AIMC Topic: Mass Spectrometry
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Using Deep Learning to Extrapolate Protein Expression Measurements.
Proteomics
Oct 16, 2020
Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for i...
MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks.
BMC bioinformatics
Oct 7, 2020
BACKGROUND: Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algo...
Understanding mass spectrometry images: complexity to clarity with machine learning.
Biopolymers
Sep 16, 2020
The application of artificial intelligence and machine learning to hyperspectral mass spectrometry imaging (MSI) data has received considerable attention over recent years. Various methodologies have shown great promise in their ability to handle the...
A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.
Nature communications
Aug 21, 2020
Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identif...
Integrated artificial neural network analysis and functional cell based affinity mass spectrometry for screening a bifunctional activator of Ca and βAR in aconite.
Journal of pharmaceutical and biomedical analysis
Aug 1, 2020
Arrhythmia, a common heart disease, is an abnormal frequency or rhythm of heartbeat caused by the origin or conduction obstacle of the heart. Aconite (Fuzi) has been regarded as an effective cardiotonic agent in traditional Chinese medicine (TCM), bu...
A Deep Learning-Based Tumor Classifier Directly Using MS Raw Data.
Proteomics
Jul 26, 2020
Since the launch of Chinese Human Proteome Project (CNHPP) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), large-scale mass spectrometry (MS) based proteomic profiling of different kinds of human tumor samples have provided huge amount of v...
Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization.
Analytical chemistry
Jul 23, 2020
This work presents a machine learning algorithm referred to as the supervised inference of feature taxonomy from ensemble randomization (SIFTER), which supports the identification of features derived from untargeted ion mobility-mass spectrometry (IM...
The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows.
Proteomics
Jun 25, 2020
A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open ...
Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning.
Analytical chemistry
Jun 25, 2020
The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, c...
Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction.
Frontiers in immunology
Jun 23, 2020
Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adve...
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