AIMC Topic: Proteomics
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Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes.
Molecular & cellular proteomics : MCP
Feb 5, 2025
Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteom...
An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine.
EMBO molecular medicine
Feb 3, 2025
Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagnosis can be challenging due to partially overlapping histological features. By proteomic profiling of...
PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.
Journal of proteome research
Jan 22, 2025
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, d...
A graph neural network approach for hierarchical mapping of breast cancer protein communities.
BMC bioinformatics
Jan 21, 2025
BACKGROUND: Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take info...
iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.
Nature communications
Jan 21, 2025
Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files fro...
Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.
Biomolecules
Jan 17, 2025
Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-ba...
Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment.
Gut
Jan 17, 2025
Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current...
Machine learning and multi-omics in precision medicine for ME/CFS.
Journal of translational medicine
Jan 14, 2025
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder that defies simplistic characterisation. Traditional approaches to diagnosing and treating ME/CFS have often fallen short due to the condition's hetero...
Analysis and validation of serum biomarkers in brucellosis patients through proteomics and bioinformatics.
Frontiers in cellular and infection microbiology
Jan 13, 2025
INTRODUCTION: This study aims to utilize proteomics, bioinformatics, and machine learning algorithms to identify diagnostic biomarkers in the serum of patients with acute and chronic brucellosis.
MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma.
Journal of proteome research
Jan 7, 2025
Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecif...
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