AI Medical Compendium Journal:
Journal of proteomics

Showing 1 to 10 of 13 articles

Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center.

Journal of proteomics
This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-...

Integrative approaches for predicting protein network perturbations through machine learning and structural characterization.

Journal of proteomics
Chromatin remodeling complexes, such as the Saccharomyces cerevisiae INO80 complex, exemplify how dynamic protein interaction networks govern cellular function through a balance of conserved structural modules and context-dependent functional partner...

Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning.

Journal of proteomics
Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the connection between sequ...

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data.

Journal of proteomics
Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. ...

MSSort-DIA: A deep learning classification tool of the peptide precursors quantified by OpenSWATH.

Journal of proteomics
OpenSWATH is an analysis toolkit commonly used for data independent acquisition (DIA). Although the output of OpenSWATH is controlled at 1% false discovery rate (FDR), the output report still contains many peptide precursors with low similarity fragm...

pValid 2: A deep learning based validation method for peptide identification in shotgun proteomics with increased discriminating power.

Journal of proteomics
Tandem mass spectrometry has been the principal method in shotgun proteomics for peptide and protein identification. However, incorrect identifications reported by proteome search engines are still unknown, and further validation methods are needed. ...

Deep learning for peptide identification from metaproteomics datasets.

Journal of proteomics
Metaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled wi...

Deep learning embedder method and tool for mass spectra similarity search.

Journal of proteomics
Spectral similarity calculation is widely used in protein identification tools and mass spectra clustering algorithms while comparing theoretical or experimental spectra. The performance of the spectral similarity calculation plays an important role ...

PB-Net: Automatic peak integration by sequential deep learning for multiple reaction monitoring.

Journal of proteomics
Mass spectrometry (MS) based proteomics has become an indispensable component of modern molecular and cellular biochemistry analysis. Multiple reaction monitoring (MRM) is one of the most well-established MS techniques for molecule detection and quan...

UniprotR: Retrieving and visualizing protein sequence and functional information from Universal Protein Resource (UniProt knowledgebase).

Journal of proteomics
UniprotR is a software package designed to easily retrieve, cluster and visualize protein data from UniProt knowledgebase (UniProtKB) using R language. The package is implemented mainly to process, parse and illustrate proteomics data in a handy and ...