{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T09:09:30Z","timestamp":1793610570789,"version":"4.5.0"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"HEC fellowship"},{"name":"MBD fellowship"},{"name":"GSU\/Computer Science startup"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, such as viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become global pandemics. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, such as the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on generative adversarial networks (GANs), which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles real data, thus, these generated data can be employed to enhance the ML models\u2019 performance by eradicating the class imbalance problem for biological sequence analysis. We perform four distinct classification tasks by using four different sequence datasets (Influenza A Virus, PALMdb, VDjDB, Host) and our results illustrate that GANs can improve the overall classification performance.<\/jats:p>","DOI":"10.3390\/biology12060854","type":"journal-article","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T02:28:56Z","timestamp":1686796136000},"page":"854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Exploring the Potential of GANs in Biological Sequence Analysis"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6434-3297","authenticated-orcid":false,"given":"Taslim","family":"Murad","sequence":"first","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8121-2168","authenticated-orcid":false,"given":"Sarwan","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4329-0234","authenticated-orcid":false,"given":"Murray","family":"Patterson","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6263","DOI":"10.1021\/jm300455c","article-title":"Antivirals targeting influenza A virus","volume":"55","author":"Das","year":"2012","journal-title":"J. Med. Chem."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1172\/JCI137647","article-title":"SARS-CoV-2: A storm is raging","volume":"130","author":"Pedersen","year":"2020","journal-title":"J. Clin. Investig."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/sj.bjp.0707307","article-title":"Chemogenomic approaches to rational drug design","volume":"152","author":"Rognan","year":"2007","journal-title":"Br. J. Pharmacol."},{"key":"ref_4","unstructured":"Dong, G., and Pei, J. (2007). Sequence Data Mining, Springer Science & Business Media."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1208\/s12248-020-00532-2","article-title":"Recent Developments on Therapeutic and Diagnostic Approaches for COVID-19","volume":"23","author":"Majumder","year":"2021","journal-title":"AAPS J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e14055","DOI":"10.7717\/peerj.14055","article-title":"Ribovirus classification by a polymerase barcode sequence","volume":"10","author":"Babaian","year":"2022","journal-title":"PeerJ"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4121","DOI":"10.1093\/bioinformatics\/bty407","article-title":"Nextstrain: Real-time tracking of pathogen evolution","volume":"34","author":"Hadfield","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1093\/molbev\/msaa015","article-title":"IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era","volume":"37","author":"Minh","year":"2020","journal-title":"Mol. Biol. Evol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1012828","DOI":"10.3389\/fgene.2022.1012828","article-title":"ISTRF: Identification of sucrose transporter using random forest","volume":"13","author":"Chen","year":"2022","journal-title":"Front. Genet."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1011659","DOI":"10.3389\/fgene.2022.1011659","article-title":"Identification of protein\u2013protein interaction associated functions based on gene ontology and KEGG pathway","volume":"13","author":"Yang","year":"2022","journal-title":"Front. Genet."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1121694","DOI":"10.3389\/fgene.2023.1121694","article-title":"PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites","volume":"14","author":"Zhang","year":"2023","journal-title":"Front. Genet."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.bbrc.2020.09.010","article-title":"Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone","volume":"533","author":"Kuzmin","year":"2020","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ma, Y., Yu, Z., Tang, R., Xie, X., Han, G., and Anh, V.V. (2020). Phylogenetic analysis of HIV-1 genomes based on the position-weighted k-mers method. Entropy, 22.","DOI":"10.3390\/e22020255"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ghandi, M., Lee, D., Mohammad-Noori, M., and Beer, M.A. (2014). Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput. Biol., 10.","DOI":"10.1371\/journal.pcbi.1003711"},{"key":"ref_15","first-page":"11784","article-title":"Wasserstein distance guided representation learning for domain adaptation","volume":"32","author":"Shen","year":"2018","journal-title":"Proc. Conf. AAAI Artif. Intell."},{"key":"ref_16","unstructured":"Xie, J., Girshick, R., and Farhadi, A. (2016). Unsupervised deep embedding for clustering analysis. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., and Rost, B. (2019). Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-3220-8"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1093\/bioinformatics\/btaa003","article-title":"UDSMProt: Universal deep sequence models for protein classification","volume":"36","author":"Strodthoff","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qiao, S., Lu, R., Han, N., Liu, D., and Zhou, J. (2019). How to balance the bioinformatics data: Pseudo-negative sampling. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-3269-4"},{"key":"ref_20","first-page":"332","article-title":"A review of class imbalance problem","volume":"1","author":"Abraham","year":"2013","journal-title":"J. Netw. Innov. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ali, S., and Patterson, M. (2021). Spike2vec: An efficient and scalable embedding approach for covid-19 spike sequences. arXiv.","DOI":"10.1109\/BigData52589.2021.9671848"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ali, S., Bello, B., Chourasia, P., Punathil, R.T., Zhou, Y., and Patterson, M. (2022). PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences. Biology, 11.","DOI":"10.3390\/biology11030418"},{"key":"ref_23","unstructured":"Han, H., Wang, W.Y., and Mao, B.H. (2005). Advances in Intelligent Computing, Proceedings of the International Conference on Intelligent Computing, ICIC 2005, Hefei, China, 23\u201326 August 2005, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.21629\/JSEE.2019.06.12","article-title":"Over-sampling algorithm for imbalanced data classification","volume":"30","author":"Xiaolong","year":"2019","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1002\/prot.21870","article-title":"Protein classification with imbalanced data","volume":"70","author":"Zhao","year":"2008","journal-title":"Proteins Struct. Funct. Bioinform."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tang, B., and He, H. (2015, January 25\u201328). KernelADASYN: Kernel based adaptive synthetic data generation for imbalanced learning. Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan.","DOI":"10.1109\/CEC.2015.7256954"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.eswa.2017.09.030","article-title":"Effective data generation for imbalanced learning using conditional generative adversarial networks","volume":"91","author":"Douzas","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3363","DOI":"10.1093\/bioinformatics\/bth408","article-title":"Reducing storage requirements for biological sequence comparison","volume":"20","author":"Roberts","year":"2004","journal-title":"Bioinformatics"},{"key":"ref_29","unstructured":"(2023, April 20). Bacterial and Viral Bioinformatics Resource Center. Available online: https:\/\/www.bv-brc.org\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1038\/s41586-021-04332-2","article-title":"Petabase-scale sequence alignment catalyses viral discovery","volume":"602","author":"Edgar","year":"2022","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"D1057","DOI":"10.1093\/nar\/gkz874","article-title":"VDJdb in 2019: Database extension, new analysis infrastructure and a T-cell receptor motif compendium","volume":"48","author":"Bagaev","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"D593","DOI":"10.1093\/nar\/gkr859","article-title":"ViPR: An open bioinformatics database and analysis resource for virology research","volume":"40","author":"Pickett","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"ref_33","unstructured":"(2021, December 29). GISAID Website. Available online: https:\/\/www.gisaid.org\/."},{"key":"ref_34","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-7737\/12\/6\/854\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:54:40Z","timestamp":1760126080000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-7737\/12\/6\/854"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,14]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["biology12060854"],"URL":"https:\/\/doi.org\/10.3390\/biology12060854","relation":{},"ISSN":["2079-7737"],"issn-type":[{"value":"2079-7737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,14]]}}}