[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117073-en":3,"doc-seo-117073-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117073,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",6,"Technology","Machine Learning Made Easy (MLme) - a comprehensive toolkit for machine learning-driven data analysis","Machine learning (ML) is crucial for extracting value from complex datasets, yet building an effective ML pipeline remains time-consuming and demanding, especially for users who must understand ML principles and perform extensive configuration and coding. Machine Learning Made Easy (MLme) addresses these barriers with streamlined support for classification tasks. The toolkit unifies Data Exploration, AutoML, CustomML, and Visualization, demonstrated through testing on six diverse datasets. Feature selection further identifies key markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) populations.","source: [https://doi.org/10.48350/191551 | downloaded:](https://doi.org/10.48350/191551 | downloaded:) 4.6.2024  \nGigaScience, 2024, 13, 1–9 DOI: 10.1093/gigascience/giad111  \nTECH NOTE  \nMachine Learning Made Easy (MLme): a comprehensive toolkit for machine learning–driven data analysis  \nAkshay Akshay1,2 ,†, Mitali Katoch 3 ,†, Navid Shekarchizadeh 4,5 , Masoud Abedi 4 , Ankush Sharma 6,7 , Fiona C. Burkhard 1,8 , Rosalyn M. Adam 9,10,11 , Katia Monastyrskaya 1,8 , and Ali Hashemi Gheinani 1,8,9,10,11 , *  \n1 Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland  \n2 Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland  \n3 Institute of Neuropathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany  \n4 Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany  \n5 Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, 04105 Leipzig, Germany  \n6 KG Jebsen Centre for B-cell Malignancies, Institute for Clinical Medicine, University of Oslo, 0318 Oslo, Norway  \n7 Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway  \n8 Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland  \n9Urological Diseases Research Center, Boston Children’s Hospital, 02115 Boston, MA, USA  \n10 Department of Surgery, Harvard Medical School, 02115 Boston, MA, USA  \n11 Broad Institute of MIT and Harvard, Cambridge, 02142 MA, USA  \n∗ Correspondence address: Ali Hashemi Gheinani, Urological Diseases Research Center, Boston Children’s Hospital, Harvard Medical School and Broad Institute of  \nMIT and Harvard, Cambridge, MA, [USA. E-mail: Ali.HashemiGheinani@childrens.harvard.edu](USA. E-mail: Ali.HashemiGheinani@childrens.harvard.edu)[ ](USA. E-mail: Ali.HashemiGheinani@childrens.harvard.edu)†Contributed equally.  \nAbstract  \nBackground: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.  \nResults: To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating 4 essential functionalities—namely, Data Exploration, AutoML, CustomML, and Visualization—MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on 6 distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme’s feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.  \nConclusion: MLme serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at [https://github.com/FunctionalUrology/MLme](https://github.com/FunctionalUrology/MLme).  \nKeywords: machine learning, classification problems, data analysis, AutoML, visualization  \nKey points  \n􀀂 MLme is a novel tool that simplifies machi","cbCailkavk1MxCfB","https://ap.wps.com/l/cbCailkavk1MxCfB","pdf",2794805,1,9,"English","en",105,"# Abstract\n## Background\n## Results\n## Conclusion\n# Key points\n# Introduction","[{\"question\":\"What problem does MLme target in machine learning-driven research?\",\"answer\":\"MLme targets the difficulty of developing effective ML pipelines, which often requires substantial coding, time, and configuration, slowing research progress.\"},{\"question\":\"Which core functionalities does MLme integrate for classification workflows?\",\"answer\":\"MLme integrates Data Exploration, AutoML, CustomML, and Visualization to support classification tasks with minimal extensive coding.\"},{\"question\":\"How was MLme evaluated and what did the results show?\",\"answer\":\"MLme was tested on six distinct datasets with varying characteristics. The tool showed promising performance consistently across these datasets.\"}]","Machine Learning Made Easy (MLme) - a comprehensive toolkit for machine learning-driven data analysis | PDF",1785673562,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-made-easy-mlme-a-comprehensive-toolkit-for-machine-learning-driven-data-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-made-easy-mlme-a-comprehensive-toolkit-for-machine-learning-driven-data-analysis/117073/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does MLme target in machine learning-driven research?","Question",{"text":75,"@type":76},"MLme targets the difficulty of developing effective ML pipelines, which often requires substantial coding, time, and configuration, slowing research progress.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which core functionalities does MLme integrate for classification workflows?",{"text":80,"@type":76},"MLme integrates Data Exploration, AutoML, CustomML, and Visualization to support classification tasks with minimal extensive coding.",{"name":82,"@type":73,"acceptedAnswer":83},"How was MLme evaluated and what did the results show?",{"text":84,"@type":76},"MLme was tested on six distinct datasets with varying characteristics. 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