AIMC Topic: Mass Spectrometry

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massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.

Bioinformatics (Oxford, England)
MOTIVATION: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computat...

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...

Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.

Methods in molecular biology (Clifton, N.J.)
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage...

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 ...

Artificial intelligence for proteomics and biomarker discovery.

Cell systems
There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have no...

Large-scale mass spectrometry data combined with demographics analysis rapidly predicts methicillin resistance in Staphylococcus aureus.

Briefings in bioinformatics
BACKGROUND: A mass spectrometry-based assessment of methicillin resistance in Staphylococcus aureus would have huge potential in addressing fast and effective prediction of antibiotic resistance. Since delays in the traditional antibiotic susceptibil...

On the feasibility of deep learning applications using raw mass spectrometry data.

Bioinformatics (Oxford, England)
SUMMARY: In recent years, SWATH-MS has become the proteomic method of choice for data-independent-acquisition, as it enables high proteome coverage, accuracy and reproducibility. However, data analysis is convoluted and requires prior information and...

Advancements within Modern Machine Learning Methodology: Impacts and Prospects in Biomarker Discovery.

Current medicinal chemistry
BACKGROUND: The adoption of biomarkers as part of high-throughput, complex microarray or sequencing data has necessitated the discovery and validation of these data through machine learning. Machine learning has remained a fundamental and indispensab...

[Selenium speciation in watermelon by g-CN enrichment combined with capillary electrophoresis-inductively coupled plasma-mass spectrometry].

Se pu = Chinese journal of chromatography
Selenium is one of the essential trace elements in the human body, and it plays a critical role in human health. In this work, 2.0 g melamine was placed in an alumina crucible, which was heated in a box-type resistance furnace for 2 h at 600 ℃, at th...

Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations.

Bioinformatics (Oxford, England)
MOTIVATION: Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, wh...