A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning.

Area of Science:

• Metabolomics
• Bioinformatics
• Computational Biology

Background:

• Machine learning, particularly deep learning, has seen significant advancements and applications in life sciences like genomics and metabolomics.
• Despite deep learning's success, the influence of data preprocessing techniques on its performance in life science data analysis remains under-assessed.

Purpose of the Study:

• To evaluate the impact of various data preprocessing methods on deep learning model performance in metabolomics.
• To establish a set of best practices for metabolomics data preprocessing to guide downstream deep learning applications.

Main Methods:

• Assessed commonly used and novel data preprocessing methods for metabolomics data.
• Analyzed the effect of these preprocessing steps on the performance of deep learning models for classification and reconstruction tasks.

Main Results:

• Identified key preprocessing steps that significantly influence deep learning performance in metabolomics.
• Quantified the performance variations across different preprocessing strategies.

Conclusions:

• Data preprocessing is a critical factor for successful deep learning in metabolomics.
• The study offers practical guidelines and best practices for researchers to optimize their metabolomics data analysis pipelines.