Hybrid tuned deep learning model for breast cancer diagnosis using genetic data – Scientific Reports

1. Mihai, A. Breast cancer screening in romania: Challenges and opportunities for early detection. Acta Endocrinol. (Bucharest) 20(1), 45–50. https://doi.org/10.4183/aeb.2024.45 (2024).

   Google Scholar 

2. AL Zomia, A. S. et al. Tracking the epidemiological trends of female breast cancer in saudi arabia since 1990 and forecasting future statistics using global burden of disease data. BMC Public Health https://doi.org/10.1186/s12889-024-19377-x (2024).

   Google Scholar 

3. Uematsu, T. Rethinking screening mammography in japan: Next-generation breast cancer screening through breast awareness and supplemental ultrasonography. Breast Cancer 31(1), 24–30. https://doi.org/10.1007/s12282-023-01506-w (2023).

   Google Scholar 

4. García-Sancha, N., Corchado-Cobos, R. & Pérez-Losada, J. Understanding susceptibility to breast cancer: From risk factors to prevention strategies. Int. J. Mol. Sci. 26(7), 2993. https://doi.org/10.3390/ijms26072993 (2025).

   Google Scholar 

5. Pal, M., Das, D. & Pandey, M. Understanding genetic variations associated with familial breast cancer. World J. Surg. Oncol. https://doi.org/10.1186/s12957-024-03553-9 (2024).

   Google Scholar 

6. Dyachenko, E. I. & Bel’skaya, L. V. How does breast cancer heterogeneity determine changes in tumor marker levels in saliva?. Curr. Issues Mol. Biol. 47(4), 216. https://doi.org/10.3390/cimb47040216 (2025).

   Google Scholar 

7. Aftimos, P. et al. Genomic and transcriptomic analyses of breast cancer primaries and matched metastases in aurora, the big molecular screening initiative. Cancer Discov. 11(11), 2796–2811. https://doi.org/10.1158/2159-8290.cd-20-1647 (2021).

   Google Scholar 

8. Person, M. D. Arif, & Ali, R. d. C. Phytonutrients: Clinical implications in breast cancer prevention. Taylor and Francis https://doi.org/10.1201/9781003128779-26 (2025).

9. Abiodun, A. G. Deep learning techniques for subtype classification and prognosis in breast cancer genomics: A systematic review and meta-analysis. Int. J. Adv. Res. Comput. Sci. 15(5), 74–83. https://doi.org/10.26483/ijarcs.v15i5.7127 (2024).

   Google Scholar 

10. Quazi, S. Retracted article: Artificial intelligence and machine learning in precision and genomic medicine. Med. Oncol. https://doi.org/10.1007/s12032-022-01711-1 (2022).

    Google Scholar 

11. Senan, E. M. et al. Classification of histopathological images for early detection of breast cancer using deep learning. J. Appl. Sci. Eng. 24(3), 323–329 (2021).

    Google Scholar 

12. Al-Jabbar, M., Alshahrani, M., Senan, E. M. & Ahmed, I. A. Multi-method diagnosis of histopathological images for early detection of breast cancer based on hybrid and deep learning. Mathematics 11(6), 1429 (2023).

    Google Scholar 

13. Al-Jabbar, M., Alshahrani, M., Senan, E. M. & Ahmed, I. A. Analyzing histological images using hybrid techniques for early detection of multi-class breast cancer based on fusion features of cnn and handcrafted. Diagnostics 13(10), 1753 (2023).

    Google Scholar 

14. Zhu, Z., Lu, S.-Y., Huang, T., Liu, L. & Liu, Z. Lka: L arge k ernel a dapter for enhanced medical image classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 394–404 (Springer, 2025).

15. Zhu, Z., Liu, L., Free, R. C., Anjum, A. & Panneerselvam, J. Opt-co: Optimizing pre-trained transformer models for efficient covid-19 classification with stochastic configuration networks. Inf. Sci. 680, 121141. https://doi.org/10.1016/j.ins.2024.121141 (2024).

    Google Scholar 

16. Zhu, H., Zhu, Z. & Lu, S.-Y. Bcct: an efficient transformer model for blood cell classification. Multimed. Syst. 32(1), 29 (2026).

    Google Scholar 

17. Lu, S.-Y., Zhu, Z., Zhang, Y.-D. & Yao, Y.-D. Tuberculosis and pneumonia diagnosis in chest x-rays by large adaptive filter and aligning normalized network with report-guided multi-level alignment. Eng. Appl. Artif. Intell. 158, 111575. https://doi.org/10.1016/j.engappai.2025.111575 (2025).

    Google Scholar 

18. Adam, N. & Wieder, R. Temporal association rule mining: Race-based patterns of treatment-adverse events in breast cancer patients using seer–medicare dataset. Biomedicines 12(6), 1213. https://doi.org/10.3390/biomedicines12061213 (2024).

    Google Scholar 

19. Tirumanadham, N. S. et al. Optimizing lung cancer prediction models: A hybrid methodology using gwo and random forest. Stud. Comput. Intell. https://doi.org/10.1007/978-3-031-82516-3_3 (2025).

    Google Scholar 

20. Aljehani, S. & Alotaibi, Y. Preserving privacy in association rule mining using multi-threshold particle swarm optimization. Inf. Sci. 692, 121673. https://doi.org/10.1016/j.ins.2024.121673 (2025).

    Google Scholar 

21. Thahiem, S. et al. Elucidation of potential mirnas as prognostic biomarkers for coronary artery disease. Human Gene 43, 201385. https://doi.org/10.1016/j.humgen.2025.201385 (2025).

    Google Scholar 

22. Shahane, S. Gene Expression Profiles of Breast Cancer. Kaggle. https://www.kaggle.com/datasets/saurabhshahane/gene-expression-profiles-of-breast-cancerAccessed: 2026–01-05 (2021).

23. Evitan, G. Breast Cancer METABRIC Dataset. Kaggle. https://www.kaggle.com/datasets/gunesevitan/breast-cancer-metabric (2020). Accessed: 2026–01-05.

24. Alzahrani, A., Raza, M. A. & Asghar, M. Z. Demystifying diagnosis: An efficient deep learning technique with explainable ai to improve breast cancer detection. PeerJ Comput. Sci. https://doi.org/10.7717/peerj-cs.2806 (2025).

    Google Scholar 

25. Alavilli, S. K., Nippatla, R. P., Kadiyala, B., Boyapati, S. & Vasamsetty, C. Unified robotic automation and ai-driven transformer-guided graph neural network with hybrid 3d-cnn, bilstm, and fuzzy decision framework for breast cancer prediction. Int. J. Autom. Smart Technol. https://doi.org/10.5875/j8f74e88 (2025).

    Google Scholar 

26. Raza, M. A., Khattak, A. M., Abbas, W. & Asghar, M. Z. Efficient diagnoses of breast cancer disease using deep learning technique. Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence, 136–143 https://doi.org/10.1145/3669754.3669775 (2024).

27. Rout, N. K., Dora, L. & Agrawal, S. Bayesian optimized artificial neural network for breast cancer classification. International Conference on Inventive Computation Technologies (ICICT), 684–691, https://doi.org/10.1109/icict60155.2024.10544809, (2024).

28. Soares, R. C. et al. Integration of bayesian optimization into hyperparameter tuning to enhance neural networks in bearing failure classification. Measurement 242, 115829. https://doi.org/10.1016/j.measurement.2024.115829 (2025).

    Google Scholar 

29. Raaj, R. S. Breast cancer detection and diagnosis using hybrid deep learning architecture. Biomed. Signal Process. Control 82, 104558. https://doi.org/10.1016/j.bspc.2022.104558 (2023).

    Google Scholar 

30. Asiri, Y. et al. Analyzing histopathological images using fused cnn features based on the geometric active contour method for early diagnosis of lung and colon cancer. Discov. Oncol. 16(1), 2036 (2025).

    Google Scholar 

31. Al-Jabbar, M., Alshahrani, M., Senan, E. M. & Ahmed, I. A. Histopathological analysis for detecting lung and colon cancer malignancies using hybrid systems with fused features. Bioengineering 10(3), 383 (2023).

    Google Scholar 

32. Ahmed, I. A., Senan, E. M., Shatnawi, H. S. A., Alkhraisha, Z. M. & Al-Azzam, M. M. A. Multi-models of analyzing dermoscopy images for early detection of multi-class skin lesions based on fused features. Processes 11(3), 910 (2023).

    Google Scholar 

33. Hamdi, M. et al. Analysis of wsi images by hybrid systems with fusion features for early diagnosis of cervical cancer. Diagnostics 13(15), 2538 (2023).

    Google Scholar 

34. Ahmed, I. A., Senan, E. M. & Shatnawi, H. S. A. Analysis of histopathological images for early diagnosis of oral squamous cell carcinoma by hybrid systems based on cnn fusion features. Int. J. Intell. Syst. 2023(1), 2662719 (2023).

    Google Scholar 

35. Hamdi, M. et al. Hybrid models based on fusion features of a cnn and handcrafted features for accurate histopathological image analysis for diagnosing malignant lymphomas. Diagnostics 13(13), 2258 (2023).

    Google Scholar 

36. Chhillar, I. & Singh, A. An improved soft voting-based machine learning technique to detect breast cancer utilizing effective feature selection and smote-enn class balancing. Discov. Artif. Intell. https://doi.org/10.1007/s44163-025-00224-w (2025).

    Google Scholar 

37. Fatima, A., Shabbir, A., Janjua, J. I., Ramay, S. A., Bhatty, R. A., Irfan, M., & Abbas, T. (n.d.). Analyzing breast cancer detection using machine learning & deep learning techniques. Journal of Computing & Biomedical Informatics. https://jcbi.org/index.php/Main/article/view/542

38. Das, A. K., Biswas, S. K., Mandal, A., Bhattacharya, A. & Sanyal, S. Machine learning based intelligent system for breast cancer prediction (mlisbcp). Expert Syst. Appl. 242, 122673. https://doi.org/10.1016/j.eswa.2023.122673 (2024).

    Google Scholar 

39. Naz, A., Khan, H., Ud Din, I., Ali, A. & Husain, M. An efficient optimization system for early breast cancer diagnosis based on internet of medical things and deep learning. Eng. Technol. Appl. Sci. Res. 14(4), 15957–15962. https://doi.org/10.48084/etasr.8080 (2024).

    Google Scholar 

40. Ahmad, J. et al. Deep learning empowered breast cancer diagnosis: Advancements in detection and classification. PLOS ONE https://doi.org/10.1371/journal.pone.0304757 (2024).

    Google Scholar 

41. Ahmad, S. et al. Deep learning-based computational approach for predicting ncrnas-disease associations in metaplastic breast cancer diagnosis. BMC Cancer https://doi.org/10.1186/s12885-025-14113-z (2025).

    Google Scholar 

42. Yaqoob, A., Verma, N. K., Aziz, R. M. & Shah, M. A. Rna-seq analysis for breast cancer detection: A study on paired tissue samples using hybrid optimization and deep learning techniques. J. Cancer Res. Clin. Oncol. https://doi.org/10.1007/s00432-024-05968-z (2024).

    Google Scholar 

43. Kumari, D., Naidu, M. V. S. S., Panda, S. & Christopher, J. Predicting breast cancer recurrence using deep learning. Discov. Appl. Sci. https://doi.org/10.1007/s42452-025-06512-5 (2025).

    Google Scholar 

44. Zeedhan, M., Mohamed Ziham, M. M., Abdul Razick, M. S. & Amin, N. U. Predicting obesity classification using k-nearest neighbors: A data science approach in python. Preprints. https://doi.org/10.20944/preprints202504.1032.v1, (2025).

45. Wang, A. X., Le, V.-T., Trung, H. N. & Nguyen, B. P. Addressing imbalance in health data: Synthetic minority oversampling using deep learning. Comput. Biol. Med. 188, 109830. https://doi.org/10.1016/j.compbiomed.2025.109830 (2025).

    Google Scholar 

46. Liu, X., Shen, F., Zhao, J. & Nie, C. Randomix: a mixed sample data augmentation method with multiple mixed modes. Multimed. Tools Appl. 84(8), 4343–4359. https://doi.org/10.1007/s11042-024-18868-8 (2024).

    Google Scholar 

47. Singh, R. & Jain, G. Forestrank: Automatic keyphrase extraction leveraging random forest classifier and multi-criteria decision-making. In: 2025 5th Asia Conference on Information Engineering (ACIE), pp. 62–67. https://doi.org/10.1109/acie64499.2025.00017 (2025).

48. Wang, K., He, Q., Jiang, X., Ma, Y., Wang, T., Zhou, H., Yu, Z., & Jiao, Z. (2025). Biological functions and therapeutic potential of UBE2T in human cancer. Current Cancer Drug Targets, 25. https://doi.org/10.2174/0115680096370867250211070948

49. Chen, K., Lei, H., Liu, X., & Wang, S. (2024). The roles of e2f7 in cancer: Current knowledge and future prospects. Heliyon, 10(14). https://doi.org/10.1016/j.heliyon.2024.e34362

50. Saleh, R. O. et al. lncrna-microrna axis in cancer drug resistance: particular focus on signaling pathways. Med. Oncol. 41(2), 52 (2024).

    Google Scholar 

51. Su, X. et al. Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning. Nat. Biomed. Eng. 9(3), 371–389 (2025).

    Google Scholar 

52. Gao, C. et al. Unraveling the role of ubiquitin-conjugating enzyme ube2t in tumorigenesis: A comprehensive review. Cells 14(1), 15 (2024).

    Google Scholar 

53. Asthana, A. Gene regulation and chromatin interactions by the dream and e2f transcription factors. PhD thesis, University of California, Santa Cruz (2022).

54. Lin, P., Zhao, Y., Li, X. & Liang, Z. Kiaa0101 in malignant pleural mesothelioma: a potential diagnostic and prognostic marker. Comb. Chem. High Throughput Screen. 25(9), 1498–1506 (2022).

    Google Scholar 

55. Mustafa, M. et al. Molecular pathways and therapeutic targets linked to triple-negative breast cancer (tnbc). Mol. Cell. Biochem. 479(4), 895–913 (2024).

56. Wu, Y. et al. Identification of the alpha1 chain of type x collagen (col10a1) as novel biomarker for the prognosis of different-grade conventional chondrosarcoma. Available at SSRN 4051466.

57. Zhou, W., Li, Y., Gu, D., Xu, J., Wang, R., Wang, H., & Liu, C. (2022). High expression COL10A1 promotes breast cancer progression and predicts poor prognosis. Heliyon, 8(10). https://doi.org/10.1016/j.heliyon.2022.e11083

58. Cheng, D. et al. Targeted delivery of znf416 sirna-loaded liposomes attenuates experimental pulmonary fibrosis. J. Transl. Med. 20(1), 523 (2022).

    Google Scholar 

59. Tzvetkova, M. Deciphering zinc finger proteins’ role in breast cancer progression: Homology-based analysis of etiology and prognosis. The National High School Journal of Science (2023).

60. Vega-Benedetti, A. F. et al. Clustered protocadherins methylation alterations in cancer. Clin. Epigenetics 11(1), 100 (2019).

    Google Scholar 

61. Zheng, Z. et al. The roles of protocadherin-7 in colorectal cancer cells on cell proliferation and its chemoresistance. Front. Pharmacol. 14, 1072033 (2023).

    Google Scholar 

62. Chagraoui, A., Thibaut, F. & De Deurwaerdère, P. 5-ht6 receptors: Contemporary views on their neurobiological and pharmacological relevance in neuropsychiatric disorders. Dialogues Clin. Neurosci. 27(1), 112–128 (2025).

    Google Scholar 

63. Li, M. et al. Gprin1 promotes gallbladder cancer progression through the cdk1 pathway (2022).

64. Rao, Y. et al. Pyrimidine synthesis enzyme ctp synthetase 1 suppresses antiviral interferon induction by deamidating irf3. Immunity 58(1), 74–89 (2025).

    Google Scholar 

65. Sun, Z., Zhang, Z., Wang, Q.-Q. & Liu, J.-L. Combined inactivation of ctps1 and atr is synthetically lethal to myc-overexpressing cancer cells. Cancer Res. 82(6), 1013–1024 (2022).

    Google Scholar 

66. Shahid, M. et al. Centromere protein f (cenpf), a microtubule binding protein, modulates cancer metabolism by regulating pyruvate kinase m2 phosphorylation signaling. Cell Cycle 17(24), 2802–2818 (2018).

    Google Scholar 

67. Li, J. et al. Knockdown of cenpf induces cell cycle arrest and inhibits epithelial-mesenchymal transition progression in glioma. Oncol. Lett. 29(1), 61 (2024).

    Google Scholar 

68. Wang, S. et al. Far upstream element-binding protein 1 (fubp1) participates in the malignant process and glycolysis of colon cancer cells by combining with c-myc. Bioengineered 13(5), 12115–12126 (2022).

    Google Scholar 

69. Lee, J. E. A., Parsons, L. M. & Quinn, L. M. Myc function and regulation in flies: how drosophila has enlightened myc cancer biology. AIMS Genetics 1(01), 081–098 (2014).

    Google Scholar 

70. Lin, Y. et al. Ctps1 promotes malignant progression of triple-negative breast cancer with transcriptional activation by ybx1. J. Transl. Med. 20(1), 17. https://doi.org/10.1186/s12967-021-03206-5 (2022).

    Google Scholar 

71. Famili-Youth, E. H. et al. Aberrant expression of collagen type x in solid tumor stroma is associated with emt, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis. BMC cancer 25(1), 247 (2025).

    Google Scholar 

72. Alvizo-Rodríguez, C., Carrasco-Carballo, A., López-Vázquez, U., Hernández-Montes, G. & Hernández-Caballero, M. E. Transcriptome analysis of triple-negative hcc1937 and mda-mb-231 breast cancer cells treated with kalanchoe pinnata revealed the regulation of migration and invasion via the downregulation of the genes jak2, rock1 and rock2. ACS omega 10(28), 31187–31200 (2025).

    Google Scholar 

73. Chen, J. et al. Deciphering the prognostic and therapeutic significance of cell cycle regulator cenpf: A potential biomarker of prognosis and immune microenvironment for patients with liposarcoma. Int. J. Mol. Sci. 24(8), 7010 (2023).

    Google Scholar 

74. Feng, H., Ju, Y., Yin, X., Qiu, W. & Zhang, X. Stlbrf: an improved random forest algorithm based on standardized-threshold for feature screening of gene expression data. Brief. Funct. Genom. 24, 048 (2025).

    Google Scholar 

75. Linares-Barrera, M. L., Martínez-Ballesteros, M., García-Heredia, J. M. & Riquelme, J. C. A feature selection and association rule approach to identify genes associated with metastasis and low survival in sarcoma. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 731–742 (Springer, 2023).

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