[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117053-en":3,"doc-seo-117053-105":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},117053,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning Made Easy (MLme) - A Comprehensive Toolkit for Machine Learning-Driven Data Analysis","Machine learning (ML) enables researchers to extract value from complex datasets, yet building effective ML pipelines demands substantial time, effort, and ML/programming expertise. Users must also fully configure pipelines to reach strong performance, limiting research progress. Machine Learning Made Easy (MLme) streamlines ML for research by targeting classification problems and integrating Data Exploration, AutoML, CustomML, and Visualization. Testing on six datasets confirms promising, versatile classification performance and demonstrates feature selection that identifies significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.","source: [https://doi.org/10.48350/189277 | downloaded:](https://doi.org/10.48350/189277 | downloaded:) 10.12.2023  \nbioRxiv preprint doi: https://doi.org/10.1101/2023.07.04.546825; this version posted July 4 , 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is  \nmade available under aCC-BY 4.0 International license.  \nMachine Learning Made Easy (MLme) : A Comprehensive Toolkit for Machine Learning-Driven Data Analysis  \nAkshay Akshay 1,2,\\# , Mitali Katoch3,\\# , Navid Shekarchizadeh4,5 , Masoud Abedi4 , Ankush Sharma6,7 , Fiona C. Burkhard 1,8 , Rosalyn M. Adam9,10,11 , 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, Switzerland  \n2 Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland  \n3 Institute of Neuropathology, Universitätsklinikum Erlangen, Friedrich-AlexanderUniversität Erlangen-Nürnberg (FAU), 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, Oslo, Norway  \n7 Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway  \n8 Department of Urology, Inselspital University Hospital, 3010 Bern, Switzerland  \n9 Urological Diseases Research Center, Boston Children’s Hospital, MA, USA  \n10 Harvard Medical School, Boston, Department of Surgery MA, USA  \n11 Broad Institute of MIT and Harvard, Cambridge, MA, USA  \n\\# Contributed equally.  \n* Corresponding author:  \nAli Hashemi Gheinani, Urological Diseases Research Center, Boston Children’s Hospital, Harvard Medical School and Broad Institute of MIT and Harvard, Cambridge, MA, USA  \ne-mail: Al[i.HashemiGheinani@childrens.harvard.edu](i.HashemiGheinani@childrens.harvard.edu)  \nbioRxiv preprint doi: [https://doi.org/10.1101/2023.07.04.546825](https://doi.org/10.1101/2023.07.04.546825); this version posted July 4, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is  \nmade available under aCC-BY 4.0 International license.  \nKeywords  \n• Machine learning  \n• Classification problems  \n• Data analysis  \n• AutoML  \n• Visualization  \nKey Points  \n• MLme is a novel tool that simplifies machine learning (ML) for researchers by integrating Data Exploration, AutoML, CustomML, and Visualization functionalities.  \n• MLme improves efficiency and productivity by streamlining the ML workflow and eliminating the need for extensive coding efforts.  \n• Rigorous testing on diverse datasets demonstrates MLme's promising performance in classification problems.  \n• MLme provides intuitive interfaces for data exploration, automated ML, customizable ML pipelines, and result visualization.  \n• Future developments aim to expand MLme's capabilities to include support for unsupervised learning, regression, hyperparameter tuning, and integration of userdefined algorithms.  \nbioRxiv preprint doi: [https://doi.org/10.1101/2023.07.04.546825](https://doi.org/10.1101/2023.07.04.546825); this version posted July 4, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is  \nmade available under aCC-BY 4.0 International license.  \nAbstract  \nBackground  \nMachine 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 ch","cbCaicxKk4zqNoo9","https://ap.wps.com/l/cbCaicxKk4zqNoo9","pdf",1677588,1,16,"English","en",105,"# Key Points\n## MLme tool capabilities\n## Performance on datasets\n## Future directions","[{\"question\":\"What problem does MLme address for researchers using machine learning?\",\"answer\":\"MLme targets the difficulty of building robust ML pipelines that require extensive time, effort, and ML/programming expertise, and it reduces the need for heavy coding while simplifying the workflow for researchers.\"},{\"question\":\"Which core functionalities are integrated into MLme?\",\"answer\":\"MLme integrates Data Exploration, AutoML, CustomML, and Visualization to cover essential stages of an ML workflow and support intuitive researcher interaction.\"},{\"question\":\"How was MLme evaluated and what results were reported?\",\"answer\":\"MLme was tested rigorously on six distinct datasets, with results showing promising classification performance across datasets and demonstrating effective feature selection for markers in specific cell populations.\"}]","Machine Learning Made Easy (MLme) - A Comprehensive Toolkit for Machine Learning-Driven Data Analysis | PDF",1785673461,40,{"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/research-report/",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/117053/",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 address for researchers using machine learning?","Question",{"text":75,"@type":76},"MLme targets the difficulty of building robust ML pipelines that require extensive time, effort, and ML/programming expertise, and it reduces the need for heavy coding while simplifying the workflow for researchers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which core functionalities are integrated into MLme?",{"text":80,"@type":76},"MLme integrates Data Exploration, AutoML, CustomML, and Visualization to cover essential stages of an ML workflow and support intuitive researcher interaction.",{"name":82,"@type":73,"acceptedAnswer":83},"How was MLme evaluated and what results were reported?",{"text":84,"@type":76},"MLme was tested rigorously on six distinct datasets, with results showing promising classification performance across datasets and demonstrating effective feature selection for markers in specific cell populations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]