AIMC Topic: Metabolomics
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Advances in mass spectrometry imaging for spatial cancer metabolomics.
Mass spectrometry reviews
Sep 6, 2022
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progr...
Learning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies.
BMC bioinformatics
Sep 1, 2022
BACKGROUND: Presently, there is a wide variety of classification methods and deep neural network approaches in bioinformatics. Deep neural networks have proven their effectiveness for classification tasks, and have outperformed classical methods, but...
Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics.
BMC bioinformatics
Jul 10, 2022
BACKGROUND: Metabolomics is a primary omics topic, which occupies an important position in both clinical applications and basic researches for metabolic signatures and biomarkers. Unfortunately, the relevant studies are challenged by the batch effect...
MB-SupCon: Microbiome-based Predictive Models via Supervised Contrastive Learning.
Journal of molecular biology
Jun 28, 2022
Human microbiome consists of trillions of microorganisms. Microbiota can modulate the host physiology through molecule and metabolite interactions. Integrating microbiome and metabolomics data have the potential to predict different diseases more acc...
Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics.
Applied microbiology and biotechnology
May 12, 2022
Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the primary sources of functional food and nutrition, and their pharmacological products are used for healthy...
Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics.
Analytical chemistry
Mar 15, 2022
Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS, which uses machine learning based on a convoluted neural network to reduce the number and fraction of false peaks. NeatMS comes w...
A non-invasive method for concurrent detection of early-stage women-specific cancers.
Scientific reports
Feb 10, 2022
We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovary across ...
How paediatric drug development and use could benefit from OMICs: A c4c expert group white paper.
British journal of clinical pharmacology
Feb 9, 2022
The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates and infants, is limited by a paucity of good-quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OM...
Quantitative structure retention relationship (QSRR) modelling for Analytes' retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance.
Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
Jan 19, 2022
In metabolomics, retention prediction methods have been developed based on the structural and physicochemical characteristics of analytes. Such methods employ regression models, harnessing machine learning algorithms mapping experimentally derived re...
Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology.
Journal of the American Society of Nephrology : JASN
Jan 11, 2022
BACKGROUND: Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD b...
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