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.

Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging.

Background: Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification.

An interpretable semi-supervised framework for patch-based classification of breast cancer.

Developing effective invasive Ductal Carcinoma (IDC) detection methods remains a challenging problem for breast cancer diagnosis. Recently, there has been notable success in utilizing deep neural networks in various application domains; however, it is well-known that deep neural networks require a large amount of labelled training data to achieve high accuracy. Such amounts of manually labelled data are time-consuming and expensive, especially when domain expertise is required.

Tissue Proteome of 2-Hydroxyacyl-CoA Lyase Deficient Mice Reveals Peroxisome Proliferation and Activation of ω-Oxidation.

Peroxisomal fatty acid α-oxidation is an essential pathway for the degradation of β-carbon methylated fatty acids such as phytanic acid. One enzyme in this pathway is 2-hydroxyacyl CoA lyase (HACL1), which is responsible for the cleavage of 2-hydroxyphytanoyl-CoA into pristanal and formyl-CoA. Hacl1 deficient mice do not present with a severe phenotype, unlike mice deficient in other α-oxidation enzymes such as phytanoyl-CoA hydroxylase deficiency (Refsum disease) in which neuropathy and ataxia are present.