AIMC Topic: Proteomics

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Graph neural networks learn emergent tissue properties from spatial molecular profiles.

Nature communications
Tissue phenotypes, such as metabolic states, inflammation, and tumor properties, emerge from both molecular states and spatial cell organization. Spatial molecular assays provide an unbiased view of tissue architecture, enabling phenotype prediction....

Exosomal biomarkers in cancer: Insights from Multi-OMIC approaches.

Clinica chimica acta; international journal of clinical chemistry
Extracellular vesicles, particularly exosomes, are emerging as powerful tools in cancer research due to their role in intercellular communication and their capacity to reflect the molecular composition of their originating cells. Multi-omic approache...

Differentiating Gastric Cancers from Acid Peptic Diseases through Integrative Targeted Proteomics and Machine Learning Approaches.

Journal of proteome research
Gastric cancers (GCs) are often diagnosed in advanced stages owing to nonspecific early symptoms resembling Acid Peptic Diseases (APDs). Despite recent efforts, a simple, liquid biopsy-based multiprotein panel prediagnostic assay capable of different...

A New Approach to Large Multiomics Data Integration.

Analytical chemistry
Data reduction and data mining are common practices for handling large-scale data from wide-ranging sources, but high-dimensional omics and imaging data sets present difficult challenges for feature extraction and data mining due to the large number ...

Plasma Proteomic High-Performance Biomarkers for Early Diagnosis of Colorectal Cancer.

Journal of proteome research
Colorectal cancer (CRC) is a major global health challenge due to its high incidence, mortality, and low rate of early detection. Early diagnosis, targeting precancerous lesions (advanced adenomas) and early stage CRC (Tis and T1), is critical for im...

Potential of Proteomics in Forensic Phenotyping: A Focus on Biological Sex Estimation.

Journal of proteome research
Forensic DNA analysis is well established for phenotyping, providing valuable investigative leads. Proteomics, the large-scale study of proteins, is emerging as a complementary tool to DNA analysis, particularly for enhancing the evidential value of ...

Deep learning-driven proteomics analysis for gene annotation in the renin-angiotensin system.

European journal of pharmacology
The renin-angiotensin system (RAS) is central to cardiovascular diseases such as hypertension and cardiomyopathy, yet the functions of many RAS genes remain unclear. This study developed a multi-label deep learning model to systematically annotate RA...

IQUP identifies quantitatively unreliable spectra with machine learning for isobaric labeling-based proteomics.

Scientific reports
Mass spectrometry‑based proteomics using isobaric labeling technology has become popular for proteomic quantitation. Existing approaches rely on the mechanism of target-decoy search and false discovery rate control to examine whether a peptide-spectr...

Inflammation and B cell activation define a plasma proteome signature predicting tuberculosis in people with HIV.

mBio
Improved biomarkers for predicting progression to active tuberculosis (TB) are urgently needed, especially in people with HIV, who are at elevated risk. We used high-throughput plasma proteomics and machine learning to identify signatures associated ...

Mapping a interactome by crosslinking mass spectrometry and machine learning.

mBio
, a widespread human parasite, persists in hosts through complex molecular interactions. Protein-protein interactions (PPIs) underpin essential biological processes, including parasite-host interactions and cellular invasion. Herein, we utilized adva...