Computational Molecular Biology 2025, Vol.15, No.4, 183-192 http://bioscipublisher.com/index.php/cmb 190 then again, there are quite a few problems. The model calculation is getting faster and faster, but its interpretability often fails to keep up. The volume of data is huge, but the results do not always translate into clear biological significance. What needs to be done in the future is probably not only to speed up the algorithm, but also to make the model's inference more "transparent" and form a virtuous cycle with experimental verification. At the same time, only by truly integrating spatial omics with fields such as immunology and pharmacology can new research ideas be opened up. Technology will eventually mature, but the process of understanding the complexity of living things is likely to take a little longer. Acknowledgments I would like to express my heartfelt thanks to all the teachers who have provided guidance for this study. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References An J., Lu Y., Chen Y., Chen Y., Zhou Z., Chen J., Peng C., Huang R., and Peng F., 2024, Spatial transcriptomics in breast cancer: providing insight into tumor heterogeneity and promoting individualized therapy, Frontiers in Immunology, 15: 1499301. https://doi.org/10.3389/fimmu.2024.1499301 Cao J., Chow L., and Dow S., 2023, Strategies to overcome myeloid cell induced immune suppression in the tumor microenvironment, Frontiers in Oncology, 13: 1116016. https://doi.org/10.3389/fonc.2023.1116016 Chew J., Uytingco C., Spalinskas R., Yin Y., Shuga J., Veire B., Anaparthy N., Hatori R., Katsor A., Katiraee L., Hermes A., Chiang J., Roelli P., Williams S., Nitsch W., Weisenfeld N., Walkser D., Koth J., Basu S., Howat W., Ganapathy K., and Stoeckius M., 2021, 83 spatially resolved transcriptomic and proteomic investigation of breast cancer and its immune microenvironment, Journal for Immuno Therapy of Cancer, 9(Suppl 2): A91-A91. https://doi.org/10.1136/jitc-2021-sitc2021.083 Chitra U., Arnold B., Sarkar H., C., Lopez-Darwin S., Sanno K., and Raphael B., 2025, Mapping the topography of spatial gene expression with interpretable deep learning, Nature Methods, 22(2): 298-309. https://doi.org/10.1101/2023.10.10.561757 Chowdhury S., Ferri-Borgogno S., Calinawan A., Yang P., Wang W., Peng J., Mok S., and Wang P., 2021, Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic analysis of ovarian cancer, bioRxiv, 03: 454931. https://doi.org/10.1101/2021.08.03.454931 Di Mauro F., and Arbore G., 2024, Spatial dissection of the immune landscape of solid tumors to advance precision medicine, Cancer Immunology Research, 12(7): 800-813. https://doi.org/10.1158/2326-6066.CIR-23-0699 Dong K., and Zhang S., 2021, Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder, Nature Communications, 13(1): 1739. https://doi.org/10.1038/s41467-022-29439-6 Du J., An Z., Huang Z., Yang Y., Zhang M., Fu X., Shi W., and Hou J., 2023, Novel insights from spatial transcriptome analysis in solid tumors, International Journal of Biological Sciences, 19(15): 4778-4792. https://doi.org/10.7150/ijbs.83098 Hamel S., Cheung E., Qu Y., Loviska M., Mayer A., Zhang L., Lu T., Sundaram V., Zhang B., and Trevino A., 2023, Abstract LB079: an end-to-end Visium spatial transcriptomics computational pipeline for generating low-code interactive reports of spatial insights, Cancer Research, 83(8_Supplement): LB079. https://doi.org/10.1158/1538-7445.AM2023-LB079 He X., Guan X., and Li Y., 2025, Clinical significance of the tumor microenvironment on immune tolerance in gastric cancer, Frontiers in Immunology, 16: 1532605. https://doi.org/10.3389/fimmu.2025.1532605 Hu J., Li X., Coleman K., Schroeder A., N., Irwin D., Lee E., Shinohara R., and Li M., 2021, SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network, Nature Methods, 18(11): 1342-1351. https://doi.org/10.1038/s41592-021-01255-8 Hu Y., Xiao K., Yang H., Liu X., Zhang C., and Shi Q., 2024, Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics, Briefings in Bioinformatics, 25(2): bbae016. https://doi.org/10.1093/bib/bbae016 Huang S., Ouyang L., Tang J., Qian K., Chen X., Xu Z., Ming J., and Xi R., 2024, Spatial transcriptomics: a new frontier in cancer research, Clinical Cancer Bulletin, 3(1): 13. https://doi.org/10.1007/s44272-024-00018-8
RkJQdWJsaXNoZXIy MjQ4ODYzNA==