References

Complete bibliography for the DeepTaxa tutorials

Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology, 26(1), 32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. https://doi.org/10.1038/nmeth.3869

Douglas, G. M., Maffei, V. J., Zaneveld, J. R., Yurgel, S. N., Brown, J. R., Taylor, C. M., Huttenhower, C., & Langille, M. G. I. (2020). PICRUSt2 for prediction of metagenome functions. Nature Biotechnology, 38(6), 685–688. https://doi.org/10.1038/s41587-020-0548-6

Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning (ICML), 1321–1330. https://arxiv.org/abs/1706.04599

Hertzberg, V. S., Singh, H., Fournier, C. N., Moustafa, A., Polak, M., Kuelbs, C. A., Torralba, M. G., Tansey, M. G., Nelson, K. E., & Glass, J. D. (2021). Gut microbiome differences between amyotrophic lateral sclerosis patients and spouse controls. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 23(1-2), 91–99. https://doi.org/10.1080/21678421.2021.1904994

Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. IEEE International Conference on Computer Vision (ICCV), 2980–2988. https://doi.org/10.1109/ICCV.2017.324

Loshchilov, I., & Hutter, F. (2019). Decoupled weight decay regularization. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1711.05101

McDonald, D., Jiang, Y., Balaban, M., Cantrell, K., Zhu, Q., Gonzalez, A., Morton, J. T., Nicolaou, G., Parks, D. H., Karst, S. M., et al. (2024). Greengenes2 unifies microbial data in a single reference tree. Nature Biotechnology, 42, 715–718. https://doi.org/10.1038/s41587-023-01845-1

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41(D1), D590–D596. https://doi.org/10.1093/nar/gks1219

Salah, R., AbdElaal, K. R., Ghonaim, L., Awe, O. I., & Moustafa, A. (2026). DeepTaxa: A hybrid CNN-BERT framework for 16S rRNA taxonomic classification. Bioinformatics Advances, 6(1), vbag166. https://doi.org/10.1093/bioadv/vbag166

Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002

Zhou, Z., Ji, Y., Li, W., Dutta, P., Davuluri, R., & Liu, H. (2024). DNABERT-2: Efficient foundation model and benchmark for multi-species genome. https://arxiv.org/abs/2306.15006