Supporting data for "Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis"

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. <br>To address these challenges, we have developed a novel tool called <i>Machine Learning Made Easy</i> (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfils the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six 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. <br>MLme serves as a valuable resource for leveraging machine learning (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.

机器学习（Machine Learning, ML）已成为研究人员从复杂数据集中开展分析、提取有价值信息的关键支撑手段。然而，构建高效且鲁棒的机器学习流水线往往面临实际挑战，需要投入大量时间与精力，进而阻碍研究进展。当前领域内的现有工具均要求使用者对机器学习原理具备深入理解，同时掌握编程技能。此外，用户还需对机器学习流水线进行全面配置，方能获得最优性能。
为解决上述挑战，我们开发了一款名为《机器学习极简入门》（Machine Learning Made Easy，简称MLme）的新型工具，旨在简化研究中机器学习的应用流程，目前主要聚焦于分类问题。该工具集成了四大核心功能模块：数据探索（Data Exploration）、自动机器学习（AutoML）、自定义机器学习（CustomML）与可视化（Visualization），可满足研究人员的多样化需求，同时无需编写大量代码。
为验证MLme的适用性，我们针对六个具备独特特性与挑战的不同数据集开展了严格测试。测试结果在各类数据集上均展现出优异性能，进一步证实了该工具的通用性与有效性。此外，借助MLme的特征选择功能，我们成功识别出CD8+初始（BACH2）、CD16+（CD16）以及CD14+（VCAN）细胞群的关键标志物。
MLme可作为利用机器学习开展深入数据分析、提升研究成果的宝贵资源，同时无需使用者担忧复杂的代码脚本问题。MLme的源代码与详细教程可通过https://github.com/FunctionalUrology/MLme获取。