Molecular Prediction and Modeling of Bee Venom Effectiveness on Metastatic Breast Cancer Tissue Using Fuzzy Logic and Artificial Intelligence

International Congress of Health Disciplines, Bilecik, Türkiye, 4 - 05 Şubat 2026, ss.27, (Özet Bildiri)

Yayın Türü: Bildiri / Özet Bildiri
Basıldığı Şehir: Bilecik
Basıldığı Ülke: Türkiye
Sayfa Sayıları: ss.27
İstanbul Medipol Üniversitesi Adresli: Evet

Özet

Metastatic breast cancer remains a major clinical challenge due to its high mortality and limited therapeutic options. This study aims to predict the molecular effectiveness of bee venom on metastatic breast cancer tissue using computational approaches based on fuzzy logic and artificial intelligence. A multidimensional dataset was generated from hypothetical biochemical parameters and cellular response indicators. Fuzzy logic algorithms were applied to handle uncertainty and biological variability, followed by artificial neural networks to classify the potential impact of bee venom on apoptotic pathways and inhibition of metastatic cell proliferation. The developed model achieved an overall prediction accuracy of 91.4%, with sensitivity and specificity values of 92.1% and 94.8%, respectively. The simulation indicated a 88% probability of significant apoptotic activation and a 68% reduction in metastatic proliferation under modeled conditions. These findings suggest that bee venom may exert notable molecular effects that can be reliably predicted through computational modeling. This approach offers a cost-effective and time-saving strategy for optimizing therapeutic interventions prior to experimental validation. Further studies will integrate real-world omics data and expand the model to include drug synergy predictions, enabling more comprehensive therapeutic planning. By accurately modeling complex molecular interactions, it opens a pathway toward personalized cancer therapies and innovative treatment strategies.