{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T07:08:37Z","timestamp":1776064117375,"version":"3.50.1"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10489-023-04618-0","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T15:02:36Z","timestamp":1686927756000},"page":"21920-21943","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Using alignment-free and pattern mining methods for SARS-CoV-2 genome analysis"],"prefix":"10.1007","volume":"53","author":[{"given":"M. Saqib","family":"Nawaz","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7680-9899","authenticated-orcid":false,"given":"Philippe","family":"Fournier-Viger","sequence":"additional","affiliation":[]},{"given":"Memoona","family":"Aslam","sequence":"additional","affiliation":[]},{"given":"Wenjin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yulin","family":"He","sequence":"additional","affiliation":[]},{"given":"Xinzheng","family":"Niu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"4618_CR1","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1038\/s41586-020-2008-3","volume":"579","author":"F Wu","year":"2020","unstructured":"Wu F et al (2020) A new coronavirus associated with human respiratory disease in China. Nature 579:265\u2013269","journal-title":"Nature"},{"key":"4618_CR2","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1038\/s41564-020-0695-z","volume":"5","author":"Coronaviridae Study Group of the International Committee on Taxonomy of Viruses","year":"2020","unstructured":"Coronaviridae Study Group of the International Committee on Taxonomy of Viruses (2020) The species Severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 5:536\u2013544","journal-title":"Nat Microbiol"},{"key":"4618_CR3","volume-title":"Bioinformatics: Sequence and Genome Analysis","author":"DM Mount","year":"2004","unstructured":"Mount DM (2004) Bioinformatics: Sequence and Genome Analysis, 2nd edn. Cold Spring Harbor Laboratory Press","edition":"2"},{"key":"4618_CR4","doi-asserted-by":"crossref","unstructured":"Aggarwal C, Bhuiyan M, Hasan M (2014) Frequent pattern mining algorithms: A survey. In: Frequent Pattern Mining, Springer","DOI":"10.1007\/978-3-319-07821-2"},{"key":"4618_CR5","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1186\/s13059-017-1319-7","volume":"18","author":"A Zielezinski","year":"2017","unstructured":"Zielezinski A et al (2017) Alignment-free sequence comparison: Benefits, applications, and tools. Genome Biol 18:186","journal-title":"Genome Biol"},{"issue":"3","key":"4618_CR6","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1093\/bib\/bbt068","volume":"15","author":"S Vinga","year":"2014","unstructured":"Vinga S (2014) Information theory applications for biological sequence analysis. Brief Bioninf 15(3):376\u2013389","journal-title":"Brief Bioninf"},{"key":"4618_CR7","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1093\/bioinformatics\/btg005","volume":"19","author":"S Vinga","year":"2003","unstructured":"Vinga S, Almeida J (2003) Alignment-free sequence comparison- A review. Bioinformatics 19:513\u2013523","journal-title":"Bioinformatics"},{"key":"4618_CR8","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/s13059-019-1755-7","volume":"20","author":"A Zielezinski","year":"2019","unstructured":"Zielezinski A et al (2019) Benchmarking of alignment-free sequence comparison methods. Genome Biol 20:144","journal-title":"Genome Biol"},{"key":"4618_CR9","first-page":"54","volume":"1","author":"P Fournier-Viger","year":"2017","unstructured":"Fournier-Viger P et al (2017) A survey of sequential pattern mining. Data Sci Patt Recog 1:54\u201377","journal-title":"Data Sci Patt Recog"},{"issue":"1","key":"4618_CR10","doi-asserted-by":"crossref","first-page":"51","DOI":"10.5808\/GI.2012.10.1.51","volume":"10","author":"MR Karim","year":"2013","unstructured":"Karim MR et al (2013) An efficient approach to mining maximal contiguous frequent patterns from large DNA sequence databases. Genomics Informat 10(1):51\u201357","journal-title":"Genomics Informat"},{"key":"4618_CR11","first-page":"144","volume":"2","author":"DR Kawade","year":"2013","unstructured":"Kawade DR, Oza KS (2013) Exploration of DNA sequences using pattern mining. J Biomed Informa 2:144\u2013148","journal-title":"J Biomed Informa"},{"issue":"5","key":"4618_CR12","doi-asserted-by":"crossref","first-page":"3086","DOI":"10.1007\/s10489-021-02193-w","volume":"51","author":"MS Nawaz","year":"2021","unstructured":"Nawaz MS, Fournier-Viger P, Shojaee A, Fujita H (2021) Using artificial intelligence techniques for COVID-19 genome analysis. Appl Intell 51(5):3086\u20133103","journal-title":"Appl Intell"},{"key":"4618_CR13","doi-asserted-by":"crossref","unstructured":"Ni L et al (2020) Mining the local dependency itemset in a products network. ACM Trans Manage Infor Syst 11 (1): 3:1-3:31","DOI":"10.1145\/3384473"},{"issue":"2","key":"4618_CR14","first-page":"26","volume":"1","author":"RU Mustafa","year":"2017","unstructured":"Mustafa RU et al (2017) Early detection of controversial urdu speeches from social media. Data Scie Patt Recogn 1(2):26\u201342","journal-title":"Data Scie Patt Recogn"},{"key":"4618_CR15","doi-asserted-by":"crossref","unstructured":"Pokou YJM, Fournier-Viger P, Moghrabi C (2016) Authorship attribution using small sets of frequent part-of-speech skip-grams. In: Proceedings of FLAIRS, pp. 86-91","DOI":"10.5220\/0005710103540361"},{"key":"4618_CR16","doi-asserted-by":"crossref","first-page":"119806","DOI":"10.1109\/ACCESS.2020.3004199","volume":"8","author":"MS Nawaz","year":"2020","unstructured":"Nawaz MS, Fournier-Viger P, Zhang J (2020) Proof learning in PVS with utility pattern mining. IEEE Access 8:119806\u2013119818","journal-title":"IEEE Access"},{"key":"4618_CR17","doi-asserted-by":"crossref","unstructured":"Nawaz MS, Sun M, Fournier-Viger P (2019). Proof guidance in PVS with sequential pattern mining. In: Proceedings of FSEN, pp. 45-60","DOI":"10.1007\/978-3-030-31517-7_4"},{"key":"4618_CR18","doi-asserted-by":"crossref","unstructured":"Schweizer D et al (2015) Using consumer behavior data to reduce energy consumption in smarthomes: Applying machine learning to save energy without lowering comfort of inhabitants. In: Proceedings of ICMLA, pp. 1123-1129","DOI":"10.1109\/ICMLA.2015.62"},{"key":"4618_CR19","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2022.102741","volume":"118","author":"MS Nawaz","year":"2022","unstructured":"Nawaz MS et al (2022) MalSPM: Metamorphic malware behavior analysis and classification using sequential pattern mining. Computers & Security 118:102741","journal-title":"Computers & Security"},{"key":"4618_CR20","doi-asserted-by":"crossref","unstructured":"Fournier-Viger P, Gueniche T, Tseng VS (2012). Using partially-ordered sequential rules to generate more accurate sequence prediction. In: Proceedings of ADMA, pp. 431-442","DOI":"10.1007\/978-3-642-35527-1_36"},{"key":"4618_CR21","doi-asserted-by":"crossref","unstructured":"Nawaz MS et al (2021) COVID-19 genome analysis using alignment-free methods. In: Proceedings of IEA AIE, pp. 316-328","DOI":"10.1007\/978-3-030-79457-6_28"},{"key":"4618_CR22","doi-asserted-by":"crossref","unstructured":"Rondo HM et al (2021) Pathogenesis, symptomatology, and transmission of SARS-CoV-2 through analysis of viral Genomics and structure. mSystems 6(5): e00095-21","DOI":"10.1128\/msystems.00095-21"},{"key":"4618_CR23","doi-asserted-by":"crossref","unstructured":"Nawaz MS, Fournier-Viger, P, He Y (2022) S-PDB: Analysis and classification of SARS-CoV-2 Spike protein structures. In: Proceedings of BIBM, pp. 2259-2265","DOI":"10.1109\/BIBM55620.2022.9995562"},{"key":"4618_CR24","doi-asserted-by":"crossref","DOI":"10.1016\/j.genrep.2020.100682","volume":"19","author":"RA Khailany","year":"2020","unstructured":"Khailany RA, Safdar M, Ozaslanc M (2020) Genomic characterization of a novel SARS-CoV-2. Gene Reports 19:100682","journal-title":"Gene Reports"},{"key":"4618_CR25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.biosystems.2016.11.004","volume":"151","author":"J-J Shu","year":"2017","unstructured":"Shu J-J (2017) A new integrated symmetrical table for genetic codes. Biosystems 151:21\u201326","journal-title":"Biosystems"},{"key":"4618_CR26","doi-asserted-by":"crossref","first-page":"3913","DOI":"10.1007\/s10489-020-01770-9","volume":"50","author":"Y Mohamadou","year":"2020","unstructured":"Mohamadou Y, Halidou A, Kapen PT (2020) A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19. Appl Intell 50:3913\u20133925","journal-title":"Appl Intell"},{"key":"4618_CR27","doi-asserted-by":"crossref","first-page":"2908","DOI":"10.1007\/s10489-020-02102-7","volume":"51","author":"J Nayak","year":"2021","unstructured":"Nayak J et al (2021) Intelligent system for COVID-19 prognosis: A state-of-the-art survey. Appl Intell 51:2908\u20132938","journal-title":"Appl Intell"},{"key":"4618_CR28","doi-asserted-by":"crossref","unstructured":"Alyasseri Z et al (2021) Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. Expert Systems e12759","DOI":"10.1111\/exsy.12759"},{"key":"4618_CR29","volume":"139","author":"S Lalmuanawma","year":"2020","unstructured":"Lalmuanawma S, Hussain J, Chhakchhuak L (2020) Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solito 139:110059","journal-title":"Chaos Solito"},{"key":"4618_CR30","volume":"22","author":"J Chen","year":"2020","unstructured":"Chen J, See JC (2020) Artificial intelligence for COVID-19: Rapid review. J Med Internet Res 22:e21476","journal-title":"J Med Internet Res"},{"key":"4618_CR31","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s12539-021-00431-w","volume":"13","author":"J Rasheed","year":"2021","unstructured":"Rasheed J et al (2021) COVID-19 in the age of artificial intelligence: A comprehensive review. Interdiscip Sci Comput Life Sci 13:153\u2013175","journal-title":"Interdiscip Sci Comput Life Sci"},{"key":"4618_CR32","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","volume":"21","author":"F Shi","year":"2021","unstructured":"Shi F et al (2021) Review of artificial intelligence techniques in imaging data acquisition, segmenta-tion and diagnosis for COVID-19. IEEE Rev Biomed Engg 21:4\u201315","journal-title":"IEEE Rev Biomed Engg"},{"key":"4618_CR33","doi-asserted-by":"crossref","unstructured":"Driggs D et al (2021) Machine Learning for COVID-19 diagnosis and prognostication: Lessons for amplifying the signal while reducing the noise. Radiology: Artificial Intelligence 3(4): e210011","DOI":"10.1148\/ryai.2021210011"},{"key":"4618_CR34","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","volume":"3","author":"M Roberts","year":"2021","unstructured":"Roberts M et al (2021) Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 3:199\u2013217","journal-title":"Nat Mach Intell"},{"key":"4618_CR35","volume":"369","author":"L Wynants","year":"2020","unstructured":"Wynants L et al (2020) Prediction models for diagnosis and prognosis of COVID-19: Systematic review and critical appraisal. BMJ 369:m1328","journal-title":"BMJ"},{"issue":"5","key":"4618_CR36","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1108\/K-05-2020-0258","volume":"50","author":"S Noor","year":"2020","unstructured":"Noor S et al (2020) Analysis of public reactions to the novel coronavirus (COVID-19) outbreak on Twitter. Kybernetes 50(5):1633\u20131653","journal-title":"Kybernetes"},{"key":"4618_CR37","doi-asserted-by":"crossref","unstructured":"Heng JW, Juwono FH, Reine R (2021) Using optimal sequencing algorithms for COVID-19 case study. In: Proceedings GECOST, pp. 1-4","DOI":"10.1109\/GECOST52368.2021.9538762"},{"key":"4618_CR38","volume":"138","author":"RK Pathan","year":"2020","unstructured":"Pathan RK, Biswas M, Khandaker MU (2020) Time series prediction of COVID19 by mutation rate analysis using recurrent neural network-based LSTM model. Chaos Solit 138:110018","journal-title":"Chaos Solit"},{"key":"4618_CR39","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104981","volume":"139","author":"M Zelenova","year":"2021","unstructured":"Zelenova M (2021) Analysis of 329,942 SARS-CoV-2 records retrieved from GISAID database. Comput Biol Med 139:104981","journal-title":"Comput Biol Med"},{"key":"4618_CR40","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1038\/s41587-021-01040-0","volume":"39","author":"K Kali","year":"2021","unstructured":"Kali K (2021) The lag in SARS-CoV-2 genome submissions to GISAID. Nat Biotechnol 39:1058\u20131060","journal-title":"Nat Biotechnol"},{"key":"4618_CR41","doi-asserted-by":"crossref","unstructured":"Arslan H (2021) Machine learning methods for COVID-19 prediction using human genomic data. Proceedings 74(1), 20","DOI":"10.3390\/proceedings2021074020"},{"issue":"4","key":"4618_CR42","first-page":"839","volume":"24","author":"H Arslan","year":"2021","unstructured":"Arslan H, Arslan H (2021) A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. Int J Eng Sci Technol 24(4):839\u2013847","journal-title":"Int J Eng Sci Technol"},{"key":"4618_CR43","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2021.107666","volume":"161","author":"H Arslan","year":"2021","unstructured":"Arslan H (2021) COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus. Comput Ind Eng 161:107666","journal-title":"Comput Ind Eng"},{"key":"4618_CR44","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1038\/s41598-020-80363-5","volume":"11","author":"Lopez-Rincon","year":"2021","unstructured":"Lopez-Rincon et al (2021) Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning. Scient Rep 11:947","journal-title":"Scient Rep"},{"issue":"2","key":"4618_CR45","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1093\/bib\/bbaa170","volume":"22","author":"SM Naeem","year":"2021","unstructured":"Naeem SM (2021) A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19. Brief Bioinf 22(2):1197\u20131205","journal-title":"Brief Bioinf"},{"issue":"4","key":"4618_CR46","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0232391","volume":"15","author":"GS Randhawa","year":"2020","unstructured":"Randhawa GS et al (2020) Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One 15(4):e0232391","journal-title":"PLoS One"},{"key":"4618_CR47","first-page":"1","volume":"6","author":"I Ahmed","year":"2021","unstructured":"Ahmed I, Jeon G (2021) Enabling artificial intelligence for genome sequence analysis of COVID-19 and alike viruses. Interdiscip Sci 6:1\u201316","journal-title":"Interdiscip Sci"},{"key":"4618_CR48","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1146\/annurev-biodatasci-080917-013431","volume":"1","author":"J Ren","year":"2018","unstructured":"Ren J et al (2018) Alignment free sequence analysis and applications. Annu Rev Biomed Sci 1:93\u2013114","journal-title":"Annu Rev Biomed Sci"},{"issue":"6","key":"4618_CR49","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1093\/bib\/bbt052","volume":"15","author":"O Bonham-Carter","year":"2014","unstructured":"Bonham-Carter O et al (2014) Alignment-free genetic sequence comparisons: A review of recent approaches by word analysis. Brief Bioinf 15(6):890\u2013905","journal-title":"Brief Bioinf"},{"issue":"3","key":"4618_CR50","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1093\/bib\/bbt067","volume":"15","author":"J Song","year":"2014","unstructured":"Song J et al (2014) New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing. Brief Bioinf 15(3):343\u2013353","journal-title":"Brief Bioinf"},{"key":"4618_CR51","doi-asserted-by":"crossref","unstructured":"Lu YY et al (2017) CAFE: aCcelerated Alignment-FrEe sequence analysis. Nucleic Acids Res 45(Web Server issue): W554-W559","DOI":"10.1093\/nar\/gkx351"},{"key":"4618_CR52","volume-title":"International Encyclopedia of Statistical Science","author":"A Frigessi","year":"2011","unstructured":"Frigessi A, Heidergott B (2011) Markov Chains. In: Lovric M (ed) International Encyclopedia of Statistical Science. Springer"},{"issue":"1","key":"4618_CR53","doi-asserted-by":"crossref","first-page":"2122","DOI":"10.1093\/bioinformatics\/btg295","volume":"19","author":"HH Otu","year":"2003","unstructured":"Otu HH, Sayood KA (2003) A new sequence distance measure for phylogenetic tree construction. Bioinformatics 19(1):2122\u20132130","journal-title":"Bioinformatics"},{"issue":"12","key":"4618_CR54","doi-asserted-by":"crossref","first-page":"3250","DOI":"10.1109\/TIT.2004.838101","volume":"50","author":"M Li","year":"2004","unstructured":"Li M et al (2004) The similarity metric. IEEE Trans Infor Theory 50(12):3250\u201364","journal-title":"IEEE Trans Infor Theory"},{"issue":"3","key":"4618_CR55","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1093\/bib\/bbt088","volume":"15","author":"R Giancarlo","year":"2014","unstructured":"Giancarlo R, Rombo SE, Utro F (2014) Compressive biological sequence analysis and archival in the era of high-throughput sequencing technologies. Brief Bioinf 15(3):390\u2013406","journal-title":"Brief Bioinf"},{"issue":"D1","key":"4618_CR56","first-page":"D84","volume":"48","author":"EW Sayers","year":"2019","unstructured":"Sayers EW et al (2019) Genbank. Nucleic Acids Res 48(D1):D84\u2013D86","journal-title":"Nucleic Acids Res"},{"key":"4618_CR57","doi-asserted-by":"crossref","unstructured":"Fournier-Viger P et al (2016). The SPMF open-source data mining library version 2. In: Proceedings ECML PKDD, pp. 36-40","DOI":"10.1007\/978-3-319-46131-1_8"},{"key":"4618_CR58","doi-asserted-by":"crossref","unstructured":"Ayres J (2002). Sequential pattern mining using a bitmap representation. In: Proceedings KDD, pp. 429-435","DOI":"10.1145\/775047.775109"},{"key":"4618_CR59","doi-asserted-by":"crossref","unstructured":"Fournier-Viger P et al (2013) TKS: Efficient mining of top-k sequential patterns. In: Proceedings of Advanced Data Mining and Applications (ADMA), pp. 109-120","DOI":"10.1007\/978-3-642-53914-5_10"},{"key":"4618_CR60","doi-asserted-by":"crossref","unstructured":"Fournier-Viger P (2014). Fast vertical mining of sequential patterns using co-occurrence information. In: Proceedings of PAKDD, pp. 40-52","DOI":"10.1007\/978-3-319-06608-0_4"},{"key":"4618_CR61","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-07821-2","volume-title":"Frequent Pattern Mining","author":"CC Aggarwal","year":"2014","unstructured":"Aggarwal CC, Han J (2014) Frequent Pattern Mining. Springer"},{"key":"4618_CR62","unstructured":"Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings VLDB, pp. 487-499"},{"key":"4618_CR63","doi-asserted-by":"crossref","unstructured":"Fournier-Viger P (2014). ERMiner: Sequential rule mining using equivalence classes. In: Proceedings of IDA, pp. 108-119","DOI":"10.1007\/978-3-319-12571-8_10"},{"key":"4618_CR64","doi-asserted-by":"crossref","unstructured":"Gueniche T et al (2015) CPT+: Decreasing the time\/space complexity of the compact prediction tree. In: Proceedings of PAKDD, pp. 625-636","DOI":"10.1007\/978-3-319-18032-8_49"},{"key":"4618_CR65","doi-asserted-by":"crossref","unstructured":"Gueniche T, Fournier-Viger P, Tseng VS (2013). Compact prediction tree: A lossless model for accurate sequence prediction. In: Proceedings of AADMA, pp. 177-188","DOI":"10.1007\/978-3-642-53917-6_16"},{"key":"4618_CR66","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/235160.235164","volume":"26","author":"VN Padmanabhan","year":"1996","unstructured":"Padmanabhan VN, Mogul JC (1996) Using predictive prefetching to improve world wide web latency. Comp Comm Rev 26:22\u201336","journal-title":"Comp Comm Rev"},{"key":"4618_CR67","unstructured":"Pitkow J, Pirolli P (1999) Mining longest repeating subsequence to predict world wide web surfing. In: Proceedings of USENIX Symposium on Internet Technologies and Systems, pp. 13-25"},{"key":"4618_CR68","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1145\/990301.990304","volume":"4","author":"M Deshpande","year":"2004","unstructured":"Deshpande M, Karypis G (2004) Selective markov models for predicting web page accesses. ACM Trans. Inter. Techn. 4:163\u2013184","journal-title":"ACM Trans. Inter. Techn."},{"key":"4618_CR69","first-page":"43","volume":"15","author":"P Laird","year":"1994","unstructured":"Laird P, Saul R (1994) Discrete sequence prediction and its applications. Machine Learning 15:43\u201368","journal-title":"Machine Learning"},{"key":"4618_CR70","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/TIT.1978.1055934","volume":"24","author":"J Ziv","year":"1978","unstructured":"Ziv J, Lempel A (1978) Compression of individual sequences via variable-rate coding. IEEE Trans. Infor. Theory. 24:530\u2013536","journal-title":"IEEE Trans. Infor. Theory."},{"issue":"3","key":"4618_CR71","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","volume":"215","author":"SF Altschul","year":"1990","unstructured":"Altschul SF et al (1990) Basic local alignment search tool. J. Molec. Biolo. 215(3):403\u2013410","journal-title":"J. Molec. Biolo."},{"issue":"6","key":"4618_CR72","doi-asserted-by":"crossref","first-page":"637","DOI":"10.3390\/genes11060637","volume":"11","author":"Dong","year":"2020","unstructured":"Dong et al (2020) Analysis of the hosts and transmission paths of SARS-CoV-2 in the COVID-19 outbreak. Genes 11(6):637","journal-title":"Genes"},{"key":"4618_CR73","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1186\/s12967-020-02344-6","volume":"18","author":"M Pachetti","year":"2020","unstructured":"Pachetti M et al (2020) Emerging SARS-CoV-2 mutation hot spots include a novel RNA-dependent-RNA polymerase variant. J. Transl. Medi. 18:179","journal-title":"J. Transl. Medi."},{"key":"4618_CR74","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-98140-6","volume-title":"Supervised Descriptive Pattern Mining","author":"S Ventura","year":"2018","unstructured":"Ventura S, Luna JM (2018) Supervised Descriptive Pattern Mining. Springer"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04618-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04618-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04618-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T13:17:00Z","timestamp":1697635020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04618-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,16]]},"references-count":74,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["4618"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04618-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,16]]},"assertion":[{"value":"7 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare no conflict on interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}