Comprehensive Analysis of Computational Models for Prediction of Anticancer Peptides Using Machine Learning and Deep Learning

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Language: Английский

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An omics-driven computational model for angiogenic protein prediction: Advancing therapeutic strategies with Ens-deep-AGP

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: unknown, P. 136475 - 136475

Published: Oct. 1, 2024

Language: Английский

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Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy

IET Systems Biology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 8, 2024

Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functions oxygen transport. Given the importance considerations, accurately identifying is essential. To address need for precise GP identification, this research introduces an innovative approach that employs hybrid-based deep learning model called Deep-GP. We generated two datasets based on primary sequences developed novel feature descriptor called, Consensus Sequence-based Trisection-Position Specific Scoring Matrix (CST-PSSM). The training phase involved application techniques, including bidirectional long short-term memory network (BiLSTM), gated recurrent unit (GRU), convolutional neural (CNN). BiLSTM CNN were hybridised ensemble learning. CST-PSSM-based achieved most accurate predictive outcomes, outperforming other competitive predictors across both testing datasets. This demonstrates potential harnessing GB prediction robust tool expedite research, streamline drug discovery, unveil therapeutic targets.

Language: Английский

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Leveraging deep learning for epigenetic protein prediction: a novel approach for early lung cancer diagnosis and drug discovery

Health Information Science and Systems, Journal Year: 2025, Volume and Issue: 13(1)

Published: March 11, 2025

Language: Английский

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Conotoxins: Classification, Prediction, and Future Directions in Bioinformatics

Toxins, Journal Year: 2025, Volume and Issue: 17(2), P. 78 - 78

Published: Feb. 9, 2025

Conotoxins, a diverse family of disulfide-rich peptides derived from the venom Conus species, have gained prominence in biomedical research due to their highly specific interactions with ion channels, receptors, and neurotransmitter systems. Their pharmacological properties make them valuable molecular tools promising candidates for therapeutic development. However, traditional conotoxin classification functional characterization remain labor-intensive, necessitating increasing adoption computational approaches. In particular, machine learning (ML) techniques facilitated advancements sequence-based classification, prediction, de novo peptide design. This review explores recent progress applying ML deep (DL) research, comparing key databases, feature extraction techniques, models. Additionally, we discuss future directions, emphasizing integration multimodal data refinement predictive frameworks enhance discovery.

Language: Английский

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IR-MBiTCN: Computational prediction of insulin receptor using deep learning: A multi-information fusion approach with multiscale bidirectional temporal convolutional network

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: 311, P. 143844 - 143844

Published: May 2, 2025

Language: Английский

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AFP-MCDF: Multi and cross-dimensional feature fusion methods for antifreeze protein prediction

Analytical Biochemistry, Journal Year: 2025, Volume and Issue: 704, P. 115881 - 115881

Published: May 9, 2025

Language: Английский

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Multi-headed Ensemble Residual CNN: A Powerful Tool for Fibroblast Growth Factor Prediction

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103348 - 103348

Published: Nov. 8, 2024

Language: Английский

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Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(24), P. 13674 - 13674

Published: Dec. 21, 2024

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the large family with high infectivity and pathogenicity primary pathogen causing global pandemic disease 2019 (COVID-19). Phosphorylation major type protein post-translational modification that plays an essential role in process SARS-CoV-2–host interactions. The precise identification phosphorylation sites host cells infected SARS-CoV-2 will be great importance to investigate potential antiviral responses mechanisms exploit novel targets for therapeutic development. Numerous computational tools have been developed on basis phosphoproteomic data generated by mass spectrometry-based experimental techniques, which can accurately ascertained across whole SARS-CoV-2-infected proteomes. In this work, we comprehensively reviewed several aspects construction strategies availability these predictors, including benchmark dataset preparation, feature extraction refinement methods, machine learning algorithms deep architectures, model evaluation approaches metrics, publicly available web servers packages. We highlighted compared prediction performance each tool independent serine/threonine (S/T) tyrosine (Y) datasets discussed overall limitations current existing predictors. summary, review would provide pertinent insights into exploitation new powerful site tools, facilitate localization more suitable target molecules verification, contribute development therapies.

Language: Английский

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