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An Efficient Framework for Predicting Cancer Type Based on Microarray Gene Expressions Using CNN-BiLSTM Technique

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Abstract

In recent years, a significant number of deaths worldwide have been due to cancer. Analysis of microarray gene expression data facilitates early cancer identification. The accurate discovery of information for thousands of genes is made possible by DNA microarray technology. In most biological study fields, gene expression analysis is crucial for obtaining the required information. It can be quite difficult to extract useful information from large datasets. Various optimizer outcomes are evaluated in this research on the RNA sequences dataset. The effectiveness of various optimizers which includes adaptive gradient optimization (AdaGrad), adaptive momentum, and stochastic gradient descent (SGD). AdaGrad and Adam are good. This paper presents an efficient framework for predicting cancer type based on the microarray gene expressions using the hybrid CNN + BiLSTM approach which can identify different forms of cancers. The Experimental Results got from the proposed CNN + BiLSTM outperforms the existing CNN and LSTM classifiers with several performance parameters like Classification Accuracy, Precision, Recall and F-measure in terms of 98.3%, 98.1%, 97.8% and 97.94%, respectively.

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Data availability

The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Fotouhi S, Asadi S, Kattan MW. A comprehensive data level analysis for cancer diagnosis on imbalanced data. J Biomed Inf. 2019;90: 103089. https://doi.org/10.1016/j.jbi.2018.12.003.

    Article  Google Scholar 

  2. Id JL, Zhou Z, Dong J, Fu Y, Li Y, Luan Z, et al. Predicting breast cancer 5-year survival using machine learning: a systematic review. PLoS ONE. 2021;16:e0250370-23. https://doi.org/10.1371/journal.pone.0250370.

    Article  Google Scholar 

  3. Kashyap D, Pal D, Sharma R, Garg VK, Goel N, Koundal D, et al. Global increase in breast cancer incidence: risk factors and preventive measures. Biomed Res Int. 2022;2022:9605439. https://doi.org/10.1155/2022/9605439.

    Article  Google Scholar 

  4. Schiff M, Castle PE, Jeronimo J, Rodriguez AC, Wacholder S. Human papillomavirus and cervical cancer. Clin Microbiol Rev. 2007;16:1–17. https://doi.org/10.1128/CMR.16.1.1-17.2003.

    Article  Google Scholar 

  5. Motieghader H, Najafi A, Sadeghi B, Masoudi-nejad A. A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. Inf Med Unlocked. 2017;9:246–54. https://doi.org/10.1016/j.imu.2017.10.004.

    Article  Google Scholar 

  6. Panda M. Elephant search optimization combined with deep neural network for microarray data analysis. J King Saud Univ Comput Inf Sci. 2017;32:940–8. https://doi.org/10.1016/j.jksuci.2017.12.002.

    Article  Google Scholar 

  7. Dargan S, Kumar M, Rohit M, Gulshan A. A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng. 2020;27(4):1071–92. https://doi.org/10.1007/s11831-019-09344-w.

    Article  MathSciNet  Google Scholar 

  8. De Guia JM. DeepGx: deep learning using gene expression for cancer classification. In: Proceeding of the IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), Vancouver BC Canada, 27–30 August 2019. IEEE; 2019. p. 913–20. https://doi.org/10.1145/3341161.3343516.

  9. Kim B, Yu K, Lee PCW. Cancer classification of single-cell gene expression data by neural network. Bioinformatics. 2020;36:1360–6. https://doi.org/10.1093/bioinformatics/btz772.

    Article  Google Scholar 

  10. Kong Y, Yu T. A deep neural network model using random forest to extract feature representation for gene expression data classification. Sci Rep. 2018;8(1):16477. https://doi.org/10.1038/s41598-018-34833-6.

    Article  MathSciNet  Google Scholar 

  11. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.

    Article  Google Scholar 

  12. Gupta S, Gupta MK, Shabaz M, Sharma A. Deep learning techniques for cancer classification using microarray gene expression data. Front Physiol. 2022;13: 952709. https://doi.org/10.3389/fphys.2022.952709.

    Article  Google Scholar 

  13. Abdollahi J, Nouri-Moghaddam B, Ghazanfari M. Deep neural network based ensemble learning algorithms for the healthcare system diagnosis of chronic diseases. ArXiv Preprint. 2021. https://arxiv.org/abs.2103.08182.

  14. Reid A, Klerk ND, Musk AWB. Does exposure to asbestos cause ovarian cancer? A systematic literature review and meta-analysis. Cancer Epidemiol Biomark Prev. 2011;20:1287–95. https://doi.org/10.1158/1055-9965.EPI-10-1302.

    Article  Google Scholar 

  15. Ahn T, Lee C. Deep learning-based identification of cancer or normal tissue using gene expression data. In: Proceeding of the IEEE international conference on bioinformatics and biomedicine (BIBM). Madrid, Spain, 03–06 December 2018. IEEE; 2018. p. 1748–52. https://doi.org/10.1109/BIBM.2018.8621108.

  16. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30(4):449–59.

    Article  Google Scholar 

  17. Alomari OA, Khader AT, Al-Betar MA, Awadallah MA. A novel gene selection method using modified MRMR and hybrid bat-inspired algorithm with β-hill climbing. Appl Intell (Dordr). 2018;48(11):4429–47. https://doi.org/10.1007/s10489-018-1207-1.

    Article  Google Scholar 

  18. Danaee P, Ghaeini R, Hendrix DA. A deep learning approach for cancer detection and relevant gene identification. Pac Symp Biocomput. 2017;22:219–29. https://doi.org/10.1142/9789813207813_0022.

    Article  Google Scholar 

  19. Daoud M, Mayo M. A survey of neural network-based cancer prediction models from microarray data. Artif Intell Med. 2019;97:204–14. https://doi.org/10.1016/j.artmed.2019.01.006.

    Article  Google Scholar 

  20. Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539.

    Article  Google Scholar 

  21. Guo Y, Liu S, Li Z, Shang X. BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data. BMC Bioinform. 2018;19(5):118–213. https://doi.org/10.1186/s12859-018-2095-4.

    Article  Google Scholar 

  22. Gupta G, Manoj G. Deep learning for brain tumor segmentation using magnetic resonance images. In: Proceeding of the IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB), Melbourne, Australia, 13–15 October 2021. IEEE; 2021. p. 1–6. https://doi.org/10.1109/CIBCB49929.2021.9562890.

  23. Joshi P, Park T. Cancer subtype classification based on super layered neural network. In: Proceeding of the IEEE international conference on bioinformatics and biomedicine, San Diego, CA, USA, 18–21 November 2019. IEEE; 2019. p. 1988–92. https://doi.org/10.1109/BIBM47256.2019.8983343.

  24. Aziz R, Verma CK, Srivastava N. A novel approach for dimension reduction of microarray. Comput Biol Chem. 2017;71:161–9. https://doi.org/10.1016/j.compbiolchem.2017.10.009.

    Article  Google Scholar 

  25. Chaunzwa TL, Hosny A, Xu Y, Shafer A, Diao N, Lanuti M, et al. Deep learning classification of lung cancer histology using CT images. Sci Rep. 2021;1:5471. https://doi.org/10.1038/s41598-021-84630-x.

    Article  Google Scholar 

  26. Huynh P, Nguyen V, Do T. Novel hybrid DCNN–SVM model for classifying RNA-sequencing gene expression data. J Inf Telecommun. 2019;3:533–47. https://doi.org/10.1080/24751839.2019.1660845.

    Article  Google Scholar 

  27. Jerez M, Franco L, Veredas FJ, Lo G. Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data. PLoS ONE. 2020;15:e0230536-24. https://doi.org/10.1371/journal.pone.0230536.

    Article  Google Scholar 

  28. Shon HS, Yi Y, Kim KO, Cha E, Kim K. Classification of stomach cancer gene expression data using CNN algorithm of deep learning. J Biomed Transl Res. 2021;20(1):15–20. https://doi.org/10.12729/jbtr.2019.20.1.015.

    Article  Google Scholar 

  29. Basavegowda HS, Dagnew G. Deep learning approach for microarray cancer data classification. CAAI Trans Intell Technol. 2020;5:22–33. https://doi.org/10.1049/trit.2019.0028.

    Article  Google Scholar 

  30. Chen X, Xie J, Yuan Q. A method to facilitate cancer detection and type classification from gene expression data using a deep autoencoder and neural network. Mach Learn. 2018. https://arxiv.org/abs1812.08674.

  31. Ching T, Zhu X, Garmire LX. Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput Biol. 2018;14:e1006076-18. https://doi.org/10.1371/journal.pcbi.1006076.

    Article  Google Scholar 

  32. Dwivedi AK. Artificial neural network model for effective cancer classification using microarray gene expression data. Neural comput Appl. 2016;29:1545–54. https://doi.org/10.1007/s00521-016-2701-1.

    Article  Google Scholar 

  33. Salman I, Ucan O, Bayat O, Shaker K. Impact of metaheuristic iteration on artificial neural network structure in medical data. Processes. 2018;6:57. https://doi.org/10.3390/pr6050057.

    Article  Google Scholar 

  34. Cho H, Lee S, Ji YG, Hyeon D, Id L. Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma. PLoS ONE. 2018;13: e0207204. https://doi.org/10.1371/journal.pone.0207204.

    Article  Google Scholar 

  35. Ronoud S, Asadi S. An evolutionary deep belief network extreme learning-based for breast cancer diagnosis. Soft Comput. 2019;23:13139–59. https://doi.org/10.1007/s00500-019-03856-0.

    Article  Google Scholar 

  36. Gupta S, Gupta MK. Computational model for prediction of malignant mesothelioma diagnosis. Comput J. 2021. https://doi.org/10.1093/comjnl/bxab146.

    Article  Google Scholar 

  37. Gupta S, Gupta MK. A comprehensive data-level investigation of cancer diagnosis on imbalanced data. Comput Intell. 2021;38:156–86. https://doi.org/10.1111/coin.12452.

    Article  MathSciNet  Google Scholar 

  38. Extraction SF. Prognosis prediction of human breast cancer by integrating deep neural network and support vector machine supervised feature extraction and classification for breast cancer prognosis prediction. In: Proceeding international congress on image and signal processing, BioMedical engineering and informatics (CISP-BMEI), Shanghai China, 14–16 October 2017. IEEE; 2017. https://doi.org/10.1109/CISP-BMEI.2017.8301908.

  39. Gao F, Wang W, Tan M, Zhu L, Zhang Y, Fessler E, et al. DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis. 2019;8:44. https://doi.org/10.1038/s41389-019-0157-8.

    Article  Google Scholar 

  40. García-díaz P, Sánchez-berriel I, Martínez- JA, Diez-pascual AM. Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data. Genomics. 2019;112:1196. https://doi.org/10.1016/j.ygeno.2019.11.004.

    Article  Google Scholar 

  41. Lin M, Jaitly V, Wang I, Hu Z, Chen L, Wahed M, et al. Application of deep learning on predicting prognosis of acute myeloid leukemia with cytogenetics age and mutations. Mach Learn. 2018. https://arxiv.org/abs1810.13247.

  42. Parvathavardhini S, Manju S. Cancer gene detection using Neuro fuzzy classification algorithm. Int J Sci Res Comput Sci Eng Inf Technol. 2020;3(3):2456.

    Google Scholar 

  43. Sevakula RK, Singh V, Member S, Kumar C, Cui Y. Transfer learning for molecular cancer classification using deep neural networks. IEEE/ACM Trans Comput Biol Bioinform. 2018;5963:2089–100. https://doi.org/10.1109/TCBB.2018.2822803.

    Article  Google Scholar 

  44. Gupta S, Gupta M. Deep learning for brain tumor segmentation using magnetic resonance images. In: Proceeding of the IEEE conference on computational intelligence in bioinformatics and computational biology, Melbourne, Australia, 13–15 October 2021. IEEE; 2021. https://doi.org/10.1109/CIBCB49929.2021.9562890.

  45. Huang Z, Johnson TS, Han Z, Helm B, Cao S, Zhang C, et al. Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations. BMC Med Genom. 2020;13(5):1–12. https://doi.org/10.1186/s12920-020-0686-1.

    Article  Google Scholar 

  46. Gupta S, Gupta MK. A comparative analysis of deep learning approaches for predicting breast cancer survivability. Arch Comput Methods Eng. 2021;29:2959–75.

    Article  MathSciNet  Google Scholar 

  47. Kumar Y, Gupta S, Singla R, Chen Y. A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Arch Comput Methods Eng. 2021;29(4):2043–70. https://doi.org/10.1007/s11831-021-09648-w.

    Article  Google Scholar 

  48. Gupta S. Computational prediction of cervical cancer diagnosis using ensemble-based classification algorithm. Comput J. 2021. https://doi.org/10.1093/comjnl/bxaa198.

    Article  Google Scholar 

  49. He B, Luo H, Zhou Z, Wang B, Liang Y, Lang J, et al. A neural network framework for predicting the tissue-of-origin of 15 common cancer types based on RNA-seq data. Front Bioeng Biotechnol. 2020;8(8):737–811. https://doi.org/10.3389/fbioe.2020.00737.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of CSE, REVA University, Bengaluru, Karnataka, 560064, India

    Prabhuraj Metipatil, P. Bhuvaneshwari & Syed Muzamil Basha

  2. Department of Computer Science & Applied Mathematics, UAS, Bengaluru, Karnataka, 560064, India

    S. S. Patil

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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

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Metipatil, P., Bhuvaneshwari, P., Basha, S.M. et al. An Efficient Framework for Predicting Cancer Type Based on Microarray Gene Expressions Using CNN-BiLSTM Technique. SN COMPUT. SCI. 4, 381 (2023). https://doi.org/10.1007/s42979-023-01774-5

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