Artificial Intelligence in Cancer Research: Predictive Modeling of Angiogenesis and Biomarker Discovery

Veera V Rama Rao M¹*, Savitha. S², Kumar Akuthota³, Pathan Firoze Khan⁴, Navyatha R⁵, Srithar S⁶

AI-driven models enhance cancer diagnostics, personalized treatment, and biomarker discovery, transforming precision oncology and patient care outcomes.

Abstract

Background: The integration of Artificial Intelligence (AI) in cancer research has dramatically enhanced the study, diagnosis, and treatment of cancer. AI’s advanced algorithms, especially recurrent neural networks (RNNs), have proven effective in analyzing complex datasets, facilitating novel biomarker discovery, and improving cancer diagnostics and prognostics. Angiogenesis, the process of new blood vessel formation, plays a crucial role in tumor growth and metastasis, making it a promising target for therapeutic strategies. This study explores the use of AI to identify angiogenesis-related biomarkers and develop personalized treatment strategies. Methods: This study utilized a large dataset from The Cancer Genome Atlas (TCGA), encompassing over 20,000 primary tumor and normal samples across 33 cancer types. Preprocessing techniques such as data cleaning, normalization, and outlier detection were applied. Dimensionality reduction through Principal Component Analysis (PCA) and data visualization using t-Distributed Stochastic Neighbor Embedding (t-SNE) were employed. A Recurrent Neural Network (RNN) was chosen to analyze the sequential biological data and identify potential biomarkers related to angiogenesis. Results: The AI model demonstrated excellent performance across multiple evaluation metrics, including accuracy (0.85), precision (0.82), recall (0.88), F1-score (0.85), and AUC-ROC (0.92), highlighting its effectiveness in predicting cancer progression and identifying key biomarkers. The RNN model was particularly adept at identifying complex patterns in angiogenesis data, facilitating a deeper understanding of tumor biology and revealing novel therapeutic targets. Conclusion: The results underscore the significant potential of AI, specifically RNNs, in advancing cancer research and personalized treatment planning. AI-driven insights into angiogenesis-related biomarkers enable targeted therapies, offering new avenues for effective cancer treatment. These findings not only improve cancer diagnosis and prognosis but also emphasize the role of AI in developing precision oncology approaches, enhancing patient outcomes, and guiding future research in cancer therapeutics.

Keywords: Artificial Intelligence, Cancer Research, Angiogenesis, Biomarker Discovery, Recurrent Neural Networks

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