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Proteomics

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Recent advances in omics and the integration of multi-omics in osteoarthritis research.

Arthritis research & therapy
Osteoarthritis (OA) is a complex disorder driven by the combination of environmental and genetic factors. Given its high global prevalence and heterogeneity, developing effective and personalized treatment methods is crucial. This requires identifyin...

Foodomics approaches: New insights in phenolic compounds analysis.

Food research international (Ottawa, Ont.)
Fruits, vegetables, and plant-based foods contain several bioactive substances such as phenolic compounds (PCs), that are plant secondary metabolites with attributed health properties. The study of the metabolic pathways of PCs, including those relat...

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-...

[Serum proteomics and machine learning unveil new diagnostic biomarkers for tuberculosis in adolescents and young adults].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Adolescents and young adults (AYAs) are one of the major populations susceptible to tuberculosis. However, little is known about the unique characteristics and diagnostic biomarkers of tuberculosis in this population. In this study, 81 AYAs were recr...

Identification of pivotal genes and regulatory networks associated with SAH based on multi-omics analysis and machine learning.

Scientific reports
Subarachnoid hemorrhage (SAH) is a disease with high mortality and morbidity, and its pathophysiology is complex but poorly understood. To investigate the potential therapeutic targets post-SAH, the SAH-related feature genes were screened by the comb...

Deconvolution of cell types and states in spatial multiomics utilizing TACIT.

Nature communications
Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning increasingly plays a role, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in...

Deep Learning-Based Prediction of Decoy Spectra for False Discovery Rate Estimation in Spectral Library Searching.

Journal of proteome research
With the advantage of extensive coverage, predicted spectral libraries are becoming an attractive alternative in proteomic data analysis. As a popular false discovery rate estimation method, target decoy search has been adopted in library search work...

Uncovering hepatic transcriptomic and circulating proteomic signatures in MASH: A meta-analysis and machine learning-based biomarker discovery.

Computers in biology and medicine
BACKGROUND: Metabolic-associated steatohepatitis (MASH), the progressive form of metabolic-associated steatotic liver disease (MASLD), poses significant risks for liver fibrosis and cardiovascular complications. Despite extensive research, reliable b...

Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks.

NPJ systems biology and applications
The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a...

Application of Bioinformatics and Machine Learning Tools in Food Safety.

Current nutrition reports
PURPOSE OF REVIEW: Food safety is a fundamental challenge in public health and sustainable development, facing threats from microbial, chemical, and physical contamination. Innovative technologies improve our capacity to detect contamination early an...