ADVANCED COMPUTATIONAL MODELING FOR PREDICTING THERAPEUTIC OUTCOMES IN OVARIAN CANCER: INTEGRATING ARTIFICIAL INTELLIGENCE AND SYSTEMS BIOLOGY

D. Jothieswari, Sahil Thakre, Selvakumar Sambandan, Deepak Saini, Anil Kumar, Sudhahar Dharmalingam, Yash Srivastav, Kamaxi Mehta and Konda V. V. S. Krishna*

ABSTRACT

Background: Ovarian cancer remains one of the most lethal gynecological malignancies, with high heterogeneity and late-stage diagnosis contributing to poor prognosis. Conventional therapeutic strategies are often limited by drug resistance and variability in patient responses. Recent advancements in computational modeling, including artificial intelligence (AI) and systems biology, offer novel avenues for understanding disease mechanisms, predicting therapeutic outcomes, and personalizing treatments. Objectives: This review aims to.
1. Explore the role of AI and systems biology in ovarian cancer research.
2. Discuss their applications in biomarker discovery, drug response prediction, and personalized therapy.
3. Highlight challenges and future directions for integrating computational approaches into clinical practice.
Methods: A comprehensive literature review was conducted to analyze recent advancements in AI and systems biology, focusing on their synergistic applications in ovarian cancer. Key topics include machine learning algorithms, network biology, tumor microenvironment modeling, and real-world examples of clinical applications.
Key Findings
1. AI Applications: Machine learning and deep learning have been instrumental in biomarker discovery, drug response prediction, and multi-omics data integration. Neural networks, such as convolutional and recurrent models, enhance imaging, genomics, and proteomics analysis.
2. Systems Biology: Network biology and multi-omics integration provide insights into cancer pathways, aiding in target identification and drug repurposing. Computational models of the tumor microenvironment elucidate cell-cell interactions and immune responses.
3. Integration: Combining AI and systems biology synergistically enhances model precision, enabling dynamic and patient-specific treatment planning. Case studies demonstrate success in predicting effective drug combinations and personalizing treatment plans.
4. Challenges: Data harmonization, computational complexity, and the need for clinical validation and ethical frameworks remain significant barriers.
Future Directions: Emerging technologies, such as quantum computing and federated learning, promise to further advance the field by enabling real-time, dynamic modeling and preserving patient data privacy. Bridging the gap between computational predictions and clinical implementation will require interdisciplinary collaboration and robust validation frameworks. Conclusion: Advanced computational modeling is revolutionizing ovarian cancer research and treatment, offering unprecedented opportunities for precision medicine. While challenges remain, the integration of AI and systems biology has the potential to significantly improve therapeutic outcomes, transforming patient care and accelerating progress in oncology.

Keywords: Ovarian cancer, artificial intelligence, systems biology, computational modeling, personalized medicine, precision oncology.