Affiliation:
1. University Institute of Pharma Sciences, Chandigarh University, Gharuan, Mohali, 140413, India
Abstract
Abstract:
Breast cancer remains a significant global health challenge, necessitating innovative approaches
to improve treatment efficacy while minimizing side effects. This review explores the promising
advancements in breast cancer drug delivery driven by the transformative potential of bioinformatics
and Artificial Intelligence (AI). Bioinformatics plays a pivotal role in unraveling the intricate
genomic landscape of breast cancer, enabling the identification of potential drug targets and biomarkers.
The integration of multi-omics data facilitates a comprehensive understanding of the disease,
guiding personalized treatment strategies. Moreover, bioinformatics-driven approaches aid in
biomarker discovery and prediction, offering novel tools for prognosis and treatment response assessment.
AI, particularly machine learning and deep learning, has revolutionized breast cancer research.
Machine learning models empower accurate diagnosis through image analysis, improve survival
prediction, and enhance risk assessment. Deep learning algorithms, such as convolutional neural
networks, enable precise tumor detection and classification from medical imaging data, notably
mammograms and MRI scans. Additionally, natural language processing techniques facilitate the
mining of vast scientific literature, uncovering hidden insights and identifying potential drug targets.
Network-based approaches integrated with AI algorithms facilitate the identification of central proteins
as promising drug targets within complex biological networks. This review also examines AIoptimized
nanoformulations designed to enhance targeted drug delivery. AI-guided design of drugloaded
nanoparticles improves drug encapsulation efficiency, release kinetics, and site-specific delivery,
offering promising solutions to overcome the challenges of conventional drug delivery.
Publisher
Bentham Science Publishers Ltd.