AIMC Topic: Metabolomics
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Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2018
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide ex...
OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets.
Proteomics
Oct 1, 2017
The availability of user-friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://ww...
Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data.
Studies in health technology and informatics
Jan 1, 2017
Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to fully account for interactions between metabolites, rely on a priori selec...
Fast metabolite identification with Input Output Kernel Regression.
Bioinformatics (Oxford, England)
Jun 15, 2016
MOTIVATION: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching t...
Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data.
Bioanalysis
Jan 1, 2015
BACKGROUND: Artificial Neural Networks (ANN) are extensively used to model 'omics' data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization.
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