AIMC Topic: Metabolic Diseases
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Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.
Scientific reports
Aug 6, 2021
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 ...
Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques.
Scientific reports
Jan 21, 2021
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...
A network-based machine-learning framework to identify both functional modules and disease genes.
Human genetics
Jan 7, 2021
Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene ide...
Neurodegenerative Diseases
Supervised Machine Learning
Gene Regulatory Networks
Gastrointestinal Diseases
Metabolic Diseases
Amino Acid Sequence
Protein Interaction Mapping
Humans
Terminology as Topic
Computational Biology
Musculoskeletal Diseases
Immune System Diseases
Mental Disorders
Predictive Value of Tests
Neoplasms
Metabolic Networks and Pathways
Systems metabolomics: from metabolomic snapshots to design principles.
Current opinion in biotechnology
Apr 8, 2020
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...
Application of Machine Learning to Identify Clustering of Cardiometabolic Risk Factors in U.S. Adults.
Diabetes technology & therapeutics
Apr 10, 2019
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
Apr 4, 2019
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...
Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling.
Scientific reports
Jan 31, 2019
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 ...
Circulating betatrophin in relation to metabolic, inflammatory parameters, and oxidative stress in patients with type 2 diabetes mellitus.
Diabetes & metabolic syndrome
Nov 3, 2018
AIMS: Recently, it was suggested that betatrophin has a role in controlling pancreatic β cell proliferation and lipid metabolism, however, its role in human subjects has not been established yet. The predicting role of betatrophin and MDA along with ...
Phase Relationship between DLMO and Sleep Onset and the Risk of Metabolic Disease among Normal Weight and Overweight/Obese Adults.
Journal of biological rhythms
Dec 20, 2017
Circadian misalignment is hypothesized to contribute to increased diabetes and obesity among shift workers and individuals with late sleep timing. Accordingly, the goal of our study was to identify-among normal and overweight/obese adults-association...
CLASH: Complementary Linkage with Anchoring and Scoring for Heterogeneous biomolecular and clinical data.
BMC medical informatics and decision making
Jul 25, 2016
BACKGROUND: The study on disease-disease association has been increasingly viewed and analyzed as a network, in which the connections between diseases are configured using the source information on interactome maps of biomolecules such as genes, prot...
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