Artificial Intelligence in Molecular Medicine | New England Journal of Medicine Skip to main
content The New England Journal of Medicine homepage Advanced Search SEARCH …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass
spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we …

Abstract Machine learning and in particular deep learning (DL) are increasingly important in
mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention …

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 …

This review provides a brief overview of the development of data‐independent acquisition
(DIA) mass spectrometry‐based proteomics and selected DIA data analysis tools. Various …

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

In mass-spectrometry-based proteomics, the identification and quantification of peptides and
proteins heavily rely on sequence database searching or spectral library matching. The lack …

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful
technology for high-throughput, accurate, and reproducible quantitative proteomics. This …

The innovation of biotechnologies has allowed the accumulation of omics data at an
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …