Machine Learning Made Easy (MLme): A comprehensive toolkit for machine learning-driven data analysis
Akshay A, Katoch M, Shekarchizadeh N, Abedi M, Sharma A, Burkhard FC, Adam RM, Monastyrskaya K, Gheinani AH (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 13
Article Number: giad111
DOI: 10.1093/gigascience/giad111
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 confguration 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, specifcally focusing on classifcation problems at present. By integrating 4 essential functionalities - namely, Data Exploration, AutoML, CustomML, and Visualization - MLme fulflls 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, reaffrming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identifed signifcant 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.
Authors with CRIS profile
Involved external institutions
Universität Bern
Switzerland (CH)
Universität Leipzig
Germany (DE)
University of Oslo
Norway (NO)
Boston Children's Hospital
United States (USA) (US)
Switzerland (CH)
Universität Leipzig
Germany (DE)
University of Oslo
Norway (NO)
Boston Children's Hospital
United States (USA) (US)
How to cite
APA:
Akshay, A., Katoch, M., Shekarchizadeh, N., Abedi, M., Sharma, A., Burkhard, F.C.,... Gheinani, A.H. (2024). Machine Learning Made Easy (MLme): A comprehensive toolkit for machine learning-driven data analysis. GigaScience, 13. https://dx.doi.org/10.1093/gigascience/giad111
MLA:
Akshay, Akshay, et al. "Machine Learning Made Easy (MLme): A comprehensive toolkit for machine learning-driven data analysis." GigaScience 13 (2024).
BibTeX: Download