Akshay Akshay, Katoch Mitali, Shekarchizadeh Navid, Abedi Masoud, Sharma Ankush, Burkhard Fiona C, Adam Rosalyn M, Monastyrskaya Katia, Gheinani Ali Hashemi
Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland.
Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland.
bioRxiv. 2023 Jul 4:2023.07.04.546825. doi: 10.1101/2023.07.04.546825.
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.
To address these challenges, we have developed a novel tool called (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 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 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.
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（ML）已成为研究人员从复杂数据集中分析和提取有价值信息的重要工具。然而，开发一个有效且强大的机器学习流程可能是一项真正的挑战，需要大量的时间和精力，从而阻碍研究进展。这一领域现有的工具需要对机器学习原理有深入理解以及具备编程技能。此外，用户需要对其机器学习流程进行全面配置以获得最佳性能。
为应对这些挑战，我们开发了一种名为（MLme）的新型工具，它简化了研究中机器学习的使用，目前特别关注分类问题。通过整合数据探索、自动机器学习、自定义机器学习和可视化这四个基本功能，MLme满足了研究人员的各种需求，同时无需进行大量编码工作。为证明MLme的适用性，我们在六个不同的数据集上进行了严格测试，每个数据集都有独特的特征和挑战。我们的结果始终显示在不同数据集上都有良好的性能，再次证明了该工具的通用性和有效性。此外，通过利用MLme的特征选择功能，我们成功识别出了CD8 + 初始（BACH2）、CD16 +（CD16）和CD14 +（VCAN）细胞群体的重要标志物。
MLme是利用机器学习（ML）促进深入数据分析和提高研究成果的宝贵资源，同时减轻了与复杂编码脚本相关的担忧。MLme的源代码和详细教程可在https://github.com/FunctionalUrology/MLme获取。
