AIMC Topic: Mass Spectrometry

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Mass spectrometry-based proteomics data from thousands of HeLa control samples.

Scientific data
Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable am...

Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation.

Scientific reports
Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of dr...

Human robotic surgery with intraoperative tissue identification using rapid evaporation ionisation mass spectrometry.

Scientific reports
Instantaneous, continuous, and reliable information on the molecular biology of surgical target tissue could significantly contribute to the precision, safety, and speed of the intervention. In this work, we introduced a methodology for chemical tiss...

Enabling Lipidomic Biomarker Studies for Protected Populations by Combining Noninvasive Fingerprint Sampling with MS Analysis and Machine Learning.

Journal of proteome research
Triacylglycerols and wax esters are two lipid classes that have been linked to diseases, including autism, Alzheimer's disease, dementia, cardiovascular disease, dry eye disease, and diabetes, and thus are molecules worthy of biomarker exploration st...

De novo design of high-affinity binders of bioactive helical peptides.

Nature
Many peptide hormones form an α-helix on binding their receptors, and sensitive methods for their detection could contribute to better clinical management of disease. De novo protein design can now generate binders with high affinity and specificity ...

Deep graph convolutional network for small-molecule retention time prediction.

Journal of chromatography. A
The retention time (RT) is a crucial source of data for liquid chromatography-mass spectrometry (LCMS). A model that can accurately predict the RT for each molecule would empower filtering candidates with similar spectra but differing RT in LCMS-base...

Application of metabolomics in diagnostics and differentiation of meningitis: A narrative review with a critical approach to the literature.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Due to its high mortality rate associated with various life-threatening sequelae, meningitis poses a vital problem in contemporary medicine. Numerous algorithms, many of which were derived with the aid of artificial intelligence, were brought up in a...

Bioactivation and reactivity research advances - 2022 year in review‡.

Drug metabolism reviews
With the 50th year mark since the launch of journal, the field of drug metabolism and bioactivation has advanced exponentially in the past decades (Guengerich 2023).This has, in a major part, been due to the continued advances across the whole spect...

Mapping HDX-MS Data to Protein Conformations through Training Ensemble-Based Models.

Journal of the American Society for Mass Spectrometry
An original approach that adopts machine learning inference to predict protein structural information using hydrogen-deuterium exchange mass spectrometry (HDX-MS) is described. The method exploits an in-house optimization program that increases the r...

Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification.

Food chemistry
The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spect...