AIMC Topic: Proteomics

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Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling.

Talanta
Multi-omics profiling integrates genomic, epigenomic, transcriptomic, and proteomic data, essential for understanding complex health and disease pathways. This review highlights the transformative potential of combining optical nanosensors with artif...

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...

Identification of a Biomarker Panel in Extracellular Vesicles Derived From Non-Small Cell Lung Cancer (NSCLC) Through Proteomic Analysis and Machine Learning.

Journal of extracellular vesicles
Antigen fingerprint profiling of tumour-derived extracellular vesicles (TDEVs) in the body fluids is a promising strategy for identifying tumour biomarkers. In this study, proteomic and immunological assays reveal significantly higher CD155 levels in...

Omics data classification using constitutive artificial neural network optimized with single candidate optimizer.

Network (Bristol, England)
Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial N...

[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...

Graph neural networks for single-cell omics data: a review of approaches and applications.

Briefings in bioinformatics
Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data ha...

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.

Bioinformatics (Oxford, England)
MOTIVATION: Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical out...

UniProt: the Universal Protein Knowledgebase in 2025.

Nucleic acids research
The aim of the UniProt Knowledgebase (UniProtKB; https://www.uniprot.org/) is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication, we describe o...

OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft.

Nucleic acids research
Patient-derived models (PDMs, particularly organoids and xenografts) are irreplaceable tools for precision medicine, from target development to lead identification, then to preclinical evaluation, and finally to clinical decision-making. So far, PDM-...

AI-driven multi-omics profiling of sepsis immunity in the digestive system.

Frontiers in immunology
Sepsis is a life-threatening systemic inflammatory syndrome characterized by a complex immune biphasic imbalance. Monitoring of immune status has not yet been implemented in clinical practice due to lack of direct therapeutic utility. Immune dysregul...