{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:06:24Z","timestamp":1743069984652,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030461645"},{"type":"electronic","value":"9783030461652"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-46165-2_5","type":"book-chapter","created":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T12:03:58Z","timestamp":1588075438000},"page":"52-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FastFeatGen: Faster Parallel Feature Extraction from Genome Sequences and Efficient Prediction of DNA $$N^6$$-Methyladenine Sites"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8784-5406","authenticated-orcid":false,"given":"Md. Khaledur","family":"Rahman","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,29]]},"reference":[{"issue":"12","key":"5_CR1","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1038\/nrm4076","volume":"16","author":"G-Z Luo","year":"2015","unstructured":"Luo, G.-Z., Blanco, M.A., Greer, E.L., He, C., Shi, Y.: DNA $$N^6$$-methyladenine: a new epigenetic mark in eukaryotes? Nat. Rev. Mol. Cell Biol. 16(12), 705 (2015)","journal-title":"Nat. Rev. Mol. Cell Biol."},{"issue":"4","key":"5_CR2","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/j.cell.2015.04.005","volume":"161","author":"EL Greer","year":"2015","unstructured":"Greer, E.L., et al.: DNA methylation on N$$^6$$-adenine in C. elegans. Cell 161(4), 868\u2013878 (2015)","journal-title":"Cell"},{"issue":"4","key":"5_CR3","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1016\/j.cell.2015.04.018","volume":"161","author":"G Zhang","year":"2015","unstructured":"Zhang, G., et al.: N$$^6$$-methyladenine DNA modification in Drosophila. Cell 161(4), 893\u2013906 (2015)","journal-title":"Cell"},{"issue":"4","key":"5_CR4","doi-asserted-by":"publisher","first-page":"16011","DOI":"10.1038\/nmicrobiol.2016.11","volume":"1","author":"G Lichinchi","year":"2016","unstructured":"Lichinchi, G., et al.: Dynamics of the human and viral m$$^6$$A RNA methylomes during HIV-1 infection of T cells. Nat. Microbiol. 1(4), 16011 (2016)","journal-title":"Nat. Microbiol."},{"issue":"5","key":"5_CR5","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1016\/j.chom.2016.10.002","volume":"20","author":"G Lichinchi","year":"2016","unstructured":"Lichinchi, G., et al.: Dynamics of human and viral RNA methylation during Zika virus infection. Cell Host Microbe 20(5), 666\u2013673 (2016)","journal-title":"Cell Host Microbe"},{"issue":"2","key":"5_CR6","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.molcel.2018.06.015","volume":"71","author":"C-L Xiao","year":"2018","unstructured":"Xiao, C.-L., et al.: N$$^6$$-methyladenine DNA modification in the human genome. Mol. Cell 71(2), 306\u2013318 (2018)","journal-title":"Mol. Cell"},{"issue":"4","key":"5_CR7","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/j.cell.2015.04.010","volume":"161","author":"Y Fu","year":"2015","unstructured":"Fu, Y., et al.: N$$^6$$-methyldeoxyadenosine marks active transcription start sites in Chlamydomonas. Cell 161(4), 879\u2013892 (2015)","journal-title":"Cell"},{"issue":"10","key":"5_CR8","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1021\/tx000085h","volume":"13","author":"S Frelon","year":"2000","unstructured":"Frelon, S., Douki, T., Ravanat, J.-L., Pouget, J.-P., Tornabene, C., Cadet, J.: High-performance liquid chromatography- tandem mass spectrometry measurement of radiation-induced base damage to isolated and cellular DNA. Chem. Res. Toxicol. 13(10), 1002\u20131010 (2000)","journal-title":"Chem. Res. Toxicol."},{"issue":"1","key":"5_CR9","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1093\/nar\/29.1.268","volume":"29","author":"RJ Roberts","year":"2001","unstructured":"Roberts, R.J., Macelis, D.: Rebase\u2014restriction enzymes and methylases. Nucleic Acids Res. 29(1), 268\u2013269 (2001)","journal-title":"Nucleic Acids Res."},{"issue":"6","key":"5_CR10","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1038\/nmeth.1459","volume":"7","author":"BA Flusberg","year":"2010","unstructured":"Flusberg, B.A., et al.: Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat. Methods 7(6), 461 (2010)","journal-title":"Nat. Methods"},{"issue":"12","key":"5_CR11","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1038\/nbt.2432","volume":"30","author":"G Fang","year":"2012","unstructured":"Fang, G., et al.: Genome-wide mapping of methylated adenine residues in pathogenic Escherichia coli using single-molecule real-time sequencing. Nat. Biotechnol. 30(12), 1232 (2012)","journal-title":"Nat. Biotechnol."},{"issue":"21","key":"5_CR12","doi-asserted-by":"publisher","first-page":"3548","DOI":"10.1002\/elps.201000357","volume":"31","author":"AM Krais","year":"2010","unstructured":"Krais, A.M., Cornelius, M.G., Schmeiser, H.H.: Genomic N$$^6$$-methyladenine determination by MEKC with LIF. Electrophoresis 31(21), 3548\u20133551 (2010)","journal-title":"Electrophoresis"},{"issue":"16","key":"5_CR13","doi-asserted-by":"publisher","first-page":"2796","DOI":"10.1093\/bioinformatics\/btz015","volume":"35","author":"W Chen","year":"2019","unstructured":"Chen, W., Lv, H., Nie, F., Lin, H.: i6mA-Pred: identifying DNA N$$^6$$-methyladenine sites in the rice genome. Bioinformatics 35(16), 2796\u20132800 (2019)","journal-title":"Bioinformatics"},{"issue":"1","key":"5_CR14","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.ygeno.2018.01.005","volume":"111","author":"P Feng","year":"2019","unstructured":"Feng, P., Yang, H., Ding, H., Lin, H., Chen, W., Chou, K.-C.: iDNA6mA-PseKNC: identifying DNA N$$^6$$-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 111(1), 96\u2013102 (2019)","journal-title":"Genomics"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Tahir, M., Tayara, H., Chong, K.T.: iDNA6mA (5-step rule): identification of DNA N$$^6$$-methyladenine sites in the rice genome by intelligent computational model via Chou\u2019s 5-step rule. Chemometrics and Intelligent Laboratory Systems (2019)","DOI":"10.1016\/j.chemolab.2019.04.007"},{"issue":"2","key":"5_CR16","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1038\/nbt.3437","volume":"34","author":"JG Doench","year":"2016","unstructured":"Doench, J.G., et al.: Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34(2), 184 (2016)","journal-title":"Nat. Biotechnol."},{"issue":"8","key":"5_CR17","doi-asserted-by":"publisher","first-page":"e0181943","DOI":"10.1371\/journal.pone.0181943","volume":"12","author":"MK Rahman","year":"2017","unstructured":"Rahman, M.K., Rahman, M.S.: CRISPRpred: a flexible and efficient tool for sgRNAs on-target activity prediction in CRISPR\/Cas9 systems. PLoS ONE 12(8), e0181943 (2017)","journal-title":"PLoS ONE"},{"issue":"16","key":"5_CR18","doi-asserted-by":"publisher","first-page":"2496","DOI":"10.1093\/bioinformatics\/btx222","volume":"33","author":"B Manavalan","year":"2017","unstructured":"Manavalan, B., Lee, J.: SVMQA: support\u2013vector-machine-based protein single-model quality assessment. Bioinformatics 33(16), 2496\u20132503 (2017)","journal-title":"Bioinformatics"},{"issue":"1","key":"5_CR19","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.jtbi.2010.12.024","volume":"273","author":"K-C Chou","year":"2011","unstructured":"Chou, K.-C.: Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol. 273(1), 236\u2013247 (2011)","journal-title":"J. Theor. Biol."},{"key":"5_CR20","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.artmed.2017.11.003","volume":"84","author":"MS Rahman","year":"2018","unstructured":"Rahman, M.S., Rahman, M.K., Kaykobad, M., Rahman, M.S.: isGPT: an optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection. Artif. Intell. Med. 84, 90\u2013100 (2018)","journal-title":"Artif. Intell. Med."},{"key":"5_CR21","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.artmed.2018.12.010","volume":"94","author":"MS Rahman","year":"2019","unstructured":"Rahman, M.S., Rahman, M.K., Saha, S., Kaykobad, M., Rahman, M.S.: Antigenic: an improved prediction model of protective antigens. Artif. Intell. Med. 94, 28\u201341 (2019)","journal-title":"Artif. Intell. Med."},{"issue":"7","key":"5_CR22","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1093\/bioinformatics\/btt072","volume":"29","author":"D-S Cao","year":"2013","unstructured":"Cao, D.-S., Xu, Q.-S., Liang, Y.-Z.: propy: a tool to generate various modes of Chou\u2019s PseAAC. Bioinformatics 29(7), 960\u2013962 (2013)","journal-title":"Bioinformatics"},{"issue":"8","key":"5_CR23","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.1093\/bioinformatics\/btu820","volume":"31","author":"B Liu","year":"2014","unstructured":"Liu, B., Liu, F., Fang, L., Wang, X., Chou, K.-C.: repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics 31(8), 1307\u20131309 (2014)","journal-title":"Bioinformatics"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Liu, B.: BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches. Brief. Bioinform. (2017)","DOI":"10.1093\/bib\/bbx165"},{"key":"5_CR25","unstructured":"Schauer, B.: Multicore processors\u2013a necessity. In: ProQuest Discovery Guides, pp. 1\u201314 (2008)"},{"issue":"6","key":"5_CR26","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MSP.2009.934110","volume":"26","author":"G Blake","year":"2009","unstructured":"Blake, G., Dreslinski, R.G., Mudge, T.: A survey of multicore processors. IEEE Signal Process. Mag. 26(6), 26\u201337 (2009)","journal-title":"IEEE Signal Process. Mag."},{"issue":"1","key":"5_CR27","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1093\/bib\/bbk007","volume":"7","author":"P Larranaga","year":"2006","unstructured":"Larranaga, P., et al.: Machine learning in bioinformatics. Brief. Bioinform. 7(1), 86\u2013112 (2006)","journal-title":"Brief. Bioinform."},{"issue":"3","key":"5_CR28","doi-asserted-by":"publisher","first-page":"185","DOI":"10.2174\/1389200219666180820112457","volume":"20","author":"N Stephenson","year":"2019","unstructured":"Stephenson, N., et al.: Survey of machine learning techniques in drug discovery. Curr. Drug Metab. 20(3), 185\u2013193 (2019)","journal-title":"Curr. Drug Metab."},{"issue":"1","key":"5_CR29","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3\u201342 (2006). \nhttps:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Mach. Learn."},{"issue":"8","key":"5_CR30","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1038\/s41477-018-0214-x","volume":"4","author":"C Zhou","year":"2018","unstructured":"Zhou, C., et al.: Identification and analysis of adenine $$N^6$$-methylation sites in the rice genome. Nat. Plants 4(8), 554 (2018)","journal-title":"Nat. Plants"},{"key":"5_CR31","doi-asserted-by":"publisher","first-page":"D85","DOI":"10.1093\/nar\/gkw95","volume":"45","author":"P Ye","year":"2016","unstructured":"Ye, P., Luan, Y., Chen, K., Liu, Y., Xiao, C., Xie, Z.: MethSMRT: an integrative database for DNA N6-methyladenine and N4-methylcytosine generated by single-molecular real-time sequencing. Nucleic Acids Res. 45, D85\u2013D89 (2016). \nhttps:\/\/doi.org\/10.1093\/nar\/gkw95","journal-title":"Nucleic Acids Res."},{"issue":"3","key":"5_CR32","doi-asserted-by":"publisher","first-page":"e4920","DOI":"10.1371\/journal.pone.0004920","volume":"4","author":"J Shao","year":"2009","unstructured":"Shao, J., Xu, D., Tsai, S.-N., Wang, Y., Ngai, S.-M.: Computational identification of protein methylation sites through bi-profile Bayes feature extraction. PLoS ONE 4(3), e4920 (2009)","journal-title":"PLoS ONE"},{"key":"5_CR33","first-page":"2079","volume":"11","author":"GC Cawley","year":"2010","unstructured":"Cawley, G.C., Talbot, N.L.: On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079\u20132107 (2010)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Computational Advances in Bio and Medical Sciences"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46165-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T23:14:21Z","timestamp":1588115661000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-46165-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461645","9783030461652"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46165-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCABS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Advances in Bio and Medical Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Miami, FL","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccabs2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccabs.engr.uconn.edu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}