AIMC Topic: Mass Spectrometry

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An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet.

The Analyst
Cancer is one of the most serious health problems worldwide. Because cancer has no specific symptoms in its early stages, it is often not diagnosed until it is in advanced stages, reducing the likelihood of successful treatment. Therefore, early diag...

Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Journal of agricultural and food chemistry
Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples (genotype-location-year combinations) from Cali...

A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models.

Scientific data
Training machine learning models for tasks such as de novo sequencing or spectral clustering requires large collections of confidently identified spectra. Here we describe a dataset of 2.8 million high-confidence peptide-spectrum matches derived from...

Autonomous mobile robots for exploratory synthetic chemistry.

Nature
Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making. Most autonomous laboratories involve bespoke automated equipment, and reaction outcomes are ofte...

Identification of novel hypertension biomarkers using explainable AI and metabolomics.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND: The global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organization's Qatar Hypertension Profile for 2023, around 33% of adults are affected by hypertension. This ...

Utilization of a natural language processing-based approach to determine the composition of artifact residues.

BMC bioinformatics
BACKGROUND: Determining the composition of artifact residues is a central problem in ancient residue metabolomics. This is done by comparing mass spectral features in common with an experimental artifact and an ancient artifact (standard method). Whi...

Untargeted Metabolomics and Soil Community Metagenomics Analyses Combined with Machine Learning Evaluation Uncover Geographic Differences in Ginseng from Different Locations.

Journal of agricultural and food chemistry
C.A. Meyer, known as the "King of Herbs," has been used as a nutritional supplement for both food and medicine with the functions of relieving fatigue and improving immunity for thousands of years in China. In agricultural planting, soil environment...

Modelling protein complexes with crosslinking mass spectrometry and deep learning.

Nature communications
Scarcity of structural and evolutionary information on protein complexes poses a challenge to deep learning-based structure modelling. We integrate experimental distance restraints obtained by crosslinking mass spectrometry (MS) into AlphaFold-Multim...

Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases.

Pharmacology & therapeutics
Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanis...

Leveraging Supervised Machine Learning Algorithms for System Suitability Testing of Mass Spectrometry Imaging Platforms.

Journal of proteome research
Quality control and system suitability testing are vital protocols implemented to ensure the repeatability and reproducibility of data in mass spectrometry investigations. However, mass spectrometry imaging (MSI) analyses present added complexity sin...