Breast cancer is often distinguished by overexpression of epidermal growth factor (EGFR) with current methods of detection lacking accuracy and are often expensive to analyze experimentally. Alghamdi et al. created a deep learning based predictor to accurately identify EGFR proteins form their primary amino acid sequences to best target therapeutically to combat drug resistance, tumor heterogeneity, and lack of robust predictive biomarkers. Prior research has shown usage of deep learning in diagnostic methods to improve accuracy and precision often in multi-modal imaging. Alghamdi et al. developed a computational predictor to quickly identify EGFR using a robust dataset via feature extraction of primary sequences of proteins. Using KSCTD, AmpPseAAC, CDT, and ProtBERT-BFD as primary feature extraction methods, the researchers utilized numerous deep learning frameworks to make a more robust generalized model. Via this analysis, ProtBERT-BFD was found to have the best performance across all descriptors (77.12% Acc, 77.71% Sn, 77.34% Sp, and MCC of 0.664); combined with ERCNN, this model is the most generalizable representation to classify EGFR. This led to the proposed model of ERCNN-EGFR built by the researchers with superior predictive power in capturing distinct features with a higher capability to generalize. In conclusion, the ERCNN-EFGR deep-learning framework was developed as a scalable, cost-effective, and reliable computational tool that can complement existing laboratory-based EGFR detection to accelerate therapeutic development to support precision oncology.