Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique

• Autorzy: Mishra Purnendu , Bhoi Nilamani

Identyfikatory

DOI

10.1016/j.bbe.2021.06.004

• Języki publikacji: EN

Abstrakty

EN

Recognizing the cancer genes from the microarray dataset is considered as the most essential research topic in bioinformatics and computational biology domain. Microarray dataset represents the state of each cell at the molecular level which is identified as the important diagnostic tool in medical field. Analyzing the microarray data may provide a huge support for cancer gene classification. Therefore recently a number of artificial intelligence and machine learning techniques are developed which utilize the microarray data for distinguishing the cancer and non-cancer cells. But still now these techniques does not achieved a satisfactory performance. Therefore, an efficient technique that provides a crisp output for cancer classification is required. To overcome such defect, an enhanced ANFIS (EANFIS) method is used in this proposed architecture for classifying the cancer genes. The convergence time of ANFIS gets increased during learning process, therefore to avoid such issue the Manta ray foraging optimization (MaFO) algorithm is hybrid along with ANFIS which improves the overall classification performance. The data given as an input to the classification process is pre-processed at the initial phase using the Ensemble Kalman Filter (EnKF) technique. After pre-processing, the genes having similar properties are clustered using an adaptive density-based spatial clustering with noise (ADBSCAN) clustering technique. Finally, the performance of proposed enhanced ANFIS is evaluated using the precision, accuracy, f-measure, recall, sensitivity, and specificity metrics. Further, the clustering based performance evaluation is also carried out using the cluster index metrics. Finally, the comparison with the state-of-the-art techniques is also performed to show the effectiveness of proposed approach.

Słowa kluczowe

EN

gene expression; neuro fuzzy inference system; cancer classification; ensemble kalman filter; density based spatial clustering

PL

ekspresja genów; system wnioskowania rozmyty; filtr Kalmana

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 3
• Strony: 916--932
• Opis fizyczny: Bibliogr. 53 poz., rys., tab., wykr.

Twórcy

autor

Mishra Purnendu

• Department of Electronics & Tele Communication Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India

autor

Bhoi Nilamani

• Department of Electronics & Tele Communication Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India

Bibliografia

• [1] Murtaza G, Shuib L, Abdul Wahab AW, Mujtaba G, Mujtaba G, Nweke HF, et al. Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artif Intell Rev 2020;53 (3):1655–720.
• [2] Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015;136(5):E359–86.
• [3] Ferlay J, Colombet M, Soerjomataram I, Dyba T, Randi G, Bettio M, et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018. Eur J Cancer 2018;103:356–87.
• [4] Araújo VJS, Guimarães AJ, de Campos Souza PV, Rezende TS, Araújo VS. Using resistin, glucose, age and bmi and pruning fuzzy neural network for the construction of expert systems in the prediction of breast cancer. Machi Learn Knowledge Extraction 2019;1(1):466–82.
• [5] Kumar A, Halder A. Semi-supervised fuzzy vaguely quantified rough nearest neighbour classifier for cancer sample classification from gene expression data. J Comput Mathemat Sci 2018;9(7):840–9.
• [6] Darrell CM, Montironi R, Paner GP. Potential biomarkers and risk assessment models to enhance the tumor-nodemetastasis (tnm) staging classification of urologic cancers. Expert Rev Mol Diagnost 2020;20(9):921–32.
• [7] Narayanan DL, Girisha KM. Genomic testing for diagnosis of genetic disorders in children: chromosomal microarray and next-generation sequencing. Indian Pediatr 2020;57 (6):549–54.
• [8] Xiao Y, Wu J, Lin Z, Zhao X. A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Programs Biomed 2018;153:1–9.
• [9] Ben Hamda C, Sangeda R, Mwita L, Meintjes A, Nkya S, Panji S, et al. A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study. PLoS ONE 2018;13(7): e0199461. https://doi.org/10.1371/journal.pone.0199461.
• [10] Daoud M, Mayo M. A survey of neural network-based cancer prediction models from microarray data. Artif Intell Med 2019;97:204–14.
• [11] Chaudhary K, Poirion OB, Lu L and Garmire L. Deep learning based multi-omics integration robustly predicts survival in liver cancer. bioRxiv, 2017.
• [12] Li Y, Bai W, Zhang L. The overexpression of CD80 and ISG15 are associated with the progression and metastasis of breast cancer by a meta-analysis integrating three microarray datasets. Pathol Oncol Res 2020;26(1):443–52.
• [13] Almugren N, Alshamlan H. A survey on hybrid feature selection methods in microarray gene expression data for cancer classification. IEEE Access 2019;7:78533–48.
• [14] Lee J, Franovic A, Shiotsu Y, Kim ST, Kim K-M, Banks KC, et al. Detection of ERBB2 (HER2) gene amplification events in cellfree DNA and response to anti-HER2 agents in a large Asian cancer patient cohort. Front Oncol 2019;9. https://doi.org/10.3389/fonc.2019.0021210.3389/fonc.2019.00212.s00110.3389/fonc.2019.00212.s002.
• [15] Wu J, Hu S, Chen Y, Li Z, Zhang J, Yuan H, et al. BCIP: a genecentered platform for identifying potential regulatory genes in breast cancer. Sci Rep 2017;7(1). https://doi.org/10.1038/srep45235.
• [16] Mevlüt TÜRE, Ömürlü İK. Development of a new supervised principal component analysis based on artificial neural networks in gene expression data. Osmangazi Tıp Dergisi 2018;40(1):20–7.
• [17] Vannini I, Fanini F, Fabbri M. Emerging roles of microRNAs in cancer. Curr Opin Genet Dev 2018;48:128–33.
• [18] Wang Y, Yang X-G, Lu Y. Informative gene selection for microarray classification via adaptive elastic net with conditional mutual information. Appl Math Model 2019;71:286–97.
• [19] Kang C, Huo Y, Xin L, Tian B, Yu B. Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine. J Theor Biol 2019;463:77–91.
• [20] Yuan M, Yang Z, Ji G. Partial maximum correlation information: a new feature selection method for microarray data classification. Neurocomputing 2019;323:231–43.
• [21] Mishra P, Bhoi N. Microarray filtering-based fuzzy C-means clustering and classification in genomic signal processing. Arabian J Sci Eng 2019;44(11):9381–95.
• [22] Mishra P and Bhoi N Genomic signal processing of microarrays for cancer gene expression and identification using cluster-fuzzy adaptive networking.
• [23] Deng S-P, Guo W-L. Identifying key genes of liver cancer by networking of multiple data sets. IEEE/ACM Trans Comput Biol Bioinf 2019;16(3):792–800.
• [24] Sampathkumar A, Rastogi R, Arukonda S, Shankar A, Kautish S, Sivaram M. An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data. J Ambient Intell Hum Comput 2020;11(11):4743–51.
• [25] Alanni R, Hou J, Azzawi H, Xiang Y. A novel gene selection algorithm for cancer classification using microarray datasets. BMC Med Genomics 2019;12(1):10.
• [26] Shukla AK. Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique. Comput Intell 2020;36(1):102–31.
• [27] Halder A, Kumar A. Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data. J Biomed Inform 2019;92:103136. https://doi.org/10.1016/j.jbi.2019.103136.
• [28] Zhang W, Wang S-L. An efficient strategy for identifying cancer-related key genes based on graph entropy. Comput Biol Chem 2018;74:142–8.
• [29] Roy S, Kumar R, Mittal V, Gupta D. Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning. Sci Rep 2020;10(1):1–15.
• [30] Yang ZY, Liu XY, Shu J, Zhang H, Ren YQ, Xu ZB, et al. Multiview based integrative analysis of gene expression data for identifying biomarkers. Sci Rep 2019;9(1):1–15.
• [31] Zakaria L, Ebeid HM, Dahshan S and Tolba MF. Analysis of classification methods for gene expression data. In International Conference on Advanced Machine Learning Technologies and Applications, Springer, Cham, March, 2019; 190-199.
• [32] Khani E, Mahmoodian H. Phase diagram and ridge logistic regression in stable gene selection. Biocybernet Biomed Eng 2020;40(3):965–76.
• [33] Zahoor J, Zafar K. Classification of microarray gene expression data using an infiltration tactics optimization (ITO) algorithm. Genes 2020;11(7):819.
• [34] Vuong HG, Nguyen TPX, Hassell LA, Jung CK. Diagnostic performances of the Afirma gene sequencing classifier in comparison with the gene expression classifier: a metaanalysis. Cancer Cytopathol 2021;129(3):182–9.
• [35] Li Z, Xie W, Liu T, Li X. Efficient feature selection and classification for microarray data. PLoS ONE 2018;13(8): e0202167.
• [36] Sun L, Kong X, Xu J, Xue Z, Zhai R, Zhang S. A hybrid gene selection method based on ReliefF and ant colony optimization algorithm for tumor classification. Sci Rep 2019;9(1). https://doi.org/10.1038/s41598-019-45223-x.
• [37] He P, Fan B, Xu X, Ding J, Liang Y, Lou Y, et al. Group K-SVD for the classification of gene expression data. Comput Electr Eng 2019;76:143–53.
• [38] Lu H, Xu Y, Ye M, Yan K, Gao Z, Jin Q. Learning misclassification costs for imbalanced classification on gene expression data. BMC Bioinf 2019;20(25):1–10.
• [39] Ma Y, Lin H, Zhang H, Song X, Yang H. Identification of potential crucial genes associated with early-onset preeclampsia via a microarray analysis. J Obstet Gynaecol Res. 2017;43(5):812–9. https://doi.org/10.1111/jog.13275. PMID: 28759171.
• [40] Liu K, Fu Q, Liu Y and Wang C. An integrative bioinformatics analysis of microarray data for identifying hub genes as diagnostic biomarkers of preeclampsia. Bioscience reports, 2019; 39(9): BSR20190187.
• [41] Yu YW, Xue YJ, Qian LL, Chen Z, Que JQ, Huang KY, et al. Screening and Identification of Potential Hub Genes in Myocardial Infarction through Bioinformatics Analysis. Clin Interv Aging 2020;15:2233.
• [42] Mahmoodian H, Ebrahimian L. Using support vector regression in gene selection and fuzzy rule generation for relapse time prediction of breast cancer. Biocybernet Biomed Eng 2016;36(3):466–72.
• [43] Karimipour H, Leung H. Relaxation-based anomaly detection in cyber-physical systems using ensemble Kalman filter. IET Cyber-Phys Syst: Theor Appl 2020;5(1):49–58.
• [44] Khan MMR, Siddique MAB, Arif RB and Oishe MR. ADBSCAN: Adaptive density-based spatial clustering of applications with noise for identifying clusters with varying densities. In 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), IEEE, 2018, September; 107-111.
• [45] AnandaKumar K and Punithavalli M. Efficient cancer classification using fast adaptive neuro-fuzzy inference system (FANFIS) based on statistical techniques. IJACSA) International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence, 2011; 132-137.
• [46] Zhao W, Zhang Z, Wang L. Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 2020;87:103300. https://doi. org/10.1016/j.engappai.2019.103300.
• [47] Elyasigomari V, Lee DA, Screen HRC, Shaheed MH. Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification. J Biomed Inform 2017;67:11–20.
• [48] Xu J, Mu H, Wang Y and Huang F. Feature genes selection using supervised locally linear embedding and correlation coefficient for microarray classification. Computational and mathematical methods in medicine, 2018; 2018.
• [49] Angulo AP. Gene selection for microarray cancer data classification by a novel rule-based algorithm. Information 2018;9(1):6.
• [50] Mollaee M, Moattar MH. A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification. Biocybernet Biomed Eng 2016;36(3):521–9.
• [51] Elyasigomari V, Mirjafari MS, Screen HRC, Shaheed MH. Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization. Appl Soft Comput 2015;35:43–51.
• [52] Kim BH, Yu K, Lee PC. Cancer classification of single-cell gene expression data by neural network. Bioinformatics 2020;36(5):1360–6.
• [53] Shukla AK, Singh P, Vardhan M. A hybrid gene selection method for microarray recognition. Biocybernet Biomed Eng 2018;38(4):975–91.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).