Author:
Chrobak Daria,Kołodzieczak Maciej,Kozlovska Polina,Krzemińska Adrianna,Miller Tymoteusz
Abstract
The rapid development of high-throughput sequencing technologies has led to an explosion of microbiological data, presenting new challenges and opportunities for understanding microbial processes and interactions. Machine learning techniques, such as the Random Forest algorithm, offer powerful tools for analyzing these large and complex datasets, providing valuable insights into microbial ecology, physiology, and evolution. In this study, we applied the Random Forest algorithm to microbiological data, focusing on data collection, preprocessing, feature selection, and model evaluation to ensure accurate, reliable, and meaningful results. Our findings demonstrated the effectiveness of the Random Forest algorithm in capturing complex relationships between microbial features and the target variable, contributing to the ongoing development of innovative solutions to pressing challenges in microbiology research and applications. Future work should explore the use of advanced machine learning techniques, integration of multi-omics data, and interdisciplinary collaborations to fully harness the potential of machine learning for advancing our understanding of microbial systems and their implications for human health, environmental sustainability, and biotechnological innovation.
Publisher
Scientific Publishing Center InterConf
Subject
General Chemical Engineering
Reference26 articles.
1. Amar, D., Frada, M., & Roth, R. (2015). Going beyond 16S rRNA gene sequencing for microbiome profiling: an overview of recent advances. In Future Science.
2. Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12(7), 878.
3. Battiti, R. (1994). Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 5(4), 537-550.
4. Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb), 281-305.
5. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.