Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis.

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES GigaScience Pub Date : 2024-01-02 DOI:10.1093/gigascience/giad111
Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi, Ankush Sharma, Fiona C Burkhard, Rosalyn M Adam, Katia Monastyrskaya, Ali Hashemi Gheinani
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Abstract

Background: 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.

Results: 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.

Conclusion: 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.

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Machine Learning Made Easy (MLme):机器学习驱动数据分析的综合工具包。
背景: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资产。然而,开发有效而强大的 ML 管道是一项真正的挑战,需要花费大量的时间和精力,从而阻碍了研究的进展。该领域的现有工具需要对 ML 原理和编程技巧有深刻的理解。此外,用户还需要对其 ML 管道进行全面配置,以获得最佳性能:为了应对这些挑战,我们开发了一款名为 "机器学习变得简单"(MLme)的新型工具,它可以简化 ML 在研究中的使用,目前尤其侧重于分类问题。通过整合 4 项基本功能--即数据探索、自动ML、自定义ML 和可视化--MLme 可以满足研究人员的各种需求,同时无需大量编码工作。为了证明 MLme 的适用性,我们在 6 个不同的数据集上进行了严格的测试,每个数据集都具有独特的特征和挑战。我们的测试结果一致表明,该工具在不同的数据集上都有良好的表现,这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,我们成功地鉴定出了 CD8+ 幼稚细胞群 (BACH2)、CD16+ 细胞群 (CD16) 和 CD14+ 细胞群 (VCAN) 的重要标记物:MLme 是利用 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。有关 MLme 的源代码和详细教程,请访问 https://github.com/FunctionalUrology/MLme。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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