Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …

Medical data has become an essential element for industrial healthcare. The growth in
medical data is accompanied by the need to process it in a secure manner. As the …

This review “teaches” researchers how to make their lackluster proteomics data look really
impressive, by applying an inappropriate but pervasive strategy that selects features in a …

(MS) to the forefront in the realm of analytical chemistry. A broad spectrum of hydrophilic and
hydrophobic compounds can now be ionized, from low molecular weight analytes to high …

In recent years, the wide application of artificial intelligence (AI) has dramatically improved
the work efficiency of clinicians and reduced their workload. This review provides a glance at …

Matrix-assisted laser desorption/ionization (MALDI) imaging is an emergent technology that
has been increasingly adopted in cancer research. MALDI imaging is capable of providing …

Background Current methods to identify, classify, and predict tumor behavior mostly rely on
histology, immunohistochemistry, and molecular determinants. However, better predictive …

Mass spectrometry imaging (MSI) is an intuitive and multidimensional analysis method that
provides spatial information on molecules in tissues or cells; it is widely used in biomedical …

The fields of proteomics and machine learning are both large disciplines, each producing
well over 5,000 publications per year. However, studies combining both fields are still …