AI Medical Compendium Topic

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Metabolomics

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Metabolite discovery: Biochemistry's scientific driver.

Cell metabolism
Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase an...

Food Metabolomics.

Journal of nutritional science and vitaminology
Foods contain not only nutrients but also a wide variety of components related to flavor and functionality. Many studies have been conducted on the components of foods. Quantitative analysis has been used to assess compounds in food such as sugars, a...

Prediction and collection of protein-metabolite interactions.

Briefings in bioinformatics
Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors ...

Machine learning modeling of the effects of media formulated with various yeast extracts on heterologous protein production in Escherichia coli.

MicrobiologyOpen
In microbial manufacturing, yeast extract is an important component of the growth media. The production of heterologous proteins often varies because of the yeast extract composition. To identify why this reduces protein production, the effects of ye...

Machine Learning-Driven Metabolomic Evaluation of Cerebrospinal Fluid: Insights Into Poor Outcomes After Aneurysmal Subarachnoid Hemorrhage.

Neurosurgery
BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) is associated with a high mortality and poor neurologic outcomes. The biologic underpinnings of the morbidity and mortality associated with aSAH remain poorly understood.

Deep learning in systems medicine.

Briefings in bioinformatics
Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features nee...

Deep learning meets metabolomics: a methodological perspective.

Briefings in bioinformatics
Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improvi...

Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data.

GigaScience
BACKGROUND: previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software.

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...