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Metabolic Diseases

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Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling.

Scientific reports
PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called "Iterative ...

Application of Machine Learning to Identify Clustering of Cardiometabolic Risk Factors in U.S. Adults.

Diabetes technology & therapeutics
The aim of this study is to compare some machine learning methods with traditional statistical parametric analyses using logistic regression to investigate the relationship of risk factors for diabetes and cardiovascular (cardiometabolic risk) for U...

Using Health Chatbots for Behavior Change: A Mapping Study.

Journal of medical systems
This study conducts a mapping study to survey the landscape of health chatbots along three research questions: What illnesses are chatbots tackling? What patient competences are chatbots aimed at? Which chatbot technical enablers are of most interest...

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview.

Endocrinology and metabolism (Seoul, Korea)
Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles hav...

Systems metabolomics: from metabolomic snapshots to design principles.

Current opinion in biotechnology
Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapsh...

A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data.

Bioinformatics (Oxford, England)
MOTIVATION: Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Compu...

Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques.

Scientific reports
The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potenti...

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.

Scientific reports
Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy ...