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RESEARCH ARTICLE

An ensemble-based serial cascaded attention network and improved variational auto encoder for breast cancer prognosis prediction using data

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Pages 98-115 | Received 29 Aug 2023, Accepted 02 Nov 2023, Published online: 24 Nov 2023

References

  • Abraham G, Kowalczyk A, Loi S, Haviv I, Zobel J. 2010. Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context. BMC Bioinformatics. 11(1):277. doi: 10.1186/1471-2105-11-277.
  • Ahmad M, Ai D, Xie G, Qadri SF, Song H, Huang Y, Wang Y, Yang J. 2019. Deep belief network modeling for automatic liver segmentation. IEEE Access. 7:20585–20595. doi: 10.1109/ACCESS.2019.2896961.
  • Ahmad M, Qadri SF, Qadri S, Saeed IA, Zareen SS, Iqbal Z, Alabrah A, Alaghbari HM, Md. Mizanur Rahman S. 2022. A lightweight convolutional neural network model for liver segmentation in medical diagnosis. Comput Intell Neurosci. 2022:7954316–79543333. doi: 10.1155/2022/7954333.
  • Ahmad M, Yang J, Ai D, Furqan Qadri S, Wang Y. 2017. Deep-stacked auto encoder for liver segmentation. IGTA 2017: Advances in Image and Graphics Technologies. 757:243–251.
  • Arya N, Saha S. 2022a. Generative incomplete multi-view prognosis predictor for breast cancer: GIMPP. IEEE/ACM Trans Comput Biol Bioinform. 19(4):2252–2263. doi: 10.1109/TCBB.2021.3090458.
  • Arya N, Saha S. 2022b. Multi-modal classification for human breast cancer prognosis prediction: proposal of deep-learning based stacked ensemble model. IEEE/ACM Trans Comput Biol Bioinf. 19(2):1032–1041.
  • Boeri C, Chiappa C, Galli F, Berardinis VD, Bardelli L, Carcano G, Rovera F. 2020. Machine Learning techniques in breast cancer prognosis prediction: a primary evaluation. Cancer Med. 9(9):3234–3243. doi: 10.1002/cam4.2811.
  • Botlagunta M, Botlagunta MD, Myneni MB, Lakshmi D, Nayyar A, Gullapalli JS, Shah MA. 2023. Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms. Sci Rep. 13(1):485. doi: 10.1038/s41598-023-27548-w.
  • Cai W, Liu B, Wei Z, Li M, Kan J. 2021. TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification. Multimed Tools Appl. 80(7):11291–11312. doi: 10.1007/s11042-020-10188-x.
  • Cheng LH, Hsu TC, Lin C. 2021. Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction. Sci Rep. 11(1):14914. doi: 10.1038/s41598-021-92864-y.
  • Dewangan KK, Dewangan DK, Sahu SP, Janghel R. 2022. Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique. Multimed Tools Appl. 81(10):13935–13960. doi: 10.1007/s11042-022-12385-2.
  • Du X, Zhao Y. 2023. Multimodal adversarial representation learning for breast cancer prognosis prediction. Comput Biol Med. 157:106765. doi: 10.1016/j.compbiomed.2023.106765.
  • Furqan Qadri S, Ai D, Hu G, Ahmad M, Huang Y, Wang Y, Yang J. 2018. Automatic deep feature learning via patch-based deep belief network for vertebrae segmentation in CT images. Appl. Sci. 9(1):69. doi: 10.3390/app9010069.
  • Hirra I, Ahmad M, Hussain A, Ashraf MU, Saeed IA, Qadri SF, Alghamdi AM, Alfakeeh AS. 2021. Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access. 9:24273–24287. doi: 10.1109/ACCESS.2021.3056516.
  • Inan MSK, Hossain S, Uddin MN. 2023. Data augmentation guided breast cancer diagnosis and prognosis using an integrated deep-generative framework based on breast tumor’s morphological information. Inf Med Unlocked. 37:101171. doi: 10.1016/j.imu.2023.101171.
  • Li T, Chen S, Zhang Y, Zhao Q, Ma K, Jiang X, Xiang R, Zhai F, Ling G. 2023. Ensemble learning-based gene signature and risk model for predicting prognosis of triple-negative breast cancer. Funct Integr Genomics. 23(2):81. doi: 10.1007/s10142-023-01009-z.
  • Liu Y, Li A, Liu J, Meng G, Wang M. 2022. TSDLPP: a novel two-stage deep learning framework for prognosis prediction based on whole slide histopathological images. IEEE/ACM Trans Comput Biol Bioinform. 19(4):2523–2532. doi: 10.1109/TCBB.2021.3080295.
  • Maglogiannis I, Zafiropoulos E, Anagnostopoulos I. 2009. An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl Intell. 30(1):24–36. doi: 10.1007/s10489-007-0073-z.
  • Mallmann MR, Staratschek-Jox A, Rudlowski C, Braun M, Gaarz A, Wolfgarten M, Kuhn W, Schultze JL. 2010. Prediction and prognosis: impact of gene expression profiling in personalized treatment of breast cancer patients. Epma J. 1(3):421–437. doi: 10.1007/s13167-010-0044-z.
  • Mühlbauer V, Hoger BB, Albrecht M, Mühlhauser I, Steckelberg A. 2019. Communicating prognosis to women with early breast cancer: overview of prediction tools and the development and pilot testing of a decision aid. BMC Health Serv Res. 19(171)
  • Naseem U, Rashid J, Ali L, Kim J, Haq QEU, Awan MJ, Imran M. 2022. An automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers. IEEE Access. 10:78242–78252. doi: 10.1109/ACCESS.2022.3174599.
  • Nugroho H, Susanty M, Irawan A, Koyimatu M, Yunita A. 2020. Fully convolutional variational autoencoder for feature extraction of fire detection system. J Comput Sci Inf. 13(1):9–15.
  • Pang H, Wei S, Zhao Y, He L, Wang J, Liu B, Zhao Y. 2020. Effective attention-based network for syndrome differentiation of AIDS. BMC Med. Inf. Decis. Making. 20:264.
  • Poirion OB, Jing Z, Chaudhary K, Huang S, Garmire LX. 2021. DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome Med. 13(1):112. doi: 10.1186/s13073-021-00930-x.
  • Qadri SF, Lin H, Shen L, Ahmad M, Qadri S, Khan S, Khan M, Zareen SS, Akbar MA, Bin Heyat MB, et al. 2023. CT-based automatic spine segmentation using patch-based deep learning. Int J Intell Syst. 2023:1–14. doi: 10.1155/2023/2345835.
  • Qadri SF, Shen L, Ahmad M, Qadri S, Zareen SS, Akbar MA. 2022. SVseg: stacked sparse Autoencoder-based patch classification modeling for vertebrae segmentation. Mathematics. 10(5):796. doi: 10.3390/math10050796.
  • Qadri SF, Shen L, Ahmad M, Qadri S, Zareen SS, Khan S. 2021. OP-convNet: a patch classification-based framework for CT vertebrae segmentation. IEEE Access. 9:158227–158240. doi: 10.1109/ACCESS.2021.3131216.
  • Sacco K, Grech G. 2015. Actionable pharmacogenetic markers for prediction and prognosis in breast cancer. Epma J. 6(1):15. doi: 10.1186/s13167-015-0037-z.
  • Shi C, Pun CM. 2018. Multi-scale hierarchical recurrent neural networks for hyperspectral image classification. Neurocomputing. 294:82–93. doi: 10.1016/j.neucom.2018.03.012.
  • Stoddard FR, Szasz AM, Szekely B, Tokes AM, Kulka J. 2011. Molecular genetic tests in the prediction of the prognosis of breast cancer. Memo. 4(3):158–162. doi: 10.1007/s12254-011-0285-0.
  • Sun D, Wang M, Li A. 2019. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans Comput Biol Bioinf. 16(3):841–850. doi: 10.1109/TCBB.2018.2806438.
  • Teng J, Abdygametova A, Du J, Ma B, Zhou R, Shyr Y, Ye F. 2020. Bayesian inference of lymph node ratio estimation and survival prognosis for breast cancer patients. IEEE J Biomed Health Inform. 24(2):354–364. doi: 10.1109/JBHI.2019.2943401.
  • Xi G, Qiu L, Xu S, Guo W, Fu F, Kang D, Zheng L, He J, Zhang Q, Li L, et al. 2021. Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer. BMC Med. 19(1):273. doi: 10.1186/s12916-021-02146-7.
  • Xiao B, Hang J, Lei T, He Y, Kuang Z, Wang L, Chen L, He J, Zhang W, Liao Y, et al. 2019. Identification of key genes relevant to the prognosis of ER-positive and ER-negative breast cancer based on a prognostic prediction system. Mol Biol Rep. 46(2):2111–2119. doi: 10.1007/s11033-019-04663-4.
  • Yamaguchi A, Honda M, Ishiguro H, Kataoka M, Kataoka TR, Shimizu H, Torii M, Mori Y, Sakita NK, Ueno K, et al. 2021. Kinetic information from dynamic contrast-enhanced MRI enables prediction of residual cancer burden and prognosis in triple-negative breast cancer: a retrospective study. Sci Rep. 11(1):10112. doi: 10.1038/s41598-021-89380-4.
  • Zhang F, Zhang Y, Zhu X, Chen X, Du H, Zhang X. 2022. PregGAN: a prognosis prediction model for breast cancer based on conditional generative adversarial networks. Comput Method Program Biomed. 224:107026. doi: 10.1016/j.cmpb.2022.107026.
  • Zhou D, Wu Y, Jiang K, Xu F, Hong R, Wang S. 2021. Identification of a risk prediction model for clinical prognosis in HER2 positive breast cancer patients. Genomics. 113(6):4088–4097. doi: 10.1016/j.ygeno.2021.10.010.

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