Multi-headed ensemble residual CNN: A powerful tool for fibroblast growth factor prediction
Naif Almusallam, Farman Ali, Harish Kumar, Tamim Alkhalifah, Fahad Alturise, Abdullah Almuhaimeed
Abstract
• Proposed a method (FGF-MERCNN) for prediction of Fibroblast Growth Factor • Features are extracted by DDE, EGAAC, and GTPC • MLP,CNN, MERCNN and GRU are used for model training • FGF-MERCNN secured the best performance Fibroblast Growth Factor (FGF) performs a significant role in the repair, nervous system, development, and maintenance, making it a promising target for treating neurological diseases such as Parkinson's, stroke, Alzheimer's, and Huntington's disorders. However, the detection of FGF remains challenging due to the high cost and time required by traditional methods. To address this issue, we propose the first sequence-based computational method, FGF-MERCNN, for identifying FGFs using deep learning. Novel datasets were constructed by converting primary sequences into numerical representations through Enhanced Group Amino Acid Composition (EGAAC), Dipeptide Deviation from Expected Mean (DDE), and Group Tripeptide Composition (GTPC). These features were combined and analyzed using several deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), Gated Recurrent Units (GRU), and Multiheaded Ensemble Residual CNN (MERCNN). Among these, the MERCNN model achieved superior performance with accuracies of 88.60% on the training set and 83.68% on the test set, as validated by 5-fold cross-validation. The results of this study represent a significant advancement in FGF detection, offering a faster, cost-effective solution with the potential to improve diagnostic and therapeutic strategies for neurological diseases.