{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:09:25Z","timestamp":1765544965568,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819722402"},{"type":"electronic","value":"9789819722389"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-2238-9_18","type":"book-chapter","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T12:01:48Z","timestamp":1714478508000},"page":"234-245","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Weighted Chaos Game Representation for\u00a0Molecular Sequence Classification"],"prefix":"10.1007","author":[{"given":"Taslim","family":"Murad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarwan","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murray","family":"Patterson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,1]]},"reference":[{"issue":"3","key":"18_CR1","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1017\/S0033583503003901","volume":"36","author":"JC Whisstock","year":"2003","unstructured":"Whisstock, J.C., Lesk, A.M.: Prediction of protein function from protein sequence and structure. Q. Rev. Biophys. 36(3), 307\u2013340 (2003)","journal-title":"Q. Rev. Biophys."},{"issue":"3","key":"18_CR2","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/j.bbrc.2020.09.010","volume":"533","author":"K Kuzmin","year":"2020","unstructured":"Kuzmin, K., et al.: Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone. Biochem. Biophys. Res. Commun. 533(3), 553\u2013558 (2020)","journal-title":"Biochem. Biophys. Res. Commun."},{"issue":"3","key":"18_CR3","doi-asserted-by":"publisher","first-page":"418","DOI":"10.3390\/biology11030418","volume":"11","author":"S Ali","year":"2022","unstructured":"Ali, S., Bello, B., Chourasia, P., Punathil, R.T., Zhou, Y., Patterson, M.: PWM2Vec: an efficient embedding approach for viral host specification from coronavirus spike sequences. Biology. 11(3), 418 (2022)","journal-title":"Biology."},{"issue":"5\u20136","key":"18_CR4","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.ygeno.2017.06.007","volume":"109","author":"B Chowdhury","year":"2017","unstructured":"Chowdhury, B., Garai, G.: A review on multiple sequence alignment from the perspective of genetic algorithm. Genomics 109(5\u20136), 419\u2013431 (2017)","journal-title":"Genomics"},{"issue":"2","key":"18_CR5","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3390\/e22020255","volume":"22","author":"Y Ma","year":"2020","unstructured":"Ma, Y., Yu, Z., Tang, R., Xie, X., Han, G., Anh, V.V.: Phylogenetic analysis of HIV-1 genomes based on the position-weighted K-mers method. Entropy 22(2), 255 (2020)","journal-title":"Entropy"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, J., Bi, C., Wang, Y., Zeng, T., Liao, B., Chen, L.: Efficient mining closed K-mers from DNA and protein sequences. In: International Conference on Big Data and Smart Computing, pp. 342\u2013349 (2020)","DOI":"10.1109\/BigComp48618.2020.00-51"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Ali, S., Patterson, M.: Spike2vec: an efficient and scalable embedding approach for COVID-19 spike sequences. In: IEEE Big Data, pp. 1533\u20131540 (2021)","DOI":"10.1109\/BigData52589.2021.9671848"},{"issue":"8","key":"18_CR8","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1093\/nar\/18.8.2163","volume":"18","author":"HJ Jeffrey","year":"1990","unstructured":"Jeffrey, H.J.: Chaos game representation of gene structure. Nucleic Acids Res. 18(8), 2163\u20132170 (1990)","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"18_CR9","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1093\/bioinformatics\/btz493","volume":"36","author":"HF L\u00f6chel","year":"2020","unstructured":"L\u00f6chel, H.F., Eger, D., Sperlea, T., Heider, D.: Deep learning on chaos game representation for proteins. Bioinformatics 36(1), 272\u2013279 (2020)","journal-title":"Bioinformatics"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Shen, J., Qu, Y., Zhang, W., Yu, Y.: Wasserstein distance guided representation learning for domain adaptation. In: AAAI Conference (2018)","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"18_CR11","unstructured":"Farhan, M., et\u00a0al.: Efficient approximation algorithms for strings kernel based sequence classification. In: NeurIPS, pp. 6935\u20136945 (2017)"},{"key":"18_CR12","unstructured":"Barnsley, M.F.: Fractals everywhere: New edition (2012)"},{"key":"18_CR13","unstructured":"Tzanov, V.: Strictly self-similar fractals composed of star-polygons that are attractors of iterated function systems. arXiv preprint arXiv:1502.01384 (2015)"},{"issue":"1","key":"18_CR14","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/0022-2836(82)90515-0","volume":"157","author":"J Kyte","year":"1982","unstructured":"Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Bio. 157(1), 105\u2013132 (1982)","journal-title":"J. Mol. Bio."},{"issue":"1","key":"18_CR15","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1146\/annurev.bi.53.070184.003115","volume":"53","author":"D Eisenberg","year":"1984","unstructured":"Eisenberg, D.: Three-dimensional structure of membrane and surface proteins. Annu. Rev. Biochem. 53(1), 595\u2013623 (1984)","journal-title":"Annu. Rev. Biochem."},{"issue":"6","key":"18_CR16","doi-asserted-by":"publisher","first-page":"3824","DOI":"10.1073\/pnas.78.6.3824","volume":"78","author":"TP Hopp","year":"1981","unstructured":"Hopp, T.P., Woods, K.R.: Prediction of protein antigenic determinants from amino acid sequences. PNAS 78(6), 3824\u20133828 (1981)","journal-title":"PNAS"},{"issue":"12","key":"18_CR17","first-page":"2577","volume":"22","author":"W Kabsch","year":"1983","unstructured":"Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolym. Orig. Res. Biomol. 22(12), 2577\u20132637 (1983)","journal-title":"Biopolym. Orig. Res. Biomol."},{"issue":"12","key":"18_CR18","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.tibs.2011.08.003","volume":"36","author":"JL MacCallum","year":"2011","unstructured":"MacCallum, J.L., Tieleman, D.P.: Hydrophobicity scales: a thermodynamic looking glass into lipid-protein interactions. Trends Biochem. Sci. 36(12), 653\u2013662 (2011)","journal-title":"Trends Biochem. Sci."},{"key":"18_CR19","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"18_CR20","unstructured":"O\u2019Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015)"},{"key":"18_CR21","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114 (2019)"},{"key":"18_CR22","unstructured":"Hassan, Z.: 3 pre-trained image classification models (2022). https:\/\/www.folio3.ai\/blog\/image-classification-models\/"},{"issue":"5","key":"18_CR23","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1010023","volume":"18","author":"K Campbell","year":"2022","unstructured":"Campbell, K., et al.: Making genomic surveillance deliver: A lineage classification and nomenclature system to inform rabies elimination. PLoS Pathog. 18(5), e1010023 (2022)","journal-title":"PLoS Pathog."},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Ali, S., Murad, T., Patterson, M.: PSSM2Vec: a compact alignment-free embedding approach for coronavirus spike sequence classification. In: Neural Information Processing (ICONIP), pp. 420\u2013432 (2023)","DOI":"10.1007\/978-981-99-1648-1_35"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2238-9_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T12:08:20Z","timestamp":1714478900000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2238-9_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819722402","9789819722389"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2238-9_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taipei","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiwan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}