Abstract
The Microarray technology permits simultaneous monitoring of several gene expressions for every sample. But unfortunately, this classification of such samples into the distinct classes has often been found to difficult as the actual number of genes (the features) will to a great extent exceed the actual number of the samples. There is a high dimensionality in gene and its expression data which is a huge challenge in many of the problems of classification. Cloud computing is that popular concept of computing which performs a processing of a data of huge volumes by making use of highly accessible resources that are geographically distributed and accessed by the users based on the policy of pay as per use. In spite of the several steps in that of the B Cell that are now elucidate the last few stages of that of the plasma cell (PC) based differentiation that have not been understood yet. The PCs had generated at the time of primary and humoral immune responses that have started their differentiation within their light zones for the germinal centers of such light zones of that of the lymph nodes or the ones in the red pulp of the spleen having a life span of about few days. The selection of the features will aim at the maintenance of their original features of such data and at this time it will seek at identifying their main features and will weed out all those that are irrelevant for building of a learning model that is impactful. Identifying one global maximum will be Non-deterministic Polynomial (NP)-hard and if the criterion is decomposable or possesses the properties that can make some approximate type of optimization easy. The artificial bee colony (ABC) is that one that is used widely and applied for solving all real world problems. The stochastic diffusion search (SDS) will be an efficient multi-agent global search based technique of optimization applied to that of several problems and here the hybrid ABC with that of the SDS feature selection will be proposed and these images will be grouped as either normal or abnormal PC of the bone marrow that is based on this gene expression data. The support vector machine is that algorithms of supervised learning that is capable of being able to solve the complex problems in classification. This proposed method of gene selection will yield a comparable performance for the classification on being compared to that of the currently existing classifiers providing another new insight in case of feature selection.
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05 December 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s10586-022-03901-y
References
Bennet, J., Ganaprakasam, C., Kumar, N.: A hybrid approach for gene selection and classification using support vector machine. International Arab Journal of Information Technology (IAJIT) 12, 695–700 (2015)
Kalaiselvi, N., Inbarani, H.H.: Fuzzy soft set based classification for gene expression data. arXiv preprint (2013). arXiv:1301.1502
Vecchiola, C., Abedini, M., Kirley, M., Chu, X., Buyya, R.: Gene expression classification with a novel coevolutionary based learning classifier system on public clouds. In: 2010 Sixth IEEE International Conference on e-Science Workshops, pp. 92–97. IEEE (2010)
Stryer, P.: Understanding data centers and cloud computing. Global Knowledge Instructor (2010)
Bhola, A., Tiwari, A.K.: Machine learning based approaches for cancer classification using gene expression data. Mach. Learn. Appl. Int. J. MLAIJ 2(3/4), 1–12 (2015)
Eddy, J.A., Sung, J., Geman, D., Price, N.D.: Relative expression analysis for molecular cancer diagnosis and prognosis. Technol. Cancer Res. Treat. 9(2), 149–159 (2010)
Štifter, S., Babarović, E., Valković, T., Seili-Bekafigo, I., Štemberger, C., Načinović, A., et al.: Combined evaluation of bone marrow aspirate and biopsy is superior in the prognosis of multiple myeloma. Diagn. Pathol. 5(1), 30 (2010)
Kaleem, B.: Plasma cell leukemia-behind a disguise. J. Clin. Case Rep. 5(533), 2 (2015)
Lavanya, C., Nandihini, M., Niranjana, R., Gunavathi, C.: Classification of microarray data based on feature selection method. Int. J. Innov. Res. Sci. Eng. Technol. 3(1), 1–9 (2014)
Gutkin, M., Shamir, R., Dror, G.: SlimPLS: a method for feature selection in gene expression-based disease classification. PLoS ONE 4(7), e6416 (2009)
Song, N., Wang, K., Xu, M., Xie, X., Chen, G., Wang, Y.: Design and analysis of ensemble classifier for gene expression data of cancer. J. Clin. Med. Genomics 3, 134 (2015)
Sharmila, L., Sakthi, U., Geethanjali, A., Sagadevan, S.: Regular expression based pattern matching for gene expression data to identify the abnormality gnome. In: 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM), pp. 301–305. IEEE (2017)
Jia, Y., Li, Y., Liu, W., Dong, H.: An efficient weighted biclustering algorithm for gene expression data. In: 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pp. 336–341. IEEE (2016)
Chen, X., Huang, J.Z., Wu, Q., Yang, M.: Subspace weighting co-clustering of gene expression data. IEEE/ACM Trans. Comput. Biol. Bioinform. 10(4), 845–857 (2017)
Dey, R., Roy, K., Bhattacharjee, D., Nasipuri, M., Ghosh, P.: An automated system for measuring hematocrit level of human blood from total RBC count. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2273–2279. IEEE (2016)
Langmead, B., Hansen, K.D., Leek, J.T.: Cloud-scale RNA-sequencing differential expression analysis with Myrna. Genome Biol. 11(8), R83 (2010)
Langmead, B., Schatz, M.C., Lin, J., Pop, M., Salzberg, S.L.: Searching for SNPs with cloud computing. Genome Biol. 10(11), R134 (2009)
Schatz, M.C.: CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics 25(11), 1363–1369 (2009)
Nguyen, T., Shi, W., Ruden, D.: CloudAligner: a fast and full-featured MapReduce based tool for sequence mapping. BMC Res. Notes 4(1), 171 (2011)
Krampis, K., Booth, T., Chapman, B., Tiwari, B., Bicak, M., Field, D., Nelson, K.E.: Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community. BMC Bioinform. 13(1), 42 (2012)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007)
Shanthi, S., Bhaskaran, V.M.: Modified artificial bee colony based feature selection: a new method in the application of mammogram image classification. Int. J. Sci. Eng. Technol. Res. 3(6), 1664–1667 (2014)
al-Rifaie, M.M., Bishop, J.M.: Stochastic diffusion search review. Paladyn J. Behav. Robot. 4(3), 155–173 (2013)
Williams, H., Bishop, M.: Stochastic diffusion search: a comparison of swarm intelligence parameter estimation algorithms with ransac. Algorithms 7(2), 206–228 (2014)
El-henawy, I.M., Ismail, M.M.: A hybrid swarm intelligence technique for solving integer multi-objective problems. Int. J. Comput. Appl. 87(3), 45 (2014)
Gorgel, P., Sertbaş, A., Kilic, N., Osman, O.: Mammographic mass classification using wavelet based support vector machine. J. Electr. Electron. Eng. 9(1), 867–875 (2009)
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03901-y
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Ragunthar, T., Selvakumar, S. RETRACTED ARTICLE: A wrapper based feature selection in bone marrow plasma cell gene expression data. Cluster Comput 22 (Suppl 6), 13785–13796 (2019). https://doi.org/10.1007/s10586-018-2094-2
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DOI: https://doi.org/10.1007/s10586-018-2094-2