{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:36:22Z","timestamp":1772120182776,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"CABin grant","award":["F. no. Agril. Edn.4\u20131\/2013A&P"],"award-info":[{"award-number":["F. no. Agril. Edn.4\u20131\/2013A&P"]}]},{"name":"CABin grant","award":["F. no. Agril. Edn.4\u20131\/2013A&P"],"award-info":[{"award-number":["F. no. Agril. Edn.4\u20131\/2013A&P"]}]},{"name":"CABin grant","award":["F. no. Agril. Edn.4\u20131\/2013A&P"],"award-info":[{"award-number":["F. no. Agril. Edn.4\u20131\/2013A&P"]}]},{"name":"CABin grant","award":["F. no. Agril. Edn.4\u20131\/2013A&P"],"award-info":[{"award-number":["F. no. Agril. Edn.4\u20131\/2013A&P"]}]},{"name":"CABin grant","award":["F. no. Agril. Edn.4\u20131\/2013A&P"],"award-info":[{"award-number":["F. no. Agril. Edn.4\u20131\/2013A&P"]}]},{"name":"CABin grant","award":["F. no. Agril. Edn.4\u20131\/2013A&P"],"award-info":[{"award-number":["F. no. Agril. Edn.4\u20131\/2013A&P"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s41060-025-00757-4","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T07:49:39Z","timestamp":1743407379000},"page":"4865-4878","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Genome-wide identification of plant-based natural antimicrobial peptides using deep learning approach in black pepper"],"prefix":"10.1007","volume":"20","author":[{"given":"Ankita","family":"Negi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kalpana","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bulbul","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarika","family":"Jaiswal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mir Asif","family":"Iquebal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"U. B.","family":"Angadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anil","family":"Rai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinesh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"757_CR1","doi-asserted-by":"crossref","first-page":"11387","DOI":"10.3389\/bjbs.2023.11387","volume":"80","author":"KW Tang","year":"2023","unstructured":"Tang, K.W., Millar, B.C., Moore, J.E.: Antimicrobial resistance (AMR). Br. J. Biomed. Sci. 80, 11387 (2023)","journal-title":"Br. J. Biomed. Sci."},{"key":"757_CR2","first-page":"445","volume":"95","author":"IE Mba","year":"2022","unstructured":"Mba, I.E., Nweze, E.I.: Antimicrobial peptides therapy: an emerging alternative for treating drug-resistant bacteria. Yale J. Biol. Med. 95, 445\u2013463 (2022)","journal-title":"Yale J. Biol. Med."},{"issue":"12","key":"757_CR3","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.3390\/molecules27123703","volume":"27","author":"OO Bakare","year":"2022","unstructured":"Bakare, O.O., Gokul, A., Fadaka, A.O., Wu, R., Niekerk, L.-A., Barker, A.M., Keyster, M., Klein, A.: Plant antimicrobial peptides (PAMPs): features, applications, production, expression, and challenges. Molecules 27(12), 3703 (2022)","journal-title":"Molecules"},{"key":"757_CR4","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1002\/ptr.6823","volume":"35","author":"S Srivastava","year":"2021","unstructured":"Srivastava, S., Dashora, K., Ameta, K.L., Singh, N.P., El-Enshasy, H.A., Pagano, M.C., Hesham, A.E., Sharma, G.D., Sharma, M., Bhargava, A.: Cysteine-rich antimicrobial peptides from plants: the future of antimicrobial therapy. Phytother. Res. 35, 256\u2013277 (2021)","journal-title":"Phytother. Res."},{"key":"757_CR5","doi-asserted-by":"crossref","first-page":"8196","DOI":"10.1007\/s13197-015-1914-0","volume":"52","author":"L Zou","year":"2015","unstructured":"Zou, L., Hu, Y.Y., Chen, W.X.: Antibacterial mechanism and activities of black pepper chloroform extract. J. Food Sci. Technol. 52, 8196\u20138203 (2015)","journal-title":"J. Food Sci. Technol."},{"key":"757_CR6","first-page":"455","volume":"2000","author":"KK Vijayan","year":"2000","unstructured":"Vijayan, K.K., Thampuran, R.A.: Pharmacology, toxicology and clinical application of black pepper. Black Pepper. 2000, 455\u201366 (2000)","journal-title":"Black Pepper."},{"key":"757_CR7","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1080\/09637480500450248","volume":"56","author":"I G\u00fcl\u00e7in","year":"2005","unstructured":"G\u00fcl\u00e7in, I.: The antioxidant and radical scavenging activities of black pepper (Piper nigrum) seeds. Int. J. Food Sci. Nutr. 56, 491\u2013499 (2005)","journal-title":"Int. J. Food Sci. Nutr."},{"key":"757_CR8","volume-title":"Antimicrobial resistance: global report on surveillance","author":"World Health Organization","year":"2014","unstructured":"World Health Organization: Antimicrobial resistance: global report on surveillance. World Health Organization, Geneva, Switzerland (2014)"},{"key":"757_CR9","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.omtn.2020.05.006","volume":"20","author":"J Yan","year":"2020","unstructured":"Yan, J., Bhadra, P., Li, A., Sethiya, P., Qin, L., Tai, H.K., Wong, K.H., Siu, S.W.: Deep-AmPEP30: improve short antimicrobial peptides prediction with deep learning. Mol. Therapy-Nucleic Acids. 20, 882\u2013894 (2020)","journal-title":"Mol. Therapy-Nucleic Acids."},{"key":"757_CR10","doi-asserted-by":"crossref","first-page":"221","DOI":"10.3389\/fimmu.2012.00221","volume":"3","author":"B Mishra","year":"2012","unstructured":"Mishra, B., Wang, G.: The importance of amino acid composition in natural AMPs: an evolutional, structural, and functional perspective. Front. Immun. 3, 221 (2012)","journal-title":"Front. Immun."},{"key":"757_CR11","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/nri.2016.29","volume":"16","author":"RE Hancock","year":"2016","unstructured":"Hancock, R.E., Haney, E.F., Gill, E.E.: The immunology of host defence peptides: beyond antimicrobial activity. Nat. Rev. Immunol. 16, 321\u2013334 (2016)","journal-title":"Nat. Rev. Immunol."},{"key":"757_CR12","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1038\/s41422-020-0305-x","volume":"30","author":"S Xia","year":"2020","unstructured":"Xia, S., Liu, M., Wang, C., Xu, W., Lan, Q., Feng, S., Qi, F., Bao, L., Du, L., Liu, S., Qin, C.: Inhibition of SARS-CoV-2 (previously 2019-nCoV) infection by a highly potent pan-coronavirus fusion inhibitor targeting its spike protein that harbors a high capacity to mediate membrane fusion. Cell Res. 30, 343\u2013355 (2020)","journal-title":"Cell Res."},{"key":"757_CR13","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/S0140-6736(21)02724-0","volume":"399","author":"CJ Murray","year":"2022","unstructured":"Murray, C.J., Ikuta, K.S., Sharara, F., Swetschinski, L., Aguilar, G.R., Gray, A., Han, C., Bisignano, C., Rao, P., Wool, E., Johnson, S.C.: Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 399, 629\u2013655 (2022)","journal-title":"Lancet."},{"key":"757_CR14","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s12223-013-0280-4","volume":"59","author":"R Nawrot","year":"2014","unstructured":"Nawrot, R., Barylski, J., Nowicki, G., Broniarczyk, J., Buchwald, W., Go\u017adzicka-J\u00f3zefiak, A.: Plant antimicrobial peptides. Folia Microbiol. (Praha) 59, 181\u2013196 (2014)","journal-title":"Folia Microbiol. (Praha)"},{"key":"757_CR15","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.3390\/ijms18091938","volume":"18","author":"J Noonan","year":"2017","unstructured":"Noonan, J., Williams, W.P., Shan, X.: Investigation of antimicrobial peptide genes associated with fungus and insect resistance in maize. Int. J. Mol. Sci. 18, 1938 (2017)","journal-title":"Int. J. Mol. Sci."},{"key":"757_CR16","doi-asserted-by":"crossref","first-page":"5262","DOI":"10.1093\/bioinformatics\/btaa653","volume":"36","author":"LCHW Fingerhut","year":"2020","unstructured":"Fingerhut, L.C.H.W., Miller, D.J., Strugnell, J.M., Daly, N.L., Cooke, I.R.: ampir: an R package for fast genome-wide prediction of antimicrobial peptides. Bioinformatics 36, 5262\u20135263 (2020)","journal-title":"Bioinformatics"},{"key":"757_CR17","first-page":"265","volume":"16","author":"M Abadi","year":"2016","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M.: Tensorflow: a system for large-scale machine learning. InOsdi 16, 265\u2013283 (2016)","journal-title":"InOsdi"},{"key":"757_CR18","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.3389\/fmicb.2020.01669","volume":"11","author":"A Le\u00f3n-Buitimea","year":"2020","unstructured":"Le\u00f3n-Buitimea, A., Garza-C\u00e1rdenas, C.R., Garza-Cervantes, J.A., Lerma-Escalera, J.A., Morones-Ram\u00edrez, J.R.: The demand for new antibiotics: antimicrobial peptides, nanoparticles, and combinatorial therapies as future strategies in antibacterial agent design. Front Microbiol. 11, 1669 (2020)","journal-title":"Front Microbiol."},{"key":"757_CR19","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.3390\/antibiotics10091095","volume":"10","author":"M Rima","year":"2021","unstructured":"Rima, M., Rima, M., Fajloun, Z., Sabatier, J.M., Bechinger, B., Naas, T.: Antimicrobial peptides: a potent alternative to antibiotics. Antibiotics. 10, 1095 (2021)","journal-title":"Antibiotics."},{"key":"757_CR20","doi-asserted-by":"crossref","first-page":"5460","DOI":"10.3390\/ijms21155460","volume":"21","author":"IK Hansen","year":"2020","unstructured":"Hansen, I.K., L\u00f6vdahl, T., Simonovic, D., Hansen, K.\u00d8., Andersen, A.J., Devold, H., Richard, C.S., Andersen, J.H., Str\u00f8m, M.B., Haug, T.: Antimicrobial activity of small synthetic peptides based on the marine peptide turgencin A: prediction of antimicrobial peptide sequences in a natural peptide and strategy for optimization of potency. Int. J. Mol. Sci.. 21, 5460 (2020)","journal-title":"Int. J. Mol. Sci.."},{"key":"757_CR21","doi-asserted-by":"crossref","first-page":"5971","DOI":"10.3390\/ijms12095971","volume":"12","author":"SC Park","year":"2011","unstructured":"Park, S.C., Park, Y., Hahm, K.S.: The role of antimicrobial peptides in preventing multidrug-resistant bacterial infections and biofilm formation. Int. J. Mol. Sci. 12, 5971\u20135992 (2011)","journal-title":"Int. J. Mol. Sci."},{"key":"757_CR22","volume":"185","author":"H Kim","year":"2020","unstructured":"Kim, H., Jang, J.H., Kim, S.C., Cho, J.H.: Development of a novel hybrid antimicrobial peptide for targeted killing of Pseudomonas aeruginosa. Eur. J. Med. Chem. 185, 111814 (2020)","journal-title":"Eur. J. Med. Chem."},{"key":"757_CR23","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nrmicro1441","volume":"4","author":"A Peschel","year":"2006","unstructured":"Peschel, A., Sahl, H.G.: The co-evolution of host cationic antimicrobial peptides and microbial resistance. Nat. Rev. Microbiol. 4, 529\u2013536 (2006)","journal-title":"Nat. Rev. Microbiol."},{"key":"757_CR24","doi-asserted-by":"crossref","first-page":"603","DOI":"10.3390\/ijms17050603","volume":"17","author":"S Wang","year":"2016","unstructured":"Wang, S., Zeng, X., Yang, Q., Qiao, S.: Antimicrobial peptides as potential alternatives to antibiotics in food animal industry. Int. J. Mol. Sci. 17, 603 (2016)","journal-title":"Int. J. Mol. Sci."},{"key":"757_CR25","doi-asserted-by":"crossref","first-page":"2866","DOI":"10.3389\/fmicb.2019.02866","volume":"10","author":"N Raheem","year":"2019","unstructured":"Raheem, N., Straus, S.K.: Mechanisms of action for antimicrobial peptides with antibacterial and antibiofilm functions. Front. Microbiol. 10, 2866 (2019)","journal-title":"Front. Microbiol."},{"key":"757_CR26","doi-asserted-by":"crossref","first-page":"2740","DOI":"10.1093\/bioinformatics\/bty179","volume":"34","author":"D Veltri","year":"2018","unstructured":"Veltri, D., Kamath, U., Shehu, A.: Deep learning improves antimicrobial peptide recognition. Bioinformatics 34, 2740\u20132747 (2018)","journal-title":"Bioinformatics"},{"key":"757_CR27","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31, 1235\u20131270 (2019)","journal-title":"Neural Comput."},{"key":"757_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00726-022-03190-0","volume":"55","author":"M Jaiswal","year":"2023","unstructured":"Jaiswal, M., Singh, A., Kumar, S.: PTPAMP: prediction tool for plant-derived antimicrobial peptides. Amino Acids 55, 1\u20137 (2023)","journal-title":"Amino Acids"},{"key":"757_CR29","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1038\/s41597-019-0154-y","volume":"6","author":"X Kang","year":"2019","unstructured":"Kang, X., Dong, F., Shi, C., Liu, S., Sun, J., Chen, J., Li, H., Xu, H., Lao, X., Zheng, H.: DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci. Data. 6, 148 (2019)","journal-title":"Sci. Data."},{"key":"757_CR30","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1093\/nar\/gkn655","volume":"37","author":"R Hammami","year":"2009","unstructured":"Hammami, R., Ben Hamida, J., Vergoten, G., Fliss, I.: PhytAMP: a database dedicated to antimicrobial plant peptides. Nucleic Acids Res. 37, 963\u2013968 (2009)","journal-title":"Nucleic Acids Res."},{"key":"757_CR31","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s12298-018-0524-5","volume":"24","author":"P Umadevi","year":"2018","unstructured":"Umadevi, P., Soumya, M., George, J.K., Anandaraj, M.: Proteomics assisted profiling of antimicrobial peptide signatures from black pepper (Piper nigrum L.). Physiol Mol Biol Plants. 24, 379\u201387 (2018)","journal-title":"Physiol Mol Biol Plants."},{"key":"757_CR32","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.1093\/nar\/gky092","volume":"46","author":"The UniProt Consortium","year":"2018","unstructured":"The UniProt Consortium: UniProt: the universal protein knowledgebase. Nucleic Acids Res. 46, 2699 (2018)","journal-title":"Nucleic Acids Res."},{"key":"757_CR33","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baac011","author":"S Ramazi","year":"2022","unstructured":"Ramazi, S., Mohammadi, N., Allahverdi, A., Khalili, E., Abdolmaleki, P.: A review on antimicrobial peptides databases and the computational tools. Database (2022). https:\/\/doi.org\/10.1093\/database\/baac011","journal-title":"Database"},{"key":"757_CR34","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1109\/TCBB.2020.2999399","volume":"19","author":"S Gull","year":"2022","unstructured":"Gull, S., Minhas, F.: AMP0: species-specific prediction of anti-microbial peptides using zero and few shot learning. IEEE\/ACM Trans. Comput. Biol. Bioinform. 19, 275\u2013283 (2022)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"757_CR35","doi-asserted-by":"crossref","first-page":"16968","DOI":"10.1371\/journal.pone.0016968","volume":"6","author":"M Torrent","year":"2011","unstructured":"Torrent, M., Andreu, D., Nogu\u00e9s, V.M., Boix, E.: Connecting peptide physicochemical and antimicrobial properties by a rational prediction model. PLoS ONE 6, 16968 (2011)","journal-title":"PLoS ONE"},{"key":"757_CR36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-11-S1-S19","volume":"11","author":"S Lata","year":"2010","unstructured":"Lata, S., Mishra, N.K., Raghava, G.P.: AntiBP2: improved version of antibacterial peptide prediction. BMC Bioinformatics 11, 1\u20137 (2010)","journal-title":"BMC Bioinformatics"},{"key":"757_CR37","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.ab.2013.01.019","volume":"436","author":"X Xiao","year":"2013","unstructured":"Xiao, X., Wang, P., Lin, W.Z., Jia, J.H., Chou, K.C.: iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal. Biochem. 436, 168\u2013177 (2013)","journal-title":"Anal. Biochem."},{"key":"757_CR38","doi-asserted-by":"crossref","unstructured":"Randou, E.G., Veltri, D., Shehu, A.: Binary response models for recognition of antimicrobial peptides. In: Proceedings of the International Conference on Bioinformatics. Computational Biology and Biomedical Informatics. pp. 76\u201385 (2013)","DOI":"10.1145\/2506583.2506597"},{"key":"757_CR39","doi-asserted-by":"crossref","first-page":"42362","DOI":"10.1038\/srep42362","volume":"7","author":"PK Meher","year":"2017","unstructured":"Meher, P.K., Sahu, T.K., Saini, V., Rao, A.R.: Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou\u2019s general PseAAC. Sci. Rep. 7, 42362 (2017)","journal-title":"Sci. Rep."},{"key":"757_CR40","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1038\/s41598-018-19752-w","volume":"8","author":"P Bhadra","year":"2018","unstructured":"Bhadra, P., Yan, J., Li, J., Fong, S., Siu, S.W.: AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Sci. Rep. 8, 1697 (2018)","journal-title":"Sci. Rep."},{"key":"757_CR41","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.compag.2014.12.008","volume":"111","author":"MA Iquebal","year":"2015","unstructured":"Iquebal, M.A., Arora, V., Rai, A., Kumar, D.: Species specific approach to the development of web-based antimicrobial peptides prediction tool for cattle. Comput. Electron. Agric. 111, 55\u201361 (2015)","journal-title":"Comput. Electron. Agric."},{"key":"757_CR42","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s12602-016-9215-0","volume":"8","author":"A Gautam","year":"2016","unstructured":"Gautam, A., Sharma, A., Jaiswal, S., Fatma, S., Arora, V., Iquebal, M.A., Nandi, S., Sundaray, J.K., Jayasankar, P., Rai, A., Kumar, D.: Development of antimicrobial peptide prediction tool for aquaculture industries. Probiotics Antimicrob Proteins. 8, 141\u2013149 (2016)","journal-title":"Probiotics Antimicrob Proteins."},{"key":"757_CR43","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1093\/bioinformatics\/btm068","volume":"23","author":"CD Fjell","year":"2007","unstructured":"Fjell, C.D., Hancock, R.E., Cherkasov, A.: AMPer: a database and an automated discovery tool for antimicrobial peptides. Bioinformatics 23, 1148\u20131155 (2007)","journal-title":"Bioinformatics"},{"key":"757_CR44","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3389\/fmicb.2013.00294","volume":"4","author":"D Gaspar","year":"2013","unstructured":"Gaspar, D., Veiga, A.S., Castanho, M.A.: From antimicrobial to anticancer peptides. A review. Front Microbiol. 4, 294 (2013)","journal-title":"Front Microbiol."},{"key":"757_CR45","volume":"2015","author":"HT Lee","year":"2015","unstructured":"Lee, H.T., Lee, C.C., Yang, J.R., Lai, J.Z., Chang, K.Y.: A large-scale structural classification of antimicrobial peptides. Biomed. Res. Int. 2015, 475062 (2015)","journal-title":"Biomed. Res. Int."},{"key":"757_CR46","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1093\/nar\/gkv1278","volume":"44","author":"G Wang","year":"2016","unstructured":"Wang, G., Li, X., Wang, Z.: APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 44, 1087\u20131093 (2016)","journal-title":"Nucleic Acids Res."},{"key":"757_CR47","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1093\/nar\/gkv1051","volume":"44","author":"FH Waghu","year":"2016","unstructured":"Waghu, F.H., Barai, R.S., Gurung, P., Idicula-Thomas, S.: CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res. 44, 1094\u20131097 (2016)","journal-title":"Nucleic Acids Res."},{"key":"757_CR48","doi-asserted-by":"publisher","first-page":"D285","DOI":"10.1093\/nar\/gky1030","volume":"47","author":"J-H Jhong","year":"2019","unstructured":"Jhong, J.-H., Chi, Y.-H., Li, W.-C., Lin, T.-H., Huang, K.-Y., Lee, T.-Y.: dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data. Nucleic Acids Res. 47, D285\u2013D297 (2019). https:\/\/doi.org\/10.1093\/nar\/gky1030","journal-title":"Nucleic Acids Res."},{"key":"757_CR49","doi-asserted-by":"crossref","first-page":"4702","DOI":"10.1038\/s41467-019-12607-6","volume":"10","author":"L Hu","year":"2019","unstructured":"Hu, L., Xu, Z., Wang, M., et al.: The chromosome-scale reference genome of black pepper provides insight into piperine biosynthesis. Nat. Commun. 10, 4702 (2019)","journal-title":"Nat. Commun."},{"key":"757_CR50","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1007\/s00299-023-03044-3","volume":"42","author":"H Ma","year":"2023","unstructured":"Ma, H., Feng, Y., Cao, Q., Jia, J., Ali, M., Shah, D., Meyers, B.C., He, H., Zhang, Y.: Evolution of antimicrobial cysteine-rich peptides in plants. Plant Cell Rep. 42, 1517\u20131527 (2023)","journal-title":"Plant Cell Rep."},{"key":"757_CR51","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","volume":"28","author":"L Fu","year":"2012","unstructured":"Fu, L., Niu, B., Zhu, Z., Wu, S., Li, W.: CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150\u20133152 (2012)","journal-title":"Bioinformatics"},{"key":"757_CR52","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s40529-021-00312-x","volume":"62","author":"J Li","year":"2021","unstructured":"Li, J., Hu, S., Jian, W., et al.: Plant antimicrobial peptides: structures, functions, and applications. Bot. Stud. 62, 5 (2021)","journal-title":"Bot. Stud."},{"key":"757_CR53","doi-asserted-by":"crossref","first-page":"5856","DOI":"10.1038\/s41467-022-33516-1","volume":"13","author":"MP Hoelscher","year":"2022","unstructured":"Hoelscher, M.P., Forner, J., Calderone, S., et al.: Expression strategies for the efficient synthesis of antimicrobial peptides in plastids. Nat. Commun. 13, 5856 (2022)","journal-title":"Nat. Commun."},{"key":"757_CR54","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning. pp. 448\u201356 (2015)"},{"key":"757_CR55","unstructured":"Tauber, L., S\u00e1nchez, V.: Introducing the normal distribution in a data analysis course: specific meaning contributed by the use of computers. In: Proceedings of Seventh International Congress for Teaching Statistics. (2002)"},{"key":"757_CR56","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"757_CR57","first-page":"338","volume":"2014","author":"H Sak","year":"2014","unstructured":"Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Interspeech 2014, 338\u2013342 (2014)","journal-title":"Interspeech"},{"key":"757_CR58","unstructured":"Nwankpa, C., Ijomah, W.L., Gachagan, A., Marshall, S.: Activation functions: Comparison of trends in practice and research for deep learning. (2018). arXiv preprint arXiv:1811.03378."}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00757-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-025-00757-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00757-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T10:48:42Z","timestamp":1758797322000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-025-00757-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,28]]},"references-count":58,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["757"],"URL":"https:\/\/doi.org\/10.1007\/s41060-025-00757-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5414643\/v1","asserted-by":"object"}]},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,28]]},"assertion":[{"value":"8 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"There are no conflicts of interest to declare.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}