Advancing neurological disease treatment: a computational approach for fibroblast growth factor detection

Fibroblast Growth Factor plays a crucial role in neurological health, contributing to neuron protection, injury recovery, and angiogenesis. It is also significantly involved in the onset and progression of neurodegenerative disorders such as Huntington's, Alzheimer's, Parkinson's disease, and stroke, making FGF a vital target for therapeutic interventions. Despite its importance, no computational tool has been developed to predict FGF proteins. In this study, we present the first novel deep learning-based computational approach designed for the prediction of FGF proteins. We constructed two novel, high-quality datasets curated from the UniProt database for training and evaluation. Sequences were transformed into numerical representations using three complementary feature encoding methods including Dipeptide Composition, Dipeptide Deviation from Expected Mean, and Grouped Amino Acid Composition. These features capture both local and global sequence information. Multiple deep learning models were explored, including Convolutional Neural Network, Bidirectional Long Short-Term Memory, Generative Adversarial Network, and Gated Recurrent Unit. Among these, our proposed Convolutional Neural Network-based model outperformed all others, achieving an accuracy of 83.50%, sensitivity of 84.30%, specificity of 82.67%, F1 score of 83.42%, and a Matthews Correlation Coefficient of 0.671. The proposed approach has the potential to advance therapeutic discovery by enabling accurate identification of Fibroblast Growth Factor and improving our understanding of their role in neurological health and disease.