AIMC Topic: Mass Spectrometry
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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...
Automated sample preparation with SP3 for low-input clinical proteomics.
Molecular systems biology
Jan 1, 2020
High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh-frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot ...
Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches.
Briefings in bioinformatics
Nov 27, 2019
Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Und...
[Research progress of feature selection and machine learning methods for mass spectrometry-based protein biomarker discovery].
Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Sep 25, 2019
With the development of mass spectrometry technologies and bioinformatics analysis algorithms, disease research-driven human proteome project (HPP) is advancing rapidly. Protein biomarkers play critical roles in clinical applications and the biomarke...
Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning.
Chemical communications (Cambridge, England)
Jan 10, 2019
Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation...
Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2019
CITRUS is a supervised machine learning algorithm designed to analyze single cell data, identify cell populations, and identify changes in the frequencies or functional marker expression patterns of those populations that are significantly associated...
IMass Time: The Future, in Future!
Omics : a journal of integrative biology
Nov 1, 2018
Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been...
Deep learning for tumor classification in imaging mass spectrometry.
Bioinformatics (Oxford, England)
Apr 1, 2018
MOTIVATION: Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to f...
[Direct determination for 22 kinds of elements in umbilical cord serum by ICP-MS].
Wei sheng yan jiu = Journal of hygiene research
Sep 1, 2017
OBJECTIVE: A method for analysis of 22 kinds of elements in umbilical cord serum by inductively coupled plasma mass spectrometry( ICP-MS) was developed.
[Determination of polycyclic aromatic hydrocarbons in girls and association between polycyclic aromatic hydrocarbons exposure and puberty timing].
Wei sheng yan jiu = Journal of hygiene research
Sep 1, 2017
OBJECTIVE: To develop amethod for simultaneous determination of four metabolites of polycyclic aromatic hydrocarbons( PAHs), and to study the association between puberty timing of girls and PAHs exposure levels in Chongqing.
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