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A Survey of Modern Gene Expression Based Techniques for Cancer Detection and Diagnosis

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Soft Computing Applications (SOFA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1222))

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Abstract

Cancer is a leading cause of death and majority of cancer patients are diagnosed in the late stages of cancer by using conventional methods. The gene expression microarray technology is applied to detect and diagnose most types of cancers in their early stages. Furthermore, it allows researchers to analyze thousands of genes simultaneously. To acquire knowledge from gene expression data, data mining methods are needed. Due to the rapid evolution of cancer detection and diagnosis techniques, a survey of modern techniques is desirable. This study reviews and provide a detailed description of these techniques. As a result, it helps to enhance existing techniques for cancer detection and diagnosis as well as guiding researchers to develop new approaches.

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References

  1. Cullen, B.R., Zeng, Y.: Method of regulating gene expression, US Patent 9,856,476, 2 January 2018

    Google Scholar 

  2. Bewick, A.J., Schmitz, R.J.: Gene body DNA methylation in plants. Curr. Opin. Plant Biol. 36, 103–110 (2017)

    Article  Google Scholar 

  3. Vijayakumar, P., Vijayalakshmi, V., Rajashree, R.: Increased level of security using DNA steganography. Int. J. Adv. Intell. Paradigms 10(1–2), 74–82 (2018)

    Article  Google Scholar 

  4. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C.-H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J.P., et al.: Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Nat. Acad. Sci. 98(26), 15149–15154 (2001)

    Article  Google Scholar 

  5. Javid, Q., Arif, M., Talpur, S.: Segmentation and classification of calcification and hemorrhage in the brain using fuzzy c-mean and adaptive neuro-fuzzy inference system. Mehran Univ. Res. J. Eng. Technol. 15(1), 29 (2016)

    Google Scholar 

  6. Muhammad, A., Guojun, W.: Segmentation of calcification and brain hemorrhage with midline detection. In: 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications, pp. 1082–1090. IEEE (2017)

    Google Scholar 

  7. Javaid, Q., Arif, M., Shah, M.A., Nadeem, M., et al.: A hybrid technique for de-noising multi-modality medical images by employing cuckoo’s search with curvelet transform. Mehran Univ. Res. J. Eng. Technol. 37(1), 29 (2018)

    Article  Google Scholar 

  8. Arif, M., Alam, K.A., Hussain, M.: Application of data mining using artificial neural network: survey. Int. J. Database Theory Appl. 8(1), 245–270 (2015)

    Article  Google Scholar 

  9. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)

    Article  Google Scholar 

  10. Hong, S.-S., Kim, D.-W., Han, M.-M.: An improved data pre-processing method for classification and insider information leakage detection. Int. J. Adv. Intell. Paradigms 11(1–2), 143–158 (2018)

    Article  Google Scholar 

  11. Patel, S.J., Sanjana, N.E., Kishton, R.J., Eidizadeh, A., Vodnala, S.K., Cam, M., Gartner, J.J., Jia, L., Steinberg, S.M., Yamamoto, T.N., et al.: Identification of essential genes for cancer immunotherapy. Nature 548(7669), 537 (2017)

    Article  Google Scholar 

  12. Sudha, V.K., Sudhakar, R., Balas, V.E.: Fuzzy rule-based segmentation of CT brain images of hemorrhage for compression. Int. J. Adv. Intell. Paradigms 4(3–4), 256–267 (2012)

    Article  Google Scholar 

  13. Rathore, S., Hussain, M., Ali, A., Khan, A.: A recent survey on colon cancer detection techniques. IEEE/ACM Trans. Comput. Biol. Bioinform. 10(3), 545–563 (2013)

    Article  Google Scholar 

  14. Lemaître, G., Martí, R., Freixenet, J., Vilanova, J.C., Walker, P.M., Meriaudeau, F.: Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput. Biol. Med. 60, 8–31 (2015)

    Article  Google Scholar 

  15. Saha, D., Bhowmik, M.K., De, B.K., Bhattacharjee, D.: A survey on imaging-based breast cancer detection. In: Proceedings of Fourth International Conference on Soft Computing for Problem Solving, pp. 255–266. Springer (2015)

    Google Scholar 

  16. Lee, K., Man, Z., Wang, D., Cao, Z.: Classification of bioinformatics dataset using finite impulse response extreme learning machine for cancer diagnosis. Neural Comput. Appl. 22(3–4), 457–468 (2013)

    Article  Google Scholar 

  17. Goswami, S., Chakrabarti, A., Chakraborty, B.: An empirical study of feature selection for classification using genetic algorithm. Int. J. Adv. Intell. Paradigms 10(3), 305–326 (2018)

    Article  Google Scholar 

  18. Lotfi, E., Keshavarz, A.: Gene expression microarray classification using PCA-BEL. Comput. Biol. Med. 54, 180–187 (2014)

    Article  Google Scholar 

  19. Rathore, S., Iftikhar, M.A., Hussain, M.: A novel approach for automatic gene selection and classification of gene based colon cancer datasets. In: 2014 International Conference on Emerging Technologies (ICET), pp. 42–47. IEEE (2014)

    Google Scholar 

  20. Bouazza, S.H., Hamdi, N., Zeroual, A., Auhmani, K.: Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers. In: 2015 Intelligent Systems and Computer Vision (ISCV), pp. 1–6. IEEE (2015)

    Google Scholar 

  21. Paul, S., Maji, P.: Gene expression and protein-protein interaction data for identification of colon cancer related genes using f-information measures. Natural Comput. 15, 1–15 (2015)

    MathSciNet  MATH  Google Scholar 

  22. Kim, K.-J., Cho, S.-B.: Meta-classifiers for high-dimensional, small sample classification for gene expression analysis. Pattern Anal. Appl. 18, 1–17 (2014)

    Google Scholar 

  23. Nguyen, T., Khosravi, A., Creighton, D., Nahavandi, S.: A novel aggregate gene selection method for microarray data classification. Pattern Recogn. Lett. 60, 16–23 (2015)

    Article  Google Scholar 

  24. Banka, H., Dara, S.: A hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. Pattern Recogn. Lett. 52, 94–100 (2015)

    Article  Google Scholar 

  25. Simjanoska, M., Bogdanova, A.M., Popeska, Z.: Recognition of colorectal carcinogenic tissue with gene expression analysis using Bayesian probability. In: ICT Innovations, pp. 305–314 (2012)

    Google Scholar 

  26. Bogdanova, A.M., Simjanoska, M., Popeska, Z.: Classification of colorectal carcinogenic tissue with different DNA chip technologies. In: The 6th International Conference on Information Technology, Ser. ICIT (2013)

    Google Scholar 

  27. Wong, W.-C., Loh, M., Eisenhaber, F.: On the necessity of different statistical treatment for illumina beadchip and affymetrix genechip data and its significance for biological interpretation. Biol. Direct 3(1), 23 (2008)

    Article  Google Scholar 

  28. Simjanoska, M., Bogdanova, A.M., Popeska, Z.: Bayesian posterior probability classification of colorectal cancer probed with Affymetrix microarray technology. In: 2013 36th International Convention on Information & Communication Technology Electronics & Microelectronics (MIPRO), pp. 959–964. IEEE (2013)

    Google Scholar 

  29. Simjanoska, M., Bogdanova, A.M.: Novel methodology for CRC biomarkers detection with leave-one-out Bayesian classification. In: ICT Innovations 2014, pp. 225–236. Springer (2015)

    Google Scholar 

  30. Ibrahim, R., Yousri, N., Ismail, M.A., El-Makky, N.M., et al.: Multi-level gene/MiRNA feature selection using deep belief nets and active learning. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3957–3960. IEEE (2014)

    Google Scholar 

  31. Wu, H.C., Zhang, L., Chan, S.C.: Reconstruction of gene regulatory networks from short time series high throughput data: review and new findings. In: 2014 19th International Conference on Digital Signal Processing (DSP), pp. 733–738. IEEE (2014)

    Google Scholar 

  32. Burton, M., Thomassen, M., Tan, Q., Kruse, T.A.: Gene expression profiles for predicting metastasis in breast cancer: a cross-study comparison of classification methods. Sci. World J. 2012 (2012)

    Google Scholar 

  33. Otoom, A.F., Abdallah, E.E., Hammad, M.: Breast cancer classification: comparative performance analysis of image shape-based features and microarray gene expression data. Int. J. Bio-Sci. Bio-Technol. 7(2), 37–46 (2015)

    Article  Google Scholar 

  34. Tong, M., Liu, K.-H., Chungui, X., Wenbin, J.: An ensemble of SVM classifiers based on gene pairs. Compute. Biol. Med. 43(6), 729–737 (2013)

    Article  Google Scholar 

  35. Li, D., Wang, Z., Cao, C., Liu, Y.: Information entropy based sample reduction for support vector data description. Appl. Soft Comput. 71, 1153–1160 (2018)

    Article  Google Scholar 

  36. Arif, M., Abdullah, N.A., Phalianakote, S.K., Ramli, N., Elahi, M.: Maximizing information of multimodality brain image fusion using curvelet transform with genetic algorithm. In: 2014 International Conference on Computer Assisted System in Health (CASH), pp. 45–51. IEEE (2014)

    Google Scholar 

  37. Cao, J., Zhang, L., Wang, B., Li, F., Yang, J.: A fast gene selection method for multi-cancer classification using multiple support vector data description. J. Biomed. Inform. 53, 381–389 (2015)

    Article  Google Scholar 

  38. Karimi, S., Farrokhnia, M.: Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: combining data dimension reduction and variable selection technique. Chemometr. Intell. Lab. Syst. 139, 6–14 (2014)

    Article  Google Scholar 

  39. Rathore, S., Hussain, M., Khan, A.: GECC: gene expression based ensemble classification of colon samples. IEEE/ACM Trans. Comput. Biol. Bioinf. (TCBB) 11(6), 1131–1145 (2014)

    Article  Google Scholar 

  40. Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Nat. Acad. Sci. 96(12), 6745–6750 (1999)

    Article  Google Scholar 

  41. Colon cancer data set Biogps (2013). http://biogps.org/dataset/1352/stage-ii-and-stage-iii-colorectal-cancer/

  42. Notterman, D.A., Alon, U., Sierk, A.J., Levine, A.J.: Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays. Cancer Res. 61(7), 3124–3130 (2001)

    Google Scholar 

  43. Hinoue, T., Weisenberger, D.J., Lange, C.P.E., Shen, H., Byun, H.-M., Van Den Berg, D., Malik, S., Pan, F., Noushmehr, H., van Dijk, C.M., et al.: Genome-scale analysis of aberrant DNA methylation in colorectal cancer. Genome Res. 22(2), 271–282 (2012)

    Article  Google Scholar 

  44. Singh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., D’Amico, A.V., Richie, J.P., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1(2), 203–209 (2002)

    Article  Google Scholar 

  45. Petkovi’c, D., Arif, M., Shamshirband, S., Bani-Hani, E.H., Kiakojoori, D.: Sensorless estimation of wind speed by soft computing methodologies: a comparative study. Informatica 26(3), 493–508 (2015)

    Article  Google Scholar 

  46. Su, A.I., Cooke, M.P., Ching, K.A., Hakak, Y., Walker, J.R., Wiltshire, T., Orth, A.P., Vega, R.G., Sapinoso, L.M., Moqrich, A., et al.: Large-scale analysis of the human and mouse transcriptomes. Proc. Nat. Acad. Sci. 99(7), 4465–4470 (2002)

    Article  Google Scholar 

  47. Bhattacharjee, A., Richards, W.G., Staunton, J., Li, C., Monti, S., Vasa, P., Ladd, C., Beheshti, J., Bueno, R., Gillette, M., et al.: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Nat. Acad. Sci. 98(24), 13790–13795 (2001)

    Article  Google Scholar 

  48. Van’t Veer, L.J., Dai, H., Van De Vijver, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., Van Der Kooy, K., Marton, M.J., Witteveen, A.T., et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871), 530–536 (2002)

    Article  Google Scholar 

  49. Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu, X., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769), 503–511 (2000)

    Article  Google Scholar 

  50. Hu, Z., Killion, P.J., Iyer, V.R.: Genetic reconstruction of a functional transcriptional regulatory network. Nat. Genet. 39(5), 683–687 (2007)

    Article  Google Scholar 

  51. Sathishkumar, E.N., Thangavel, K, Nishama, A: Comparative analysis of discretization methods for gene selection of breast cancer gene expression data. In: Computational Intelligence, Cyber Security and Computational Models, pp. 373–378. Springer (2014)

    Google Scholar 

  52. Marisa, L., de Reyniès, A., Duval, A., Selves, J., Gaub, M.P., Vescovo, L., Etienne-Grimaldi, M.C., Schiappa, R., Guenot, D., Ayadi, M., et al.: Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value. PLoS Med. 10, e1001453 (2013)

    Article  Google Scholar 

  53. Liu, Y., Ji, Y., Qiu, P.: Identification of thresholds for dichotomizing DNA methylation data. EURASIP J. Bioinform. Syst. Biol. 2013, 8 (2013)

    Article  Google Scholar 

  54. Yang, K.-C., Hsu, C.-L., Lin, C.-C., Juan, H.-F., Huang, H.-C.: Mirin: identifying microrna regulatory modules in protein-protein interaction networks. Bioinformatics 30(17), 2527–2528 (2014)

    Article  Google Scholar 

  55. Qi, P., Xiang, D.: The long non-coding RNAs, a new cancer diagnostic and therapeutic gold mine. Mod. Pathol. 26(2), 155–165 (2013)

    Article  Google Scholar 

  56. Won, J.R., Gao, D., Chow, C., Cheng, J., Lau, S.Y., Ellis, M.J., Perou, C.M., Bernard, P.S., Nielsen, T.O.: A survey of immunohistochemical biomarkers for basal-like breast cancer against a gene expression profile gold standard. Mod. Pathol. 26(11), 1438–1450 (2013)

    Article  Google Scholar 

  57. Radha, R., Rajendiran, P.: Using k-means clustering technique to study of breast cancer. In: 2014 World Congress on Computing and Communication Technologies (WCCCT), pp. 211–214. IEEE (2014)

    Google Scholar 

  58. Wang, N., Wang, Y., Hao, H., Wang, L., Wang, Z., Wang, J., Wu, R.: A bi-Poisson model for clustering gene expression profiles by RNA-seq. Briefings Bioinform. 15, 534–541 (2013). bbt029bbt029

    Article  Google Scholar 

  59. Jun, H., Tzeng, J.-Y.: Integrative gene set analysis of multi-platform data with sample heterogeneity. Bioinformatics 30(11), 1501–1507 (2014)

    Article  Google Scholar 

  60. Mahata, K., Sarkar, A.: Cancer gene silencing network analysis using cellular automata. In: 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1–5. IEEE (2015)

    Google Scholar 

  61. Saribudak, A., Gundry, S., Zou, J., Uyar, M.U.: Genomic based personalized chemotherapy analysis to support decision systems for breast cancer. In: 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 495–500. IEEE (2015)

    Google Scholar 

  62. Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Statis. Assoc. 97(457), 77–87 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  63. Dettling, M., Bühlmann, P.: Boosting for tumor classification with gene expression data. Bioinformatics 19(9), 1061–1069 (2003)

    Article  Google Scholar 

  64. Chamard-Jovenin, C., Jung, A.C., Chesnel, A., Abecassis, J., Flament, S., Ledrappier, S., Macabre, C., Boukhobza, T., Dumond, H.: From er\(\alpha \)66 to er\(\alpha \)36: a generic method for validating a prognosis marker of breast tumor progression. BMC Syst. Biol. 9(1), 28 (2015)

    Article  Google Scholar 

  65. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Hoboken (2004)

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, Guangzhou University, Guangzhou, 510006, China

    Hafiz ur Rahman, Muhammad Arif, Guojun Wang & Jianer Chen

  2. Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia

    Sadam Al-Azani & Emad Ramadan

  3. Faculty of Medicine, Vasile Goldis Western University of Arad, Arad, Romania

    Teodora Olariu & Iustin Olariu

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  1. Hafiz ur Rahman
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  2. Muhammad Arif
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  3. Sadam Al-Azani
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  4. Emad Ramadan
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  5. Guojun Wang
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  6. Jianer Chen
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  7. Teodora Olariu
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  8. Iustin Olariu
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Corresponding author

Correspondence to Guojun Wang .

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Editors and Affiliations

  1. "Aurel Vlaicu" University of Arad, Faculty of Engineering, Arad, Romania

    Valentina Emilia Balas

  2. Faculty of Engineering and Information Technology, University of Technology Sydney, Centre for Artificial Intelligence, Sydney, Australia

    Lakhmi C. Jain

  3. Faculty of Engineering, “Aurel Vlaicu” University of Arad, Arad, Arad, Romania

    Marius Mircea Balas

  4. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

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Rahman, H.u. et al. (2021). A Survey of Modern Gene Expression Based Techniques for Cancer Detection and Diagnosis. In: Balas, V., Jain, L., Balas, M., Shahbazova, S. (eds) Soft Computing Applications. SOFA 2018. Advances in Intelligent Systems and Computing, vol 1222. Springer, Cham. https://doi.org/10.1007/978-3-030-52190-5_3

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