Abstract
The extraction of useful information from deoxyribonucleic acid (DNA) is a major component of bioinformatics research, and DNA sequence categorization has a variety of applications, including genomic and biomedical data processing. DNA sequence classification is a critical problem in a general computational framework for biomedical data processing, and numerous machine learning techniques have been used to complete this task in recent years. Machine learning is a data processing technique that uses training data to create judgments, predictions, classifications, and recognitions. To learn the functions of a new protein, genomic researchers classify DNA sequences into known categories. As a result, it is critical to discover and characterize those genes. We employ machine learning approaches to distinguish between infected and normal genes using classification methods. In this study, we used the multinomial Naive Bayes classifier, SVM, KNN, and others to classify DNA sequences using label and k-mer encoding. Different categorization metrics are used to evaluate the models. The multinomial Naive Bayes classifier, SVM, KNN, decision tree, random forest, and logistic regression with k-mer encoding all have good accuracy on testing data, with 93.16% and 93.13%, respectively.
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References
Gelfand, M.S.: Prediction of function in DNA sequence analysis. J. Comput. Biol. 2(1), 87–115 (1995)
Bukh, J., Purcell, R.H., Miller, R.H.: Importance of primer selection for the detection of hepatitis C virus RNA with the polymerase chain reaction assay. Proc. Natl. Acad. Sci. 89(1), 187–191 (1992)
Dorn-In, S., Bassitta, R., Schwaiger, K., Bauer, J., Hölzel, C.S.: Specific amplification of bacterial DNA by optimized so-called universal bacterial primers in samples rich in plant DNA. J. Microbiol. Methods 113, 50–56 (2015)
Pacheco, M.A., Cepeda, A.S., Bernotienė, R., Lotta, I.A., Matta, N.E., Valkiūnas, G., Escalante, A.A.: Primers targeting mitochondrial genes of avian haemosporidian: PCR detection and differential DNA amplification of parasites belonging to different genera. Int. J. Parasitol 48 (8), 657–670 (2018)
Mridha, K. et al.: Deep learning algorithms are used to automatically detection invasive ducal carcinoma in whole slide images. In: 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), pp. 123–129 (2021). https://doi.org/10.1109/ICCCA52192.2021.9666302
Mridha, K., et al.: Web based brain tumor detection using neural network. In: 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), pp. 137–143 (2021). https://doi.org/10.1109/ICCCA52192.2021.9666248
Zheng, Y., Azevedo, R.B.R., Graur, D.: An Evolutionary Classification of Genomic Function, vol. 7, no. 3, p. 4 (2015)
Mridha, K., Pandey, A.P., Ranpariya, A., Ghosh, A., Shaw, R.N.: Web-based brain tumor detection using neural network. In: 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), pp. 137–143 (2021)
Boggess, L., Chen, L.: Neural networks for genome signature analysis. In: 9th International Conference on Neural Information Processing (ICONIP’OZ)
Srinivasa Rao, P.S.V., Usha Devi, N.S.S.S.N., Kiran Sree, P.: CDLGP: a novel unsupervised classifier using deep learning for gene prediction. In: IEEE International Conference on Power, Control, Signals, and Instrumentation Engineering (2017)
Vijayan, K., Gopinath, D.P., Nair, A.S., Nair, V.V.: ANN-based classification of unknown genome fragments using chaos game representation. In: Second International Conference on Machine Learning and Computing (2010)
Wang, J.T.L., Shasha, D., Wu, C.H., Ma, Q.: DNA sequence classification via an expectation-maximization algorithm and neural networks: a case study. In: IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews (2001)
Sinha, T., et al.: Analysis and prediction of COVID-19 confirmed cases using deep learning models: a comparative study. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds.) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol. 218. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2164-2_18
Auerbach, J., Gola, D., Held, E., Holzinger, E.R., Legault, M.-A., Sun, R., Tintle, N., Yang, H.-C., König, I.R.: Machine learning and data mining in complex genomic data—a review on the lessons learned in Genetic Analysis Workshop 19, p. 8 (2016)
Ross Quinlan, J.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Breiman, L.: Random forest. Mach. Learn. 45(1), 5–32 (2001)
Mridha, K., Kumar, D., Shukla, M., Jani, M.: Temporal features and machine learning approaches to study brain activity with EEG and ECG. Int. Conf. Adv. Comput. Innov. Technol. Eng. (ICACITE) 2021, 409–414 (2021)
Mridha, K., Kumbhani, S., Jha, S., Joshi, D., Ghosh, A., Shaw, R.N.: Deep learning algorithms are used to automatically detection invasive ducal carcinoma in whole slide images. In: 2021 IEEE 6th International Conference on Computing, Communication, and Automation (ICCCA), pp. 123–129 (2021)
Palimkar, P. et al.: Machine learning technique to prognosis diabetes disease: random forest classifier approach. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds.) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol. 218. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2164-2_19
Hamidi, O., Poorolajal, J., Sadeghifar, M., Abbasi, H., Maryanaji, Z., Faridi, H.R., Tapak, L.: A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran. Theoret. Appl. Climatol. 119(3–4), 723–731 (2015)
Mridha, K.: Early prediction of breast cancer by using artificial neural network and machine learning techniques. In: 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), pp. 582–587 (2021)
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Sarkar, S., Mridha, K., Ghosh, A., Shaw, R.N. (2022). Machine Learning in Bioinformatics: New Technique for DNA Sequencing Classification. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_27
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DOI: https://doi.org/10.1007/978-981-19-2980-9_27
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