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An omics-driven computational model for angiogenic protein prediction: Advancing therapeutic strategies with Ens-deep-AGP.
- Source :
-
International journal of biological macromolecules [Int J Biol Macromol] 2024 Dec; Vol. 282 (Pt 1), pp. 136475. Date of Electronic Publication: 2024 Oct 18. - Publication Year :
- 2024
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Abstract
- Angiogenic proteins (AGPs) play a critical role in both pathological and physiological activities, making them key therapeutic targets in diseases like cancer, heart disease, and stroke. Traditional methods for identifying AGPs are labor-intensive and time-consuming, creating a need for more efficient approaches. This study addresses this challenge by developing a novel computational model, Ens-Deep-AGP, designed to enhance AGP prediction. The model introduces innovative feature engineering techniques, including Position Specific Scoring Matrix-Decomposition-Discrete Cosine Transform (PSSM-DC-DCT) and Position Specific Scoring Matrix-Auto-Cross-Discrete Wavelet Transform (PSSM-ACC-DWT), which capture comprehensive protein sequence information. The ensemble feature set of these approaches are then fed into Multi-headed Ensemble Residual Convolutional Neural Network (MERCNN), a robust deep learning architecture. Ens-Deep-AGP achieved remarkable accuracy rates of 99.79 % on training dataset and 92.97 % on testing dataset, surpassing Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory Networks (BiLSTM). The successful prediction of AGPs is crucial for accelerating drug development, discovering novel therapeutic targets and deepen our understanding of AGPs' complex roles in healthcare.<br />Competing Interests: Declaration of competing interest Authors have no competing interest.<br /> (Copyright © 2024 Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1879-0003
- Volume :
- 282
- Issue :
- Pt 1
- Database :
- MEDLINE
- Journal :
- International journal of biological macromolecules
- Publication Type :
- Academic Journal
- Accession number :
- 39423981
- Full Text :
- https://doi.org/10.1016/j.ijbiomac.2024.136475