Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with two-dimensional convolutional neural network

References

• Adnan, A., Hongya, W., Ali, F., Khalid, M., Alghushairy, O., & Alsini, R. (2023). A bi-layer model for identification of piwiRNA using deep neural learning. Journal of Biomolecular Structure & Dynamics, 1–9. https://doi.org/10.1080/07391102.2023.2243523
   Web of Science ®Google Scholar
• Ahmad, A., Akbar, S., Hayat, M., Ali, F., Khan, S., & Sohail, M. (2020). Identification of antioxidant proteins using a discriminative intelligent model of k-space amino acid pairs based descriptors incorporating with ensemble feature selection. Biocybernetics and Biomedical Engineering, 42(2), 727–735. https://doi.org/10.1016/j.bbe.2020.10.003
   Web of Science ®Google Scholar
• Ahmad, A., Akbar, S., Khan, S., Hayat, M., Ali, F., Ahmed, A., & Tahir, M. (2021). Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks. Chemometrics and Intelligent Laboratory Systems, 208, 104214. https://doi.org/10.1016/j.chemolab.2020.104214
   Web of Science ®Google Scholar
• Ahmad, A., Akbar, S., Tahir, M., Hayat, M., & Ali, F. (2022). iAFPs-EnC-GA: Identifying antifungal peptides using sequential and evolutionary descriptors based multi-information fusion and ensemble learning approach. Chemometrics and Intelligent Laboratory Systems, 222, 104516. https://doi.org/10.1016/j.chemolab.2022.104516
   Web of Science ®Google Scholar
• Ahmed, S., Kabir, M., Arif, M., Ali, Z., Ali, F., & Swati, Z. N. (2018). Improving secretory proteins prediction in Mycobacterium tuberculosis using the unbiased dipeptide composition with support vector machine. International Journal of Data Mining and Bioinformatics, 21(3), 212–229. https://doi.org/10.1504/IJDMB.2018.097682
   Web of Science ®Google Scholar
• Akbar, S., Ahmad, A., Hayat, M., Rehman, A. U., Khan, S., & Ali, F. (2021). iAtbP-Hyb-EnC: prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model. Computers in Biology and Medicine, 137, 104778. https://doi.org/10.1016/j.compbiomed.2021.104778
   PubMed Web of Science ®Google Scholar
• Akbar, S., Ali, H., Ahmad, A., Sarker, M. R., Saeed, A., Salwana, E., Gul, S., Khan, A., & Ali, F. (2023). Prediction of amyloid proteins using embedded evolutionary & ensemble feature selection based descriptors with eXtreme gradient boosting model. IEEE Access, 11, 39024–39036. https://doi.org/10.1109/ACCESS.2023.3268523
   Web of Science ®Google Scholar
• Akbar, S., Ali, F., Hayat, M., Ahmad, A., Khan, S., & Gul, S. (2022). Prediction of antiviral peptides using transform evolutionary & SHAP analysis based descriptors by incorporation with ensemble learning strategy. Chemometrics and Intelligent Laboratory Systems, 230, 104682. https://doi.org/10.1016/j.chemolab.2022.104682
   Web of Science ®Google Scholar
• Akbar, S., Khan, S., Ali, F., Hayat, M., Qasim, M., & Gul, S. (2020). iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach. Chemometrics and Intelligent Laboratory Systems, 204, 104103. https://doi.org/10.1016/j.chemolab.2020.104103
   Web of Science ®Google Scholar
• Akbar, S., Mohamed, H. G., Ali, H., Saeed, A., Khan, A. A., Gul, S., Ahmad, A., Ali, F., Ghadi, Y. Y., & Assam, M. (2023). Identifying neuropeptides via evolutionary and sequential based multi-perspective descriptors by Incorporation with ensemble classification strategy. IEEE Access, 11, 49024–49034. https://doi.org/10.1109/ACCESS.2023.3274601
   Web of Science ®Google Scholar
• Ali, F., Ahmed, S., Swati, K., & Akbar, S. (2019). DP-BINDER: Machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information. Journal of Computer-Aided Molecular Design, 33(7), 645–658. https://doi.org/10.1007/s10822-019-00207-x
   PubMed Web of Science ®Google Scholar
• Ali, F., Akbar, S., Ghulam, A., Maher, Z. A., Unar, A., & Talpur, B. (2021). AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information. Computers in Biology and Medicine, 139, 105006. https://doi.org/10.1016/j.compbiomed.2021.105006
   PubMed Web of Science ®Google Scholar
• Ali, F., Alghamdi, W., Almagrabi, A. O., Alghushairy, O., Banjar, A., & Khalid, M. (2023). Deep-AGP: Prediction of angiogenic protein by integrating two-dimensional convolutional neural network with discrete cosine transform. International Journal of Biological Macromolecules, 243, 125296. https://doi.org/10.1016/j.ijbiomac.2023.125296
   PubMed Web of Science ®Google Scholar
• Ali, F., Arif, M., Khan, Z. U., Kabir, M., Ahmed, S., & Yu, D.-J. (2020). SDBP-Pred: Prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM. Analytical Biochemistry, 589, 113494. https://doi.org/10.1016/j.ab.2019.113494
   PubMed Web of Science ®Google Scholar
• Ali, F., Barukab, O., Gadicha, A. B., Patil, S., Alghushairy, O., & Sarhan, Y. (2022). DBP-iDWT: Improving DNA-binding proteins prediction using multi-perspective evolutionary profile and discrete wavelet transform. Computational Intelligence and Neuroscience, 2022, 2987407–2987408. https://doi.org/10.1155/2022/2987407
   PubMed Web of Science ®Google Scholar
• Ali, F., Ghulam, A., Maher, Z. A., Khan, M. A., Khan, S. A., Hongya, W., Adnan A., (2022). Deep-PCL: A deep learning model for prediction of cancerlectins and non cancerlectins using optimized integrated features. Chemometrics and Intelligent Laboratory Systems, 221, 104484. https://doi.org/10.1016/j.chemolab.2021.104484
   Web of Science ®Google Scholar
• Ali, F., & Hayat, M. (2015). Classification of membrane protein types using Voting Feature Interval in combination with Chou s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 384, 78–83. https://doi.org/10.1016/j.jtbi.2015.07.034
   PubMed Web of Science ®Google Scholar
• Ali, F., & Hayat, M. (2016). Machine learning approaches for discrimination of Extracellular Matrix proteins using hybrid feature space. Journal of Theoretical Biology, 403, 30–37. https://doi.org/10.1016/j.jtbi.2016.05.011
   PubMed Web of Science ®Google Scholar
• Ali, F., Kabir, M., Arif, M., Swati, K., Khan, U., Ullah, M., & Yu, D. J. (2018). DBPPred-PDSD: Machine learning approach for prediction of DNA-binding proteins using Discrete Wavelet Transform and optimized integrated features space. Chemometrics and Intelligent Laboratory Systems, 182, 21–30. https://doi.org/10.1016/j.chemolab.2018.08.013
   Web of Science ®Google Scholar
• Ali, F., Kumar, H., Patil, S., Ahmad, A., Babour, A., & Daud, A. (2022). Deep-GHBP: Improving prediction of Growth Hormone-binding proteins using deep learning model. Biomedical Signal Processing and Control, 78, 103856. https://doi.org/10.1016/j.bspc.2022.103856
   Web of Science ®Google Scholar
• Ali, F., Kumar, H., Patil, S., Ahmed, A., Banjar, A., & Daud, A. (2022). DBP-DeepCNN: Prediction of DNA-binding proteins using wavelet-based denoising and deep learning. Chemometrics and Intelligent Laboratory Systems, 229, 104639. https://doi.org/10.1016/j.chemolab.2022.104639
   Web of Science ®Google Scholar
• Ali, F., Kumar, H., Patil, S., Kotecha, K., Banjar, A., & Daud, A. (2022). Target-DBPPred: An intelligent model for prediction of DNA-binding proteins using discrete wavelet transform based compression and light eXtreme gradient boosting. Computers in Biology and Medicine, 145, 105533. https://doi.org/10.1016/j.compbiomed.2022.105533
   PubMed Web of Science ®Google Scholar
• Arif, M., Ahmad, S., Ali, F., Fang, G., Li, M., & Yu, D. J. (2020). TargetCPP: Accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree. Journal of Computer-Aided Molecular Design, 34(8), 841–856. https://doi.org/10.1007/s10822-020-00307-z
   PubMed Web of Science ®Google Scholar
• Banjar, A., Ali, F., Alghushairy, O., & Daud, A. (2022). iDBP-PBMD: A machine learning model for detection of DNA-binding proteins by extending compression techniques into evolutionary profile. Chemometrics and Intelligent Laboratory Systems, 231, 104697. https://doi.org/10.1016/j.chemolab.2022.104697
   Web of Science ®Google Scholar
• Barukab, O., Ali, F., Alghamdi, W., Bassam, Y., & Khan, S. A. (2022). DBP-CNN: Deep learning-based prediction of DNA-binding proteins by coupling discrete cosine transform with two-dimensional convolutional neural network. Expert Systems with Applications, 197, 116729. https://doi.org/10.1016/j.eswa.2022.116729
   Web of Science ®Google Scholar
• Chen, D. S., & Hurwitz, H. (2018). Combinations of bevacizumab with cancer immunotherapy. Cancer Journal (Sudbury, MA), 24(4), 193–204. https://doi.org/10.1097/PPO.0000000000000327
   PubMed Web of Science ®Google Scholar
• Cheung, N., Wong, I. Y., & Wong, T. (2014). Ocular anti-VEGF therapy for diabetic retinopathy: Overview of clinical efficacy and evolving applications. Diabetes Care, 37(4), 900–905. https://doi.org/10.2337/dc13-1990
   PubMed Web of Science ®Google Scholar
• Crafts, T. D., Jensen, A. R., Blocher-Smith, E. C., & Markel, T. (2015). Vascular endothelial growth factor: Therapeutic possibilities and challenges for the treatment of ischemia. Cytokine, 71(2), 385–393. https://doi.org/10.1016/j.cyto.2014.08.005
   PubMed Web of Science ®Google Scholar
• Elebiyo, T. C., Rotimi, D., Evbuomwan, I. O., Maimako, R. F., Iyobhebhe, M., Ojo, O. A., Oluba, O. M., & Adeyemi, O. S. (2022). Reassessing vascular endothelial growth factor (VEGF) in anti-angiogenic cancer therapy. Cancer Treatment and Research Communications, 32, 100620. https://doi.org/10.1016/j.ctarc.2022.100620
   PubMedGoogle Scholar
• Falcon, B. L., Chintharlapalli, S., Uhlik, M. T., & Pytowski, B. (2016). Antagonist antibodies to vascular endothelial growth factor receptor 2 (VEGFR-2) as anti-angiogenic agents. Pharmacology & Therapeutics, 164, 204–225. https://doi.org/10.1016/j.pharmthera.2016.06.001
   PubMed Web of Science ®Google Scholar
• Ghulam, A., Ali, F., Sikander, R., Ahmad, A., Ahmed, A., & Patil, S. (2022). ACP-2DCNN: Deep learning-based model for improving prediction of anticancer peptides using two-dimensional convolutional neural network. Chemometrics and Intelligent Laboratory Systems, 226, 104589. https://doi.org/10.1016/j.chemolab.2022.104589
   Web of Science ®Google Scholar
• Ghulam, A., Memon, M., Hyder, M., Maher, Z., Unar, A., Swati, Z., & Farman, A. (2021). Identification of novel protein sequencing SARS CoV-2 coronavirus using machine learning. Bioscience Research, 18, 47–58. https://doi.org/10.1016/j.compbiomed.2021.104359
   Web of Science ®Google Scholar
• Ghulam, A., Sikander, R., Ali, F., Swati, K., Unar, A., & Talpur, B. (2022). Accurate prediction of immunoglobulin proteins using machine learning model. Informatics in Medicine Unlocked, 29, 100885. https://doi.org/10.1016/j.imu.2022.100885
   Google Scholar
• Ghulam, A., Swati, Z. N. K., Ali, F., Tunio, S., Jabeen, N., & Iqbal, N. (2023). DeepImmuno-PSSM: Identification of immunoglobulin based on deep learning and PSSM-profiles. VAWKUM Transactions on Computer Sciences, 11(1), 54–66. https://doi.org/10.21015/vtcs.v11i1.1396
   Google Scholar
• Guo, Y., Zhou, D., Li, P., Li, C., Cao, J., & Systems, L. (2022). Context-aware poly (A) signal prediction model via deep spatial–temporal neural networks. IEEE Transactions on Neural Networks and Learning Systems, 1–13. https://doi.org/10.1109/TNNLS.2022.3226301
   Google Scholar
• Guo, Y., Zhou, D., Ruan, X., & Cao, J. (2023). Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features. Neural Networks, 165, 491–505. https://doi.org/10.1016/j.neunet.2023.05.052
   PubMed Web of Science ®Google Scholar
• Hoeben, A., Landuyt, B., Highley, M. S., Wildiers, H., Van Oosterom, A. T., & De Bruijn, E. (2004). Vascular endothelial growth factor and angiogenesis. Pharmacological Reviews, 56(4), 549–580. https://doi.org/10.1124/pr.56.4.3
   PubMed Web of Science ®Google Scholar
• Kabir, M., Arif, M., Ali, F., Ahmad, S., Swati, Z. N. K., & Yu, D. J. (2019). Prediction of membrane protein types by exploring local discriminative information from evolutionary profiles. Analytical Biochemistry, 564–565, 123–132. https://doi.org/10.1016/j.ab.2018.10.027
   PubMed Web of Science ®Google Scholar
• Khan, Z. U., Ali, F., Ahmad, I., Hayat, M., & Pi, D. (2019). iPredCNC: Computational prediction model for cancerlectins and non-cancerlectins using novel cascade features subset selection. Chemometrics and Intelligent Laboratory Systems, 195, 103876. https://doi.org/10.1016/j.chemolab.2019.103876
   Web of Science ®Google Scholar
• Khan, Z. U., Ali, F., Khan, I. A., Hussain, Y., & Pi, D. (2019). iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via Chou’s 5-step rule and pseudo components. Chemometrics and Intelligent Laboratory Systems, 189, 169–180. https://doi.org/10.1016/j.chemolab.2019.05.003
   Web of Science ®Google Scholar
• Khan, I. A., Pi, D., Khan, N., Khan, Z. U., Hussain, Y., Nawaz, A., & Ali, F. (2021). A privacy-conserving framework based intrusion detection method for detecting and recognizing malicious behaviours in cyber-physical power networks. Applied Intelligence, 51(10), 7306–7321. https://doi.org/10.1007/s10489-021-02222-8
   Web of Science ®Google Scholar
• Khan, Z. U., Pi, D., Yao, S., Nawaz, A., Ali, F., & Ali, S. (2021). piEnPred: A bi-layered discriminative model for enhancers and their subtypes via novel cascade multi-level subset feature selection algorithm. Frontiers of Computer Science, 15(6), 1–11. https://doi.org/10.1007/s11704-020-9504-3
   Web of Science ®Google Scholar
• Khan, A., Uddin, J., Ali, F., Ahmad, A., Alghushairy, O., Banjar, A., & Daud, A. (2022). Prediction of antifreeze proteins using machine learning. Scientific Reports, 12(1), 20672. https://doi.org/10.1038/s41598-022-24501-1
   PubMed Web of Science ®Google Scholar
• Khan, A., Uddin, J., Ali, F., Banjar, A., & Daud, A. (2022). Comparative analysis of the existing methods for prediction of antifreeze proteins. Chemometrics and Intelligent Laboratory Systems, 232, 104729. https://doi.org/10.1016/j.chemolab.2022.104729
   Web of Science ®Google Scholar
• Khan, A., Uddin, J., Ali, F., Kumar, H., Alghamdi, W., & Ahmad, A. (2023). AFP-SPTS: An accurate prediction of antifreeze proteins using sequential and pseudo-tri-slicing evolutionary features with an extremely randomized tree. Journal of Chemical Information and Modeling, 63(3), 826–834. https://doi.org/10.1021/acs.jcim.2c01417
   PubMedGoogle Scholar
• Le, N. Q. K., Yapp, E. K. Y., & Yeh, H. (2019). ET-GRU: Using multi-layer gated recurrent units to identify electron transport proteins. BMC Bioinformatics, 20(1), 377. https://doi.org/10.1186/s12859-019-2972-5
   PubMedGoogle Scholar
• Li, W., Guo, Y., Wang, B., & Yang, B. (2023). Learning spatiotemporal embedding with gated convolutional recurrent networks for translation initiation site prediction. Pattern Recognition, 136, 109234. https://doi.org/10.1016/j.patcog.2022.109234
   Web of Science ®Google Scholar
• Lugano, R., Ramachandran, M., Dimberg, A. J. C., & Sciences, M. (2020). Tumor angiogenesis: Causes, consequences, challenges and opportunities. Cellular and Molecular Life Sciences, 77(9), 1745–1770. https://doi.org/10.1007/s00018-019-03351-7
   PubMed Web of Science ®Google Scholar
• McKay, G. J., Silvestri, G., Orr, N., Chakravarthy, U., & Hughes, A. (2009). VEGF and age-related macular degeneration. Ophthalmology, 116(6), 1227.e1–e1223. https://doi.org/10.1016/j.ophtha.2009.01.008
   PubMed Web of Science ®Google Scholar
• Melincovici, C. S., Boşca, A. B., Şuşman, S., Mărginean, M., Mihu, C., & Istrate, M. (2018). Vascular endothelial growth factor (VEGF)-key factor in normal and pathological angiogenesis. Romanian Journal of Morphology, 59(2), 455–467. https://doi.org/10.1078/s41598-022-09453-1
   PubMed Web of Science ®Google Scholar
• Rahu, S., Ghulam, A., Farman, A., Talpur, D. B., Talpur, M. S. H., Saba, E., Maher, Z. A., & Tunio, S. (2022). UBI-XGB: Identification of ubiquitin proteins using machine learning model. Journal of Mountain Area Research, 8, 14–26. https://doi.org/10.53874/jmar.v8i0.167
   Google Scholar
• Salehinejad, H., Sankar, S., Barfett, J., Colak, E., & Valaee, S. (2017). Recent advances in recurrent neural networks. Neural and Evolutionary Computing, 9(2), 55–67. https://doi.org/10.48550/arXiv.1801.01078
   Google Scholar
• Shibuya, M. (2013). Vascular endothelial growth factor and its receptor system: Physiological functions in angiogenesis and pathological roles in various diseases. Journal of Biochemistry, 153(1), 13–19. https://doi.org/10.1093/jb/mvs136
   PubMed Web of Science ®Google Scholar
• Sikander, R., Ghulam, A., & Ali, F. (2022). XGB-DrugPred: Computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set. Scientific Reports, 12(1), 5505. https://doi.org/10.1038/s41598-022-09484-3
   PubMed Web of Science ®Google Scholar
• Som, S., Chandra, N., Ahuja, L., Khatri, S. K., Som, S., & Monga, H. (2021). Utilizing gated recurrent units to retain long term dependencies with recurrent neural network in text classification. Journal of Information Systems and Telecommunication, 9(34), 89–102. https://doi.org/10.52547/jist.9.34.89
   Google Scholar
• Taimeh, Z., Loughran, J., Birks, E. J., & Bolli, R. (2013). Vascular endothelial growth factor in heart failure. Nature Reviews. Cardiology, 10(9), 519–530. https://doi.org/10.1038/nrcardio.2013.94
   PubMed Web of Science ®Google Scholar
• Yan, F., Feng, Y. J. C., & Systems, I. (2022). A two-stage stacked-based heterogeneous ensemble learning for cancer survival prediction. Complex & Intelligent Systems, 8(6), 4619–4639. https://doi.org/10.1007/s40747-022-00791-w
   Web of Science ®Google Scholar
• Yang, L., Wei, P., Zhong, C., Li, X., Tang, Y. Y., (2020). Protein structure prediction based on BN-GRU method. International Journal of Wavelets, Multiresolution and Information Processing, 18(6), 2050045. https://doi.org/10.1142/S0219691320500459
   Web of Science ®Google Scholar