Artificial Intelligence in Advance Angiogenesis and Inflammation Research: A Breakthrough in Disease Prediction and Therapy

P. Daniel Sundarraj ^1*, J. Raja², R. Usha³, Arul Kumar V P⁴, N Sirisha⁵, Srithar S⁶

AI-driven insights into angiogenesis and inflammation can revolutionize disease prediction, biomarker discovery, and personalized therapeutic interventions.

Abstract

Background: Angiogenesis and inflammation are fundamental biological processes, crucial to human health. Dysregulation of these processes is implicated in diseases such as cancer, cardiovascular disorders, and autoimmune diseases. Despite advances in research, the complexity of these interactions has been challenging to understand. Recent developments in artificial intelligence (AI) offer a promising approach for overcoming these challenges, especially in big data analysis. This study explores AI applications in quantifying angiogenesis and inflammatory markers and predicting disease progression. Methods: AI algorithms, including machine learning (ML) and deep learning (DL), were employed to analyze high-throughput biological data. The study applied Lasso regression for biomarker discovery, Long Short-Term Memory (LSTM) networks for predicting disease progression, and Gaussian Mixture Models (GMM) for patient subgroup identification. Image analysis using DeepLabv3+ was conducted to assess angiogenesis and inflammatory markers in histological images. Model performance was evaluated using R-squared (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. Results: The AI framework demonstrated high accuracy in predicting disease progression, with notable R² values and low MSE and RMSE values. The application of AI led to the successful identification of angiogenesis-related genes and biomarkers in various diseases, including diabetic foot ulcers and chronic obstructive pulmonary disease. AI-based image analysis also provided precise quantification of angiogenesis and inflammation, enhancing the understanding of disease mechanisms. Conclusion: AI-driven approaches significantly improve the analysis of complex biological processes, offering new insights into angiogenesis and inflammation. The high predictive accuracy of the AI models underscores their potential in clinical applications, such as personalized treatment strategies and disease monitoring. As AI continues to evolve, its integration into biomedical research will likely yield further advancements in disease prediction, diagnosis, and treatment.

Keywords: Angiogenesis, Inflammation, Artificial Intelligence, Machine Learning, Biomarkers

References

Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, baaa010. https://doi.org/10.1093/database/baaa010

Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2023). Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082. https://doi.org/10.3390/app13127082

Cimmino, G., Muscoli, S., De Rosa, S., Cesaro, A., Perrone, M. A., Selvaggio, S., ... & Coronelli, M. (2023). Evolving concepts in the pathophysiology of atherosclerosis: From endothelial dysfunction to thrombus formation through multiple shades of inflammation. Journal of Cardiovascular Medicine, 24(Supplement 2), e156-e167. https://doi.org/10.2459/JCM.0000000000001450

Cremin, C. J., Dash, S., & Huang, X. (2022). Big data: historic advances and emerging trends in biomedical research. Current Research in Biotechnology, 4, 138-151. https://doi.org/10.1016/j.crbiot.2022.02.004

Jerka, D., Bonowicz, K., Piekarska, K., Gokyer, S., Derici, U. S., Hindy, O. A., ... & Gagat, M. (2024). Unraveling endothelial cell migration: Insights into fundamental forces, inflammation, biomaterial applications, and tissue regeneration strategies. ACS Applied Bio Materials, 7(4), 2054-2069. https://doi.org/10.1021/acsabm.3c01227

Kumar Attar, R., & Komal. (2022). The emergence of Natural Language Processing (NLP) techniques in healthcare AI. In Artificial intelligence for innovative healthcare informatics (pp. 285-307). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-96569-3_14

Latifi-Navid, H., Barzegar Behrooz, A., Jamehdor, S., Davari, M., Latifinavid, M., Zolfaghari, N., ... & Sheibani, N. (2023). Construction of an Exudative Age-Related Macular Degeneration Diagnostic and Therapeutic Molecular Network Using Multi-Layer Network Analysis, a Fuzzy Logic Model, and Deep Learning Techniques: Are Retinal and Brain Neurodegenerative Disorders Related? Pharmaceuticals, 16(11), 1555. https://doi.org/10.3390/ph16111555

Leong, T. K. M., Lo, W. S., Lee, W. E. Z., Tan, B., Lee, X. Z., Lee, L. W. J. N., ... & Yeong, J. (2021). Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space. Advanced Drug Delivery Reviews, 177, 113959. https://doi.org/10.1016/j.addr.2021.113959

Liu, Z., Roberts, R. A., Lal-Nag, M., Chen, X., Huang, R., & Tong, W. (2021). AI-based language models powering drug discovery and development. Drug Discovery Today, 26(11), 2593-2607. https://doi.org/10.1016/j.drudis.2021.06.009

Patel, M. A., Knauer, M. J., Nicholson, M., Daley, M., Van Nynatten, L. R., Martin, C., ... & Fraser, D. D. (2022). Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism. Molecular Medicine, 28(1), 122. https://doi.org/10.1186/s10020-022-00548-8

Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., ... & Pedrocchi, A. L. G. (2023). Artificial intelligence for predictive biomarker discovery in immuno-oncology: A systematic review. Annals of Oncology. https://doi.org/10.1016/j.annonc.2023.10.125

Rana, M., & Bhushan, M. (2023). Machine learning and deep learning approach for medical image analysis: diagnosis to detection. Multimedia Tools and Applications, 82(17), 26731-26769. https://doi.org/10.1007/s11042-022-14305-w

Raparthi, M. (2022). AI Assisted Drug Discovery: Emphasizing Its Role in Accelerating Precision Medicine Initiatives and Improving Treatment Outcomes. Human-Computer Interaction Perspectives, 2(2), 1-10.

Rehman, A., Naz, S., & Razzak, I. (2022). Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Systems, 28(4), 1339-1371. https://doi.org/10.1007/s00530-020-00736-8

Sahu, M., Gupta, R., Ambasta, R. K., & Kumar, P. (2022). Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. Progress in Molecular Biology and Translational Science, 190(1), 57-100. https://doi.org/10.1016/bs.pmbts.2022.03.002

Sayed, N., Huang, Y., Nguyen, K., Krejciova-Rajaniemi, Z., Grawe, A. P., Gao, T., ... & Furman, D. (2021). An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nature Aging, 1(7), 598-615. https://doi.org/10.1038/s43587-021-00082-y

Subramanian, A., Zakeri, P., Mousa, M., Alnaqbi, H., Alshamsi, F. Y., Bettoni, L., ... & Carmeliet, P. (2022). Angiogenesis goes computational–The future way forward to discover new angiogenic targets? Computational and Structural Biotechnology Journal, 20, 5235-5255. https://doi.org/10.1016/j.csbj.2022.09.019

Wang, X., Meng, L., Zhang, J., Zou, L., Jia, Z., Han, X., ... & Lu, M. (2023). Identification of angiogenesis-related genes in diabetic foot ulcer using machine learning algorithms. Heliyon, 9(12).

Yang, Q., Wijerathne, H., Langston, J. C., Kiani, M. F., & Kilpatrick, L. E. (2021). Emerging approaches to understanding microvascular endothelial heterogeneity: a roadmap for developing anti-inflammatory therapeutics. International Journal of Molecular Sciences, 22(15), 7770. https://doi.org/10.3390/ijms22157770

Yousefi, B., Akbari, H., & Maldague, X. P. (2020). Detecting vasodilation as potential diagnostic biomarker in breast cancer using deep learning-driven thermomics. Biosensors, 10(11), 164. https://doi.org/10.3390/bios10110164

Zekavat, S. M., Raghu, V. K., Trinder, M., Ye, Y., Koyama, S., Honigberg, M. C., ... & Natarajan, P. (2022). Deep learning of the retina enables phenome-and genome-wide analyses of the microvasculature. Circulation, 145(2), 134-150. https://doi.org/10.1161/CIRCULATIONAHA.121.057709

Zhang, M. J., Zhang, Y., Fei, X. Y., Luo, Y., Ru, Y., Jiang, J. S., ... & Wang, R. P. (2024). Identification of Angiogenesis-Related Genes and Molecular Subtypes for Psoriasis Based on Random Forest Algorithm. Clinical and Experimental Immunology, uxae052. https://doi.org/10.1093/cei/uxae052

Zhang, Y., Wang, H., Oliveira, R. H. M., Zhao, C., & Popel, A. S. (2022). Systems biology of angiogenesis signaling: Computational models and omics. WIREs Mechanisms of Disease, 14(4), e1550. https://doi.org/10.1002/wsbm.1550

Zheng, C., Chen, X., Shuliang, H., & Mao, L. (2023). Challenges and developments in artificial intelligence-based algorithms for precision medicine and patient-specific predictive models. Expert Systems with Applications, 224, 119561. https://doi.org/10.1016/j.eswa.2023.119561