AIMC Topic: Metabolomics
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Prediction and collection of protein-metabolite interactions.
Briefings in bioinformatics
Sep 2, 2021
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
Jun 1, 2021
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
Apr 15, 2021
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
Mar 22, 2021
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
Mar 22, 2021
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
Jan 23, 2021
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)
Dec 30, 2020
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...
Uncovering the key dimensions of high-throughput biomolecular data using deep learning.
Nucleic acids research
Jun 4, 2020
Recent advances in high-throughput single-cell RNA-seq have enabled us to measure thousands of gene expression levels at single-cell resolution. However, the transcriptomic profiles are high-dimensional and sparse in nature. To address it, a deep lea...
A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning.
Journal of Alzheimer's disease : JAD
Jan 1, 2020
BACKGROUND: Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer's disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess...
FOBI: an ontology to represent food intake data and associate it with metabolomic data.
Database : the journal of biological databases and curation
Jan 1, 2020
Nutrition research can be conducted by using two complementary approaches: (i) traditional self-reporting methods or (ii) via metabolomics techniques to analyze food intake biomarkers in biofluids. However, the complexity and heterogeneity of these t...
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