AIMC Topic: Mass Spectrometry

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Adapting a Low-Cost and Open-Source Commercial Pipetting Robot for Nanoliter Liquid Handling.

SLAS technology
Low-volume liquid handling capabilities in bioanalytical workflows can dramatically improve sample processing efficiency and reduce reagent costs, yet many commercial nanoliter liquid handlers cost tens of thousands of dollars or more. We have succes...

Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification.

Nature communications
Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these ...

MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Despite the availability of several pre-processing software, poor peak integration remains a prevalent problem in untargeted metabolomics data generated using liquid chromatography high-resolution mass spectrometry (LC-MS). As a result,...

Using Deep Learning to Extrapolate Protein Expression Measurements.

Proteomics
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
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
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
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
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
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
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...