Topic: AI-Driven EV Biomarker Discovery

Extracellular vesicle (EV) research continues to expand its influence across a broad spectrum of biomedical fields. EVs have garnered growing interest over the past decades due to their role in intercellular communication and their influence on key biological processes such as metabolism and immune response. Their potential as ideal biomarkers has made them particularly attractive for diagnostic and therapeutic applications.

Artificial intelligence (AI) is revolutionizing biomedical research by enabling rapid, comprehensive analysis of complex, high-dimensional data, revealing patterns that were previously inaccessible through traditional methods. In EV research, AI methodologies span supervised learning algorithms - such as random forests and support vector machines - for classifying EV content and origin, as well as unsupervised learning, clustering, reinforcement learning, and natural language processing, further expanding the analytical toolkit available to researchers.

In the context of EV-based studies, AI enhances biomarker discovery, improves diagnostic precision, and supports the development of personalized therapeutic strategies. The integration of multi-omics data with automated image and text analysis is transforming scientific investigation, fostering cross-disciplinary innovation and driving a paradigm shift in biomedical methodology. This scope includes original research articles, reviews, and cutting-edge methodological studies focused on the intersection of AI and EVs, including but not limited to:

● AI-driven biomarker discovery from EVs;

● Deep learning approaches for classifying EV-derived molecular signatures;

● Integration of AI and EVs in diagnostics and liquid biopsy applications;

● Machine learning algorithms for predicting early disease onset and treatment response based on EV content;

● EV-AI in early disease detection and mechanistic insights into disease biology;
● AI-based analysis of EV morphology and size distribution from microscopy and imaging data;

● Multi-omics integration using AI to identify EV-mediated signaling pathways;

● Predictive modeling of EV biology using systems biology and machine learning frameworks;

● AI-assisted assessment of EV heterogeneity and subpopulation characterizatio.

Artificial intelligence, biomarkers, machine learning, multi-omics, natural language processing, algorithms, unsupervised learning, EV classification