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Genomic analyses in cancer have been enormously impactful, leading to the identification of
driver mutations and development of targeted therapies. But the functions of the vast majority …

Mass spectrometry is unmatched in its versatility for studying practically any aspect of the
proteome. Because the foundations of mass spectrometry-based proteomics are complex …

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 …

Natural language processing (NLP) is a field of computer science concerned with automated
text and language analysis. In recent years, following a series of breakthroughs in deep and …

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

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

The data‐independent acquisition mass spectrometry (DIA‐MS) has rapidly evolved as a
powerful alternative for highly reproducible proteome profiling with a unique strength of …

A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly
and efficiently. Image-based, high-throughput phenoty** has a number of advantages …