An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | Proceedings of BIBM 2021 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1109/BIBM52615.2021.9669418 |
| Keywords | Machine Learning; Concept drift; Metabolomics Analysis; Biomedical engineering; Computational biomedical analysis; Metabolomic prediction |
| Description | The most challenging applications of data analysis prediction are mostly related to scenarios, where the source data is being provided in a time course. As the distribution of the underlying reality shifts over a time, a classification model trained on the previously relevant data starts to yield incorrect predictions about the data that are relevant right now. This phenomenon in machine learning is called concept drift. Within biomedical data, one of the molecular networks that is most significantly changing over a time, is the metabolome. Using metabolomics analysis to biomedical applications, makes an ideal tool for preventive healthcare, pharmaceutical industry, and even ecology engineering. This study provides an innovated perspective on the analysis of metabolomics datasets using the concept of drift detection. The evaluation is based on two main goals. The first goal is connected to the concept drift detection in available metabolomics datasets and the second goal is to provide the assessment of commonly used tools, resulting in the best detection approach for a general metabolomics dataset. |