AIMC Topic: Proteomics

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Machine learning-based proteomics profiling of ALS identifies downregulation of RPS29 that maintains protein homeostasis and STMN2 level.

Communications biology
Amyotrophic lateral sclerosis (ALS) is a devastating motor neuron disease. The molecular understanding of ALS is hampered by the lack of experimental models recapitulating disease heterogeneity and analytical framework integrating multi-omics dataset...

Urinary Complement proteome strongly linked to diabetic kidney disease progression.

Nature communications
Diabetic kidney disease (DKD) progression is not well understood. Using high-throughput proteomics, biostatistical, pathway and machine learning tools, we examine the urinary Complement proteome in two prospective cohorts with type 1 or 2 diabetes an...

Plasma proteomics for biomarker discovery in childhood tuberculosis.

Nature communications
Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Current TB diagnostic assays have poor performance in children, thus a global priority is the identificati...

Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm.

Journal of proteome research
A major challenge in mass-spectrometry-based metaproteomics is accurately identifying and quantifying biological functions across the full taxonomic lineage of microorganisms. This issue stems from what we refer to as the "shared confidently identifi...

Evaluation of the False Discovery Rate in Library-Free Search by DIA-NN Using Human Proteome.

Journal of proteome research
Recently, deep-learning-based spectral libraries have gained increasing attention. Several data-independent acquisition (DIA) software tools have integrated this feature, known as a library-free search, thereby making DIA analysis more accessible. H...

Library-based virtual match-between-runs quantification in GlyPep-Quant improves site-specific glycan identification.

Nature communications
Glycosylation changes are closely related to various diseases, including cancer. The quantitative analysis of site-specific glycans at proteomics scale remains challenging due to low glycopeptide spectra interpretation. Here, we present GlyPep-Quant,...

Analysis of Protein-Protein Interactions in CC125 by Co-Fractionation Mass Spectrometry.

Journal of proteome research
, a unicellular eukaryotic green alga, is an important biological model. Previous studies on protein complexes in have primarily focused on photosynthesis and ciliary movement, while understanding the overall protein complex network is still limited...

Machine Learning and DIA Proteomics Reveal New Insights into Carbapenem Resistance Mechanisms in .

Journal of proteome research
The emergence of Carbapenem-resistant (CRKP) represents a major public health concern, primarily driven by its ability to evade a wide range of antibiotics. Despite extensive genomic studies, proteomic insights into antibiotic resistance mechanisms ...

Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer's disease in cerebrospinal fluid.

Alzheimer's research & therapy
BACKGROUND: The complex pathogenesis of Alzheimer's disease (AD) has resulted in limited current biomarkers for its classification and diagnosis, necessitating further investigation into reliable universal biomarkers or combinations.

Glycoproteomics Analysis to Identify Potential Biomarkers and Investigate CD14 Monocyte Activation Mechanisms via Exosomal Proteins in Cerebral Infarction Using a Novel Glycogen-Functionalized Nanoprobe.

Analytical chemistry
Cerebral infarction (CI) is a leading cause of disability and mortality, with activated plasma monocytes and altered protein glycosylation identified as critical contributors to its pathology. However, the mechanisms linking peripheral monocyte activ...