallelizing and optimizing a bioinformatics pairwise sequence alignment algorithm for many-core architecture. Parallel. Comput. 2011, 37, 244-59.
16. Xia, Z.; Cui, Y.; Zhang, A.; et al. A review of parallel implementations for the Smith–Waterman algorithm. Interdiscip. Sci. Comput. Life. Sci. 2022, 14, 1-14.
17. Camacho, C.; Coulouris, G.; Avagyan, V.; et al. BLAST+: architecture and applications. BMC. Bioinformatics. 2009, 10, 421.
18. Sievers, F.; Wilm, A.; Dineen, D.; et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 2011, 7, 539.
19. Le, N. Q. K.; Ho, Q. T.; Nguyen, T. T. D.; Ou, Y. Y. A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information. Brief. Bioinform. 2021, 22, bbab2005.
20. Hopf, T. A.; Ingraham, J. B.; Poelwijk, F. J.; et al. Mutation effects predicted from sequence co-variation. Nat. Biotechnol. 2017, 35, 128-35.
21. Wang, D.; Yang, S. X. Intelligent feature extraction, data fusion and detection of concrete bridge cracks: current development and challenges. Intell. Robot. 2022, 2, 391-406.
22. Zhao, S.; Qiu, S.; Xu, X.; Ciampitti, I. A.; Zhang, S.; He, P. Change in straw decomposition rate and soil microbial community composition after straw addition in different long-term fertilization soils. Appl. Soil. Ecol. 2019, 138, 123-33.
23. Ji, Y.; Zhou, Z.; Liu, H.; Davuluri, R. V. DNABERT: pre-trained bidirectional encoder representations from transformers model for DNA-language in genome. Bioinformatics 2021, 37, 2112-20.
24. Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; Bengio, Y. Graph attention networks. In International Conference on Learning Representations, 2018. https://openreview.net/forum?id=rJXMpikCZ. (accessed 18 Jun 2025).
25. Gao, W.; Cai, K.; Li, D.; et al. Soil taxonomy and suitability assessment on typical tobacco-planting farmlands in Guizhou, Southwest China. SN. Appl. Sci. 2019, 1, 877.
26. Fernández, A.; García, S.; Herrera, F.; Chawla, N. V. SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 2018, 61, 863-905.
27. Zhang, T.; Wu, Z.; Li, L.; et al. CellGAT: a GAT-based method for constructing a cell communication network integrating multiomics information. Biomolecules 2025, 15, 342.
28. Kong, X.; Xing, W.; Wei, X.; Bao, P.; Zhang, J.; Lu, W. STGAT: spatial-temporal graph attention networks for traffic flow forecasting. IEEE. Access. 2020, 8, 134363-72.
29. Sellers, T.; Lei, T.; Luo, C.; Jan, G. E.; Ma, J. A node selection algorithm to graph-based multi-waypoint optimization navigation and mapping. Intell. Robot. 2022, 2, 333-54.
30. Zhou, Z.; Ji, Y.; Li, W.; Dutta, P.; Davuluri, R.; Liu, H. DNABERT-2: efficient foundation model and benchmark for multi-species genome. arXiv2023, arXiv: 2306.15006. https://arxiv.org/abs/2306.15006. (accessed 18 Jun 2025).
