Abstract
Biological sequence classification is vital in various fields, such as genomics and bioinformatics. The advancement and reduced cost of genomic sequencing have brought the attention of researchers for protein and nucleotide sequence classification. Traditional approaches face limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences, while numerous machine-learning models have been proposed to tackle this challenge. In this work, we propose Hist2Vec, a novel kernel-based embedding generation approach for capturing sequence similarities. Hist2Vec combines the concept of histogram-based kernel matrices and Gaussian kernel functions. It constructs histogram-based representations using the unique k-mers present in the sequences. By leveraging the power of Gaussian kernels, Hist2Vec transforms these representations into high-dimensional feature spaces, preserving important sequence information. Hist2Vec aims to address the limitations of existing methods by capturing sequence similarities in a high-dimensional feature space while providing a robust and efficient framework for classification. We employ kernel Principal Component Analysis (PCA) using standard machine-learning algorithms to generate embedding for efficient classification. Experimental evaluations on protein and nucleotide datasets demonstrate the efficacy of Hist2Vec in achieving high classification accuracy compared to state-of-the-art methods. It outperforms state-of-the-art methods by achieving \(>76\%\) and \(>83\%\) accuracies for DNA and Protein datasets, respectively. Hist2Vec provides a robust framework for biological sequence classification, enabling better classification and promising avenues for further analysis of biological data.
S. Ali, H. Mansoor and P. Chourasia—Equal Contribution
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Ali, S., Bello, B., Chourasia, P., et al.: Pwm2vec: an efficient embedding approach for viral host specification from coronavirus spike sequences. MDPI Biology (2022)
Ali, S., Bello, B., Chourasia, P., et al.: Virus2vec: Viral sequence classification using machine learning. arXiv preprint arXiv:2304.12328 (2023)
Ali, S., Patterson, M.: Spike2vec: An efficient and scalable embedding approach for covid-19 spike sequences. CoRR arXiv:2109.05019 (2021)
Ali, S., Sahoo, B., Khan, M.A., Zelikovsky, A., Khan, I.U., Patterson, M.: Efficient approximate kernel based spike sequence classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022)
Ali, S., Tamkanat-E-Ali, Khan, M.A., Khan, I., Patterson, M., et al.: Effective and scalable clustering of sars-cov-2 sequences. Accepted for publication at “International Conference on Big Data Research (ICBDR)” (2021)
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)
Bokharaeian, B., et al.: Automatic extraction of ranked snp-phenotype associations from text using a bert-lstm-based method. BMC Bioinform. 24(1), 144 (2023)
Bonidia, R.P., Sampaio, L.D., et al.: Feature extraction approaches for biological sequences: a comparative study of mathematical features. Briefings in Bioinform. 22(5), bbab011 (2021)
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., Linial, M.: Proteinbert: a universal deep-learning model of protein sequence and func. Bioinformatics 38(8) (2022)
Chen, J., Li, K., et al.: A survey on applications of artificial intelligence in fighting against covid-19. ACM Comput. Surv. (CSUR) 54(8), 1–32 (2021)
Chourasia, P., Ali, S., Ciccolella, S., Vedova, G.D., Patterson, M.: Reads2vec: Efficient embedding of raw high-throughput sequencing reads data. J. Comput. Biol. 30(4), 469–491 (2023)
Chourasia, P., Ali, S., et al.: Clustering sars-cov-2 variants from raw high-throughput sequencing reads data. In: International Conference on Computational Advances in Bio and Medical Sciences, pp. 133–148. Springer (2021)
Corso, G., et al.: Neural distance embeddings for biological sequences. In: Advances in Neural Information Processing Systems, vol. 34, pp. 18539–18551 (2021)
Farhan, M., Tariq, J., Zaman, A., Shabbir, M., Khan, I.: Efficient approximation algorithms for strings kernel based sequence classification. In: Advances in neural information processing systems (NeurIPS), pp. 6935–6945 (2017)
Gabler, F., Nam, S.Z., et al.: Protein sequence analysis using the mpi bioinformatics toolkit. Curr. Protoc. Bioinformatics 72(1), e108 (2020)
Golestan Hashemi, F.S., et al.: Intelligent mining of large-scale bio-data: bioinformatics applications. Biotech Biotechnol. Equipment 32(1), 10–29 (2018)
Guan, M., Zhao, L., Yau, S.S.T.: Classification of protein sequences by a novel alignment-free method on bacterial and virus families. Genes 13(10), 1744 (2022)
Heinzinger, M., et al.: Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinform. 20(1), 1–17 (2019)
Hsu, C.W., et al.: A practical guide to support vector classification (2003)
Human DNA: https://www.kaggle.com/code/nageshsingh/demystify-dna-sequencing-with-machine-learning/data (2022). Accessed 10 Oct 2022
Khajeh-Saeed, A., Poole, S., Perot, J.B.: Acceleration of the smith-waterman algorithm using single and multiple graphics processors. J. Comput. Phys. 229(11), 4247–4258 (2010)
Khandelwal, M., Kumar Rout, R., Umer, S., Mallik, S., Li, A.: Multifactorial feature extraction and site prognosis model for protein methylation data. Brief. Funct. Genomics 22(1), 20–30 (2023)
Kuzmin, K., et al.: Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone. Biochem. Biophys. Res. Commun. 533, 553–558 (2020)
Leslie, C., Eskin, E., Noble, W.S.: The spectrum kernel: A string kernel for svm protein classification. In: Biocomputing, pp. 564–575 (2001)
Lin, S.W., Ying, K.C., Chen, S.C., Lee, Z.J.: Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst. Appl. 35(4), 1817–1824 (2008)
Lou, H., Schwartz, M., Bruck, J., Farnoud, F.: Evolution of \( k \)-mer frequencies and entropy in duplication and substitution mutation systems. IEEE Trans. Inf. Theory 66(5), 3171–3186 (2019)
Mitchell, A.L., Attwood, T.K., Babbitt, P.C., Blum, M., Bork, P., Bridge, A., Brown, S.D., Chang, H.Y., El-Gebali, S., Fraser, M.I., et al.: Interpro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47(D1), D351–D360 (2019)
Otto, M.P.: Scalable and interpretable kernel methods based on random fourier features (2023)
P. Kuksa, P., Khan, I., Pavlovic, V.: Generalized similarity kernels for efficient sequence classification. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 873–882. SIAM (2012)
Pickett, B.E., Sadat, E.L., Zhang, Y., Noronha, J.M., Squires, R.B., et al.: Vipr: an open bioinformatics database and analysis resource for virology research. Nucleic acids research, pp. D593–D598 (2012)
Qi, R., Guo, F., Zou, Q.: String kernels construction and fusion: a survey with bioinformatics application. Front. Comp. Sci. 16(6), 166904 (2022)
Rao, R., Bhattacharya, N., et al.: Evaluating protein transfer learning with tape. Advances in neural information processing systems 32 (2019)
Roman, I., Santana, R., et al.: In-depth analysis of svm kernel learning and its components. Neural Comput. Appl. 33(12), 6575–6594 (2021)
Saifuddin, K.M., et al.: Seq-hygan: Sequence classification via hypergraph attention network. arXiv preprint arXiv:2303.02393 (2023)
Scholkopf, B., Sung, K.K., et al.: Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45(11), 2758–2765 (1997)
Shen, J., Qu, et al.: Wasserstein distance guided representation learning for domain adaptation. In: AAAI Conference on Artificial Intelligence (2018)
Sikander, R., Ghulam, A., Ali, F.: Xgb-drugpred: computational prediction of druggable proteins using extreme gradient boosting and optimized features set. Sci. Rep. 12(1), 5505 (2022)
Solis-Reyes, S., Avino, M., Poon, A., Kari, L.: An open-source k-mer based machine learning tool for fast and accurate subtyping of hiv-1 genomes. Plos One (2018)
Sun, C., Ai, X., Zhang, Z., Hancock, E.R.: Labeled subgraph entropy kernel. arXiv preprint arXiv:2303.13543 (2023)
Taslim, M., Prakash, C., et al.: Hashing2vec: Fast embedding generation for sars-cov-2 spike sequence classification. In: ACML, pp. 754–769. PMLR (2023)
Vamathevan, J., Clark, et al.: Applications of machine learning in drug discovery and development. Nature Rev. Drug Discovery 18(6), 463–477 (2019)
Wood, D., Salzberg, S.: Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15 (2014)
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478–487 (2016)
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Ali, S., Mansoor, H., Chourasia, P., Patterson, M. (2023). Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification. In: Guo, X., Mangul, S., Patterson, M., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2023. Lecture Notes in Computer Science(), vol 14248. Springer, Singapore. https://doi.org/10.1007/978-981-99-7074-2_30
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