Machine Learning in Metabolic Diseases
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".
Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 6661
Special Issue Editor
2. Research Center for Environmental Health, 85764 Neuherberg, Germany
Interests: type 2 diabetes and complications; bioinformatics; machine learning; omics data integration; translational research
Special Issue Information
Dear Colleagues,
Machine learning (ML) concerns computer algorithms that improve their performance by learning from large sets of data. As a subdiscipline of artificial intelligence, ML has been developed and applied in analyzing complex data such as metabolomics to predict, identify and validate biomarkers / risk factors of metabolic diseases. The key steps of ML includes 1) data gathering and pre-processing; 2) model selection, training and testing; and 3) prediction, inference and applications. Large and high quality data enable good performance for predicting disease risk to develop efficient personalized diagnosis and therapy.
This Special Issue focuses on ML in metabolic diseases. Topics include studies aimed at developing and / or using ML in the following areas:
- Collection of data (e.g., human cohort studies, clinical studies, biobanks), and data pre-processing (e.g., harmonization / normalization of individuals molecular profiles or clinical phenotypes);
- Techniques for optimized ML model selection. ML methods may include supervised (e.g., regression and classification analysis, support vector machine and random forest) and unsupervised (e.g., clustering, principal component analysis, autoencoders and generative adversarial networks);
- Application of ML for improved prediction, identification and validation of risk factors, modifiers and / or biomarkers of metabolic diseases.
Dr. Rui Wang-Sattler
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- supervised
- unsupervised
- model selection
- training and testing
- data pre-processing
- prediction
- identification
- validation risk factors/biomarkers
- metabolic disease
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