Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics.

ABSTRACT:

Motivation

Metabolomics generates complex data necessitating advanced computational methods for generating biological insight. While machine learning (ML) is promising, the challenges of selecting the best algorithms and tuning hyperparameters, particularly for non-experts, remain. Automated machine learning (AutoML) can streamline this process; however, the issue of interpretability could persist. This research introduces a unified pipeline that combines AutoML with explainable AI (XAI) techniques to optimize metabolomics analysis.

Results

We tested our approach on two datasets: renal cell carcinoma (RCC) urine metabolomics and ovarian cancer (OC) serum metabolomics. AutoML, using auto-sklearn, surpassed standalone ML algorithms such as SVM and random forest in differentiat

SUBMITTER: Bifarin OO 

PROVIDER: S-EPMC10634896 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature