Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence.

Background: Breast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis of BC. This study aims to identify targeted metabolomic biomarker candidates based on explainable artificial intelligence (XAI) for the specific detection of BC.

In-vivo and in-vitro assessment of curcumin loaded bile salt stabilized nanovesicles for oral delivery.

Background: Bile salts enriched nanovesicles (bilosomes) have been attention worthy in the past few years due to their distinctive effect on the enhancement of drug delivery through various physiological administration routes. Oral delivery of multifunctioning phytochemical curcumin has faced a lot of difficulties due to its scarce solubility and poor oral bioavailability.

Objective: The current investigation aimed to develop curcumin loaded bilosomes for improvement of oral curcumin bioavailability with maximum efficiency and safety.

Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches.

Background: Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificial intelligence (XAI) approaches based on metabolomics panel data to identify biomarkers and develop predictive models for T2DM.

Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence.

This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model.