Cancer disease multinomial classification using transfer learning and SVM on the genes’ sequences
Keywords:Cancer, FCGR, Deep Insight, Transfer Learning, SVM
INTRODUCTION: Early disease detection plays an important role in medical field especially for cancer disease, which helps doctors in diagnosing and identifying the therapeutic process. Aiming to provide assistance, many biological techniques other than machine and deep learning models were proposed. They were applied on a different type of data such as medical images and clinical data. Despite the efficiency of those techniques, they remain costly and need a lot of execution and preparation time, and resources.
OBJECTIVES: In this paper, we present a novel method of disease detection analyzing the genes sequences composition.
METHODS: We start by extracting k-mer nucleotides as features from gene sequences with the Frequency Chaos Game Representation (FCGR) technique. Since extracted data are huge, we use a DeepInsight model to extract the most representative k-mers.
A combination of a transfer learning model, which is Residual neural Network (ResNet), and a support vector machine (SVM) algorithm is then used then to classify samples into 18 cancer disease types.
RESULTS: We achieved an accuracy of 0.98 while choosing FCGR6 in feature extraction, and a combination of ResNet50 and SVM in the multinomial classification step, against an accuracy of 0.97 while using ResNet50 with a fully connected layer and FCGR5.
CONCLUSION: Defining the gene sequence alterations helps in the disease detection at early stage. Here, we adopt the FCGR method (that gives the frequency of each k-mer) in defining features of the gene sequences. Then, we use deep learning models to deal with the big number of characteristics and predicting different cancer diseases.
Jiang L, Zhu J. Review of MiRNA-disease association prediction. Current Protein and Peptide Science. 2020; 21(11): 1044–1053. DOI: https://doi.org/10.2174/1389203721666200210102751
Shakeel PM, Burhanuddin MA, Desa MI. Lung cancer detection from ct image using improved profuse clustering and deep learning instantaneously trained neural networks. Measurement. 2019; 145: 702-712. DOI: https://doi.org/10.1016/j.measurement.2019.05.027
Asuntha A SA. Deep learning for lung cancer detection and classification. Multimedia Tools and Applications. 2020; 79(11): 7731–7762. DOI: https://doi.org/10.1007/s11042-019-08394-3
Allugunti VR. Breast cancer detection based on thermographic images using machine learning and deep learning algorithms. International Journal of Engineering in Computer Science. 2022; 4(1): 49--56. DOI: https://doi.org/10.33545/26633582.2022.v4.i1a.68
Alanazi, Saad Awadh and Kamruzzaman, MM and Islam Sarker, Md Nazirul and Alruwaili, Madallah and Alhwaiti, Yousef and Alshammari, Nasser and Siddiqi, Muhammad Hameed. Boosting breast cancer detection using convolutional neural network. Journal of Healthcare Engineering. 2021; 2021. DOI: https://doi.org/10.1155/2021/5528622
Begum, Almas and Kumar, V Dhilip and Asghar, Junaid and Hemalatha, D and Arulkumaran, G. A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis. Complexity. 2022; 2022. DOI: https://doi.org/10.1155/2022/9299621
Mambou, Sebastien Jean and Maresova, Petra and Krejcar, Ondrej and Selamat, Ali and Kuca, Kamil. Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors. 2018; 18: 2799. DOI: https://doi.org/10.3390/s18092799
Nawaz, Majid and Sewissy, Adel A and Soliman, Taysir Hassan A. Multi-class breast cancer classification using deep learning convolutional neural network. Int. J. Adv. Comput. Sci. Appl. 2018; 9(6): 316--332. DOI: https://doi.org/10.14569/IJACSA.2018.090645
Mallick, Pradeep Kumar and Ryu, Seuc Ho and Satapathy, Sandeep Kumar and Mishra, Shruti and Nguyen, Gia Nhu and Tiwari, Prayag. Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access. 2019; 7: 46278--46287. DOI: https://doi.org/10.1109/ACCESS.2019.2902252
Alharbi, Fadi and Vakanski, Aleksandar. Machine learning methods for cancer classification using gene expression data: A review. Bioengineering. 2023; 10: 173. DOI: https://doi.org/10.3390/bioengineering10020173
Ainscough, Benjamin J and Griffith, Malachi and Coffman, Adam C and Wagner, Alex H and Kunisaki, Jason and Choudhary, Mayank NK and McMichael, Joshua F and Fulton, Robert S and Wilson, Richard K and Griffith, Obi L and others. DoCM: a database of curated mutations in cancer. Nature methods. 2016; 13(10): 806--807. DOI: https://doi.org/10.1038/nmeth.4000
Jeffrey, H Joel. Chaos game representation of gene structure. Nucleic acids research. 1990; 18(8): 2163--2170. DOI: https://doi.org/10.1093/nar/18.8.2163
Sharma, Alok and Vans, Edwin and Shigemizu, Daichi and Boroevich, Keith A and Tsunoda, Tatsuhiko. DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture. Scientific reports. 2019; 9(1): 1--7. DOI: https://doi.org/10.1038/s41598-019-47765-6
Laurens van der Maaten and Geoffrey Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research. 2008; 9(86): 2579--2605.
Schölkopf, Bernhard and Smola, Alexander and Müller, Klaus-Robert. Kernel principal component analysis. In International conference on artificial neural networks; 1997: Springer. p. 583--588. DOI: https://doi.org/10.1007/BFb0020217
Sain, Stephan R. The nature of statistical learning theory. 1996.. DOI: https://doi.org/10.1080/00401706.1996.10484565
Simonyan, Karen and Zisserman, Andrew. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014.
He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian. Proceedings of the IEEE conference on computer vision and pattern recognition. In ; 2016. p. 770--778.
Kramer, Oliver and Kramer, Oliver. Genetic algorithms: Springer; 2017. DOI: https://doi.org/10.1007/978-3-319-52156-5
Lopez-Garcia, Guillermo and Jerez, Jose M and Franco, Leonardo and Veredas, Francisco J. Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data. PloS one. 2020; 15. DOI: https://doi.org/10.1371/journal.pone.0230536
Das, Bihter and Toraman, Suat. Deep transfer learning for automated liver cancer gene recognition using spectrogram images of digitized DNA sequences. Biomedical Signal Processing and Control. 2022; 72: 103317. DOI: https://doi.org/10.1016/j.bspc.2021.103317
How to Cite
Copyright (c) 2023 Ines Slimene, Imene Messaoudi, Afef Elloumi Oueslati, Zied Lachiri
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.