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

Reuss B, von Bohlen O. Fibroblast growth factors and their receptors in the central nervous system. Cell Tissue Res. 2003;313(2):139–57.

Article  Google Scholar 

Upadhya R, Zingg W, Shetty S, Shetty A. Astrocyte-derived extracellular vesicles: neuroreparative properties and role in the pathogenesis of neurodegenerative disorders. J Control Release. 2020;323:225–39.

Article  Google Scholar 

Alam R, et al. New insights into the role of fibroblast growth factors in Alzheimer’s disease. Mol Biol Rep. 2022;49(2):1413–27.

Article  Google Scholar 

Dordoe C, et al. The role of microglial activation on ischemic stroke: modulation by fibroblast growth factors. Cytokine Growth Factor Rev. 2023;74:122–33.

Article  Google Scholar 

Khrapko KR et al., Methods of DNA sequencing by hybridization based on optimizing concentration of matrix-bound oligonucleotide and device for carrying out same, ed: Google Patents, 1996.

Pagano K, et al. Identification of a novel extracellular inhibitor of FGF2/FGFR signaling axis by combined virtual screening and NMR spectroscopy approach. Bioorganic Chem. 2023;136:106529.

Article  Google Scholar 

Zaha D. Significance of immunohistochemistry in breast cancer. World J Clin Oncol. 2014;5(3):382.

Article  Google Scholar 

Veisi A, Delavari H, Energy S, Adaptive fractional backstepping intelligent controller for maximum power extraction of a wind turbine system, vol. 15, no. 6, 2023.

Veisi A, Delavari H. Deep reinforcement learning-based robust nonlinear controller for photovoltaic systems. Neural Comput Appl. 2024;36(32):19989–20009.

Article  Google Scholar 

Veisi A, Delavari H. Fractional data driven controller based on adaptive neural network optimizer. Expert Syst Appl. 2024;257:125077.

Article  Google Scholar 

Veisi A, Delavari H. Deep reinforcement learning optimizer based novel Caputo fractional order sliding mode data driven controller. Eng Appl Artif Intell. 2025;140:109725.

Article  Google Scholar 

Ghulam A, Sikander R, Ali F. AI and machine learning-based practices in various domains: a survey. VAWKUM Trans Comput Sci. 2022;10(1):21–41.

Article  Google Scholar 

Ali F, Alghamdi W, Almagrabi AO, Alghushairy O, Banjar A, Khalid M. Deep-AGP: prediction of angiogenic protein by integrating two-dimensional convolutional neural network with discrete cosine transform. Int J Biol Macromol. 2023. https://doi.org/10.1016/j.ijbiomac.2023.125296.

Article  Google Scholar 

Ali F, Kumar H, Alghamdi W, Kateb FA, Alarfaj F. Recent advances in machine learning-based models for prediction of antiviral peptides. Arch Comput Methods Eng. 2023. https://doi.org/10.1007/s11831-023-09933-w.

Article  Google Scholar 

Zouari S, et al. Deep-GB: a novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy. IET Syst Biol. 2024;18(6):208–17.

Article  MathSciNet  Google Scholar 

Adnan A, Hongya W, Ali F, Khalid M, Alghushairy O, Alsini R. A bi-layer model for identification of piwiRNA using deep neural learning. J Biomol Struct Dyn. 2024. https://doi.org/10.1080/07391102.2023.2243523.

Article  Google Scholar 

Alghushairy O, et al. Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting. J Biomol Struct Dyn. 2023;42(22):12330–41.

Article  Google Scholar 

Ali F, Ahmed S, Swati ZNK, Akbar S. DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information. J Comput Aided Mol Des. 2019;33(7):645–58.

Article  Google Scholar 

Ali F, Arif M, Khan ZU, Kabir M, Ahmed S, Yu D-J. SDBP-Pred: prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM. Anal Biochem. 2020;589:113494. https://doi.org/10.1016/j.ab.2019.113494.

Article  Google Scholar 

Ali F, Ghulam A, Maher ZA, Khan MA, Khan SA, Hongya W. Deep-PCL: a deep learning model for prediction of cancerlectins and non cancerlectins using optimized integrated features. Chemometr Intell Lab Syst. 2022;221:104484.

Article  Google Scholar 

Ali F, Barukab O, Gadicha AB, Patil S, Alghushairy O, Sarhan AY. DBP-iDWT: improving DNA-binding proteins prediction using multi-perspective evolutionary profile and discrete wavelet transform. Comput Intell Neurosci. 2022. https://doi.org/10.1155/2022/2987407.

Article  Google Scholar 

Ali F, Hayat M. Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space. J Theor Biol. 2016;403:30–7.

Article  MathSciNet  Google Scholar 

Barukab O, Ali F, Alghamdi W, Bassam Y, Khan SA. DBP-CNN: deep learning-based prediction of DNA-binding proteins by coupling discrete cosine transform with two-dimensional convolutional neural network. Expert Syst Appl. 2022. https://doi.org/10.1016/j.eswa.2022.116729.

Article  Google Scholar 

Ali F, Khalid M, Masmoudi A, Alghamdi W, Yafoz A, Alsini R. VEGF-ERCNN: a deep learning-based model for prediction of vascular endothelial growth factor using ensemble residual CNN. J Comput Sci. 2024;83:102448.

Article  Google Scholar 

Ali F, Akbar S, Ghulam A, Maher ZA, Unar A, Talpur DB. AFP-CMBPred: computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information. Comput Biol Med. 2021;139:105006. https://doi.org/10.1016/j.compbiomed.2021.105006.

Article  Google Scholar 

Ali F, et al. DBPPred-PDSD: machine learning approach for prediction of DNA-binding proteins using discrete wavelet transform and optimized integrated features space. Chemometr Intell Lab Syst. 2018;182:21–30.

Article  Google Scholar 

Khan A, et al. Prediction of antifreeze proteins using machine learning. Sci Rep. 2022;12(1):1–10.

Article  Google Scholar 

Almusallam N, Ali F, Kumar H, Alkhalifah T, Alturise F, Almuhaimeed A. Multi-headed ensemble residual CNN: a powerful tool for fibroblast growth factor prediction. Results Eng. 2024;24:103348. https://doi.org/10.1016/j.rineng.2024.103348.

Article  Google Scholar 

Zhang D, Kabuka M. Multimodal deep representation learning for protein interaction identification and protein family classification. BMC Bioinform. 2019;20:1–14.

Article  Google Scholar 

Lin H, Ding H. Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J Theor Biol. 2011;269(1):64–9.

Article  MathSciNet  Google Scholar 

Almusallam N, Ali F, Masmoudi A, Ghazalah SA, Alsini R, Yafoz A. An omics-driven computational model for angiogenic protein prediction: advancing therapeutic strategies with Ens-deep-AGP. Int J Biol Macromol. 2024;282:136475.

Article  Google Scholar 

Bhasin M, Raghava GP. Classification of nuclear receptors based on amino acid composition and dipeptide composition. J Biol Chem. 2004;279(22):23262–6.

Article  Google Scholar 

Ahmad A, et al. Deep-AntiFP: prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks. Chemometr Intell Lab Syst. 2021;208:104214.

Article  Google Scholar 

Ali F, Khalid M, Masmoudi A, Alghamdi W, Yafoz A, Alsini R. VEGF-ERCNN: a deep learning-based model for prediction of vascular endothelial growth factor using ensemble residual CNN. J Comput Sci. 2024. https://doi.org/10.1016/j.jocs.2024.102448.

Article  Google Scholar 

Ali F, Kumar H, Patil S, Ahmad A, Babour A, Daud A. Deep-GHBP: improving prediction of growth hormone-binding proteins using deep learning model. Biomed Signal Process Control. 2022;78:103856. https://doi.org/10.1016/j.bspc.2022.103856.

Article  Google Scholar 

Ali F, Khalid M, Almuhaimeed A, Masmoudi A, Alghamdi W, Yafoz A. IP-GCN: a deep learning model for prediction of insulin using graph convolutional network for diabetes drug design. J Comput Sci. 2024;81:102388.

Article  Google Scholar 

Ali F, Kumar H, Patil S, Ahmed A, Banjar A, Daud A. DBP-DeepCNN: prediction of DNA-binding proteins using wavelet-based denoising and deep learning. Chemometr Intell Lab Syst. 2022. https://doi.org/10.1016/j.chemolab.2022.104639.

Article  Google Scholar 

Nakach F-Z, Idri A, Goceri E. A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification. Artif Intell Rev. 2024;57(12):327.

Article  Google Scholar 

Goceri E. GAN based augmentation using a hybrid loss function for dermoscopy images. Artif Intell Rev. 2024;57(9):234.

Article  Google Scholar 

Idlahcen F, Idri A, Goceri E. Exploring data mining and machine learning in gynecologic oncology. Artif Intell Rev. 2024;57(2):20.

Article  Google Scholar 

Goceri E, Karakaş AA, Comparative evaluations of CNN based networks for skin lesion classification, In: 14th International conference on computer graphics, visualization, computer vision and image processing, 5th international conference on big data analytics, data mining and computational intelligence and 9th international conference on theory and practice in modern computing, 2020.

Goceri E. An efficient network with CNN and transformer blocks for glioma grading and brain tumor classification from MRIs. Expert Syst Appl. 2025;268:126290.

Article  Google Scholar 

Swati ZNK, et al. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph. 2019;75:34–46.

Article  Google Scholar 

Khan A, et al. AFP-SPTS: an accurate prediction of antifreeze proteins using sequential and pseudo-tri-slicing evolutionary features with an extremely randomized tree. J Chem Inf Model. 2023;63(3):826–34.

Article  Google Scholar 

Khalid M, et al. An ensemble computati

Comments (0)

No login
gif