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

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms
that allow computers to learn solving a (complex) problem from existing data. This ability can …

The growing utility of peptide retention prediction in proteomics would benefit from the
development of a universal peptide retention standard for better alignment of …

We describe the practical implementation of a new RP (pH 10− pH 2) 2D HPLC− ESI/MS
scheme for large-scale bottom-up analysis in proteomics. When compared to the common …

For the first time, the performance of a generalised artificial neural network (ANN) approach
for the prediction of 2492 chromatographic retention times (t R) is presented for a total of …

We have previously described methodology that makes it possible to genetically encode a
wide array of unnatural amino acids in both prokaryotic and eukaryotic organisms. Here, we …

Separation selectivity of C18 reversed-phase columns from different manufacturers has
been compared to evaluate the applicability of our sequence-specific retention calculator …

The modelling and prediction of reversed-phase chromatographic retention time (t R) under
gradient elution conditions for 166 pharmaceuticals in wastewater extracts is presented …

The computational generation of gradient retention time data for retrospective detection of
suspected sports do** species in postanalysis human urine sample data is presented …

A multiparametric sequence-specific model for predicting peptide electrophoretic mobility
has been developed using large-scale bottom-up proteomic CE-MS data (5%(∼ 0.8 M) …