The recent development of machine learning methods to identify peptides in complex mass
spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods …

Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human
proteins. However, experimental and computational challenges restrict progress in the field …

When confronted with a substance of unknown identity, researchers often perform mass
spectrometry on the sample and compare the observed spectrum to a library of previously …

Deep learning models for prediction of three key LC-MS/MS properties from peptide
sequences were developed. The LC-MS/MS properties or behaviors are indexed retention …

Introduction Data-independent acquisition (DIA) is an emerging technology for large-scale
proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has …

In shotgun proteomics, de novo peptide sequencing from tandem mass spectrometry data is
the key technology for finding new peptide or protein sequences. It has successful …

Modern proteomics–the large-scale analysis of proteins (Graves and Haystead, 2002)–
relies heavily on the analysis of complex raw experimental, time series-like data. In a typical …

Multiclass classification in machine learning allows the automation of optimal decision
making by means of learning algorithms and annotated datasets. However, it is still difficult …

As global demand for energy and materials grow while our dependence on petroleum and
fossil fuels declines, it is necessary to revolutionize the way we make new materials …

Metaproteomics are becoming widely used in microbiome research for gaining insights into
the functional state of the microbial community. Current metaproteomics studies are …