Application of machine learning in understanding atherosclerosis: Emerging insights

Area of Science:

• Cardiovascular Biology
• Computational Biology
• Biomedical Data Science

Background:

• Biological processes integrate molecular signaling networks crucial for maintaining homeostasis.
• Dysfunctional biological processes disrupt homeostasis, leading to diseases like atherosclerosis.
• Understanding atherosclerosis requires analyzing complex, heterogeneous biological data.

Purpose of the Study:

• To review current applications of machine learning in atherosclerosis research.
• To provide insights into atherosclerotic plaque formation and progression.
• To highlight the role of machine learning in understanding cardiovascular disease.

Main Methods:

• Utilizing machine learning frameworks to analyze large biological datasets.
• Applying machine learning for patient risk stratification.
• Employing machine learning in multi-modality image processing and biomarker discovery for atherosclerotic plaque.

Main Results:

• Machine learning facilitates the analysis of complex biological circuitry.
• Machine learning methods are increasingly used in cardiovascular disease research.
• These methods offer insights into atherosclerotic plaque formation and progression.

Conclusions:

• Machine learning is a powerful tool for deciphering complex biological systems.
• Advancements in data acquisition and analysis enhance our understanding of atherosclerosis.
• Machine learning applications are crucial for understanding atherosclerotic plaque progression in cardiovascular disease.