[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126559-en":3,"doc-seo-126559-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126559,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique - Original Research","Computational prediction of promoters in Agrobacterium tumefaciens strain C58 focuses on promoter recognition as a key step for deciphering gene expression regulation. The study develops a machine learning model that encodes promoter sequences using accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings, then optimizes these features with correlation and an mRMR-based algorithm. A random forest classifier discriminates promoter from non-promoter sequences, evaluated by 10-fold cross-validation with overall accuracy of 0.837.","TYPE Original Research PUBLISHED 13 April 2023  \nDOI 10. 3389/fmicb.2023.1170785  \nOPEN ACCESS  \nEDITED BY  \nEttayapuram Ramaprasad Azhagiya Singam, University of California, Berkeley, United States  \nREVIEWED BY  \nVijaya Sundar Jeyaraj,  \nUniversity of Illinois at Urbana-Champaign, United States  \nLiang Cheng,  \nHarbin Medical University, China Hao Wu,  \nSchool of Software, Shandong University, China  \n*CORRESPONDENCE  \nHasan Zulfiqar  \n hasanzulfi[qar@uestc.edu.cn](qar@uestc.edu.cn)[ ](qar@uestc.edu.cn)[Zhao-Yue Zhang](Zhao-Yue Zhang)  \n [zyzhang@uestc.edu.cn](zyzhang@uestc.edu.cn)[ ](zyzhang@uestc.edu.cn)Fen Liu  \n [nmlf906@163.com](nmlf906@163.com)  \nSPECIALTY SECTION  \nThis article was submitted to Evolutionary and Genomic Microbiology, a section of the journal  \nFrontiers in Microbiology  \nRECEIVED 21 February 2023  \nACCEPTED 17 March 2023  \nPUBLISHED 13 April 2023  \nCITATION  \nZulfiqar H, Ahmed Z, Kissanga  \nGrace-Mercure B, Hassan F, Zhang Z-Y and Liu F (2023) Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique. Front. Microbiol. 14:1170785 .  \ndoi: 10.3389/fmicb.2023.1170785  \nCOPYRIGHT  \n© 2023 Zulfiqar, Ahmed, Kissanga  \nGrace-Mercure, Hassan, Zhang and Liu. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nComputational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique  \nHasan Zulfiqar1,2*, Zahoor Ahmed1 ,  \nBakanina Kissanga Grace-Mercure2 , Farwa Hassan2 , Zhao-Yue Zhang2* and Fen Liu3*  \n1Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China, 2 School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China, 3 Department of Radiation Oncology, Peking University Cancer Hospital (Inner Mongolia Campus), A􀀈liated Cancer Hospital of Inner Mongolia Medical University, Inner Mongolia Cancer Hospital, Hohhot, China  \nPromotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58 . In the model, promotor sequences were encoded by three di􀀀erent kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58 . The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0 .837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.  \nKEYWORDS  \nprokaryotic promotors, feature extraction, agrobacterium tumefaciens strain C58, featureselection, algorithms  \n1. Introduction  \nAgrobacterium belongs to the family of ubiquitous gram-negative soil bacteria. Infectious strains of agrobacterium such as agrobacterium tumefaciens strain C58 cause hairy root and crown gall diseases in plants (Goodner et al., 2001) . Promotors are the genomic regions upstream of a gene on DNA where transcription factor and RNA polymerase bind together to initiate gene tra","cbCainXiYajGEffl","https://ap.wps.com/l/cbCainXiYajGEffl","pdf",813530,3,1,9,"English","en",105,"# Introduction\n## Promoter biology and significance\n## Existing experimental and computational approaches\n## Gap for Agrobacterium tumefaciens C58","[{\"question\":\"What problem does the study address about promoters in Agrobacterium tumefaciens C58?\",\"answer\":\"It addresses the need for an accurate computational method to recognize promoter sequences in A. tumefaciens C58, where no dedicated model was available.\"},{\"question\":\"How are promoter sequences represented in the proposed machine learning model?\",\"answer\":\"Promoter sequences are encoded using accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings.\"},{\"question\":\"What classification method and evaluation results are reported?\",\"answer\":\"The optimized features are fed into a random forest classifier, and 10-fold cross-validation yields an overall accuracy of 0.837.\"}]","Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique - Original Research | PDF",1785933319,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"computational-prediction-of-promotors-in-agrobacterium-tumefaciens-strain-c58-by-using-the-machine-learning-technique-original-research","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/computational-prediction-of-promotors-in-agrobacterium-tumefaciens-strain-c58-by-using-the-machine-learning-technique-original-research/126559/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address about promoters in Agrobacterium tumefaciens C58?","Question",{"text":76,"@type":77},"It addresses the need for an accurate computational method to recognize promoter sequences in A. tumefaciens C58, where no dedicated model was available.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are promoter sequences represented in the proposed machine learning model?",{"text":81,"@type":77},"Promoter sequences are encoded using accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings.",{"name":83,"@type":74,"acceptedAnswer":84},"What classification method and evaluation results are reported?",{"text":85,"@type":77},"The optimized features are fed into a random forest classifier, and 10-fold cross-validation yields an overall accuracy of 0.837.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]