Alberts, B. et al. (2017) Molecular Biology of the Cell. https://doi.org/10.1201/9781315735368.

Huang, W., Zhang, J., Wang, Y., & Huang, D. (2010) A simple method to analyze the similarity of biological sequences based on the fuzzy theory. Journal of Theoretical Biology, 265, 3. https://doi.org/10.1016/j.jtbi.2010.05.008.

Khastan, A. & Hooshyar, L. (2019) A computational method to analyze the similarity of biological sequences under uncertainty. Iranian Journal of Fuzzy Systems. 16, 6. https://doi.org/10.22111/ijfs.2019.5017.

Khodaei, A., Feizi-Derakhshi, M. R., & Mozaffari-Tazehkand B. (2021) A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences. BioImpacts, 11, 2. https://doi.org/10.34172/BI.2021.16.

Khodaei, A., Feizi-Derakhshi, M. R., & Mozaffari-Tazehkand, B. (2020) A pattern recognition model to distinguish cancerous DNA sequences via signal processing methods. Soft Computing, 24, 21. https://doi.org/10.1007/s00500-020-04942-4.

Yang, A., Zhang, W., Wang, J., Yang, K., Han, Y., & Zhang, L. (2020) Review on the application of machine learning algorithms in the sequence data mining of DNA. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/fbioe.2020.01032.

Sun, Y. et al. (2019) Identification of 12 cancer types through genome deep learning. Science Reports, 9, 1. https://doi.org/10.1038/s41598-019-53989-3.

Akbar, S., Hayat, M., Iqbal, M., & Jan, M. A. (2017). iACP-GAEnsC: evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space. Artificial Intellegence in Medicine, 79, 62–70. https://doi.org/10.1016/j.artmed.2017.06.008.

Article  Google Scholar 

Akbar, S., Hayat, M., Tahir, M., Khan, S., & Alarfaj, F. K. (2022). cACP-DeepGram: classification of anticancer peptides via deep neural network and skip-gram-based word embedding model. Artificial Intellegence in Medicine, 131, 102349. https://doi.org/10.1016/j.artmed.2022.102349.

Article  Google Scholar 

Akbar, S., Rahman, A. U., Hayat, M., & Sohail, M. (2020). cACP: classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components. Chemometrics and Intelligent Laboratory Systems, 196, 103912. https://doi.org/10.1016/j.chemolab.2019.103912.

Article  CAS  Google Scholar 

Akbar, S., Hayat, M., Tahir, M., & Chong, K. T. (2020). CACP-2LFS: classification of anticancer peptides using sequential discriminative model of KSAAP and two-level feature selection approach. IEEE Access, 8, 131939–131948. https://doi.org/10.1109/ACCESS.2020.3009125.

Article  Google Scholar 

Pecorino, L. (2012) Molecular Biology of Cancer: Mechanisms, Targets, And Therapeutics. Oxford University Press.

Singh, M., Prasad, C. P., Singh, T. D., & Kumar, L. (2018). Cancer research in India: Challenges & opportunities. Indian Journal of Medical Research, 148, 362–365. https://doi.org/10.4103/ijmr.IJMR_1711_18.

Article  PubMed  PubMed Central  Google Scholar 

Zhang, J., Zhang, W., & Yang, H. (2016) In search of coding and non-coding regions of DNA sequences based on balanced estimation of diffusion entropy. Journal of Biological Physics, 42. https://doi.org/10.1007/s10867-015-9399-7.

Das, J., Barman, S., & Das, J. (2014) Bayesian fusion in cancer gene prediction CODEC design view project genomic signal processing view project Bayesian fusion in cancer gene prediction. CCSN. [Online]. Available: https://www.researchgate.net/publication/280917849

Satapathi, G. N., Srihari, P., Jyothi, A., & Lavanya, S. (2013) Prediction of cancer cell using DSP techniques. in International Conference on Communication and Signal Processing, ICCSP 2013 - Proceedings. https://doi.org/10.1109/iccsp.2013.6577034.

Roy, T., & Barman, S. (2014) A behavioral study of healthy and cancer genes by modeling electrical network. Gene, 550. https://doi.org/10.1016/j.gene.2014.08.020.

Roy, T., & Barman, S. (2016) Performance analysis of network model to identify healthy and cancerous colon genes. IEEE Journal of Biomedical and Health Informatics, 20. https://doi.org/10.1109/JBHI.2015.2408366.

Das, J., & Barman, S. (2017) DSP based entropy estimation for identification and classification of Homo sapiens cancer genes. Microsystem Technologies, 23 (no. 9). https://doi.org/10.1007/s00542-016-3056-3.

Singha Roy, S., & Barman, S. (2021) A non-invasive cancer gene detection technique using FLANN based adaptive filter. Microsystem Technologies, 27 (no. 2). https://doi.org/10.1007/s00542-018-4036-6.

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13. 2015. https://doi.org/10.1016/j.csbj.2014.11.005.

Margaliot, M. (2008) Pattern Recognition (Theodoridis, S. and Koutroumbas, K.; 2006) [Book reviews]. IEEE Transactions on Neural Networks, 19 (no. 2). https://doi.org/10.1109/tnn.2008.929642.

SenthilVelMurugan, N., Vallinayagam, V. V. V., Senthamarai Kannan, & Viveka, T. (2013) Analysis of liver cancer DNA sequence data using data mining. International Journal of Computer Application, 61 (no. 3). https://doi.org/10.5120/9909-4502.

Blitzstein, J. K., & Hwang, J. (2014) Introduction to probability. https://doi.org/10.1201/b17221.

Fernandes, A. A. T., Filho, D. B. F., da Rocha, E. C., & da Silva Nascimento, W. (2020) Read this paper if you want to learn logistic regression. Revista de Sociologia e Politica, vol. 28 (no. 74). https://doi.org/10.1590/1678-987320287406EN.

Liu, L. (2018) Research on logistic regression algorithm of breast cancer diagnose data by machine learning. in Proceedings - 2018 International Conference on Robots and Intelligent System, ICRIS 2018, Institute of Electrical and Electronics Engineers Inc., pp. 157–160. https://doi.org/10.1109/ICRIS.2018.00049.

Ha, J., Kambe, M., & Pe, J. (2011) Data Mining, Data Mining: Concepts and Techniques. https://doi.org/10.1016/C2009-0-61819-5.

Dong, G., & Pei, J. (2007) Classification, clustering, features and distances of sequence data. in Sequence Data Mining, 47–65. https://doi.org/10.1007/978-0-387-69937-0_3.

Shaikh, F. J., & Rao, D. S. (2021). Prediction of cancer disease using machine learning approach. in Materials Today: Proceedings, 50, 40–47. https://doi.org/10.1016/j.matpr.2021.03.625.

Article  Google Scholar 

De Ridder, D., De Ridder, J., & Reinders, M. J. T. (2013) Pattern recognition in bioinformatics. Briefings in Bioinformatic, 14 (no. 5). https://doi.org/10.1093/bib/bbt020.

Rong, M. L. K., Kuruoglu, E. E., & Chan, W. K. V. (2023) Modeling SARS-CoV-2 nucleotide mutations as a stochastic process. PLoS One, 18 (no. 4). https://doi.org/10.1371/journal.pone.0284874.

Rymarczyk, T., Kozłowski, E., Kłosowski, G., & Niderla, K. (2019) Logistic regression for machine learning in process tomography. Sensors (Switzerland), 19 (no. 15). https://doi.org/10.3390/s19153400.

Burge, C. B., & Karlin, S. (1998) Finding the genes in genomic DNA. Current Opinion in Structural Biology, 8 (no. 3). https://doi.org/10.1016/S0959-440X(98)80069-9.

GenBank National Center for Biotechnology Information Database. Available from: http://www.ncbi.nlm.nih.gov.

Pham, B. T. et al. (2020) A comparative study of kernel logistic regression, radial basis function classifier, multinomial naive bayes, and logistic model tree for flash flood susceptibility mapping. Water (Switzerland), 12 (no. 1). https://doi.org/10.3390/w12010239.

Cawley, G. C., & Talbot, N. L. C. (2008) Efficient approximate leave-one-out cross-validation for kernel logistic regression. Machine Learning, 71 (no. 2–3). https://doi.org/10.1007/s10994-008-5055-9.

Tien Bui, D., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13 (no. 2). https://doi.org/10.1007/s10346-015-0557-6.

Cawley, G. C., & Talbot, N. L. C. (2004). Efficient model selection for kernel logistic regression. in Proceedings - International Conference on Pattern Recognition, 2, 439–442. https://doi.org/10.1109/ICPR.2004.1334249.

Article  Google Scholar 

Breneman, J. (2005) Kernel methods for pattern analysis. Technometrics, 47 (no. 2). https://doi.org/10.1198/tech.2005.s264.

Amami, R., Ben Ayed, D., & Ellouze, N. (2012). An empirical comparison of SVM and some supervised learning algorithms for vowel recognition. International Journal of Intelligent Information Processing, 3(no. 1), 63–70. https://doi.org/10.4156/ijiip.vol3.issue1.6.

Article  Google Scholar 

Raza, A., Uddin, J., Almuhaimeed, A., Akbar, S., Zou, Q., & Ahmad, A. (2023) AIPs-SnTCN: predicting anti-inflammatory peptides using fasttext and transformer encoder-based hybrid word embedding with self-normalized temporal convolutional networks. Journal of Chemical Information and Modelling. 63 (no. 21). https://doi.org/10.1021/acs.jcim.3c01563.

Akbar, S., Zou, Q., Raza, A., & Alarfaj, F. K. (2024). iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks. Artif Intell Med, 151, 102860 https://doi.org/10.1016/j.artmed.2024.102860. p. 102860, May.

Article  PubMed  Google Scholar