Emerging Artificial Intelligence Methodologies in Computational Biology

Journal of Molecular Biology, Journal Year: 2025, Volume and Issue: unknown, P. 169002 - 169002

Published: Feb. 1, 2025

Language: Английский

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PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial intelligence

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: March 13, 2025

Introduction Pathological myopia (PM) is a serious visual impairment that may lead to irreversible damage or even blindness. Timely diagnosis and effective management of PM are great significance. Given the increasing number cases worldwide, there an urgent need develop automated, accurate, highly interpretable diagnostic technology. Methods We proposed computational model called PMPred-AE based on EfficientNetV2-L with attention mechanism optimization. In addition, Gradient-weighted class activation mapping (Grad-CAM) technology was used provide intuitive interpretation for model’s decision-making process. Results The experimental results demonstrated achieved excellent performance in automatically detecting PM, accuracies 98.50, 98.25, 97.25% training, validation, test datasets, respectively. can focus specific areas image when making detection decisions. Discussion developed capable reliably providing accurate detection. Grad-CAM also process model. This approach provides healthcare professionals tool AI

Language: Английский

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pNPs-CapsNet: Predicting Neuropeptides Using Protein Language Models and FastText Encoding-Based Weighted Multi-View Feature Integration with Deep Capsule Neural Network

ACS Omega, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Neuropeptides (NPs) are critical signaling molecules that essential in numerous physiological processes and possess significant therapeutic potential. Computational prediction of NPs has emerged as a promising alternative to traditional experimental methods, often labor-intensive, time-consuming, expensive. Recent advancements computational peptide models provide cost-effective approach identifying NPs, characterized by high selectivity toward target cells minimal side effects. In this study, we propose novel deep capsule neural network-based model, namely pNPs-CapsNet, predict non-NPs accurately. Input samples numerically encoded using pretrained protein language models, including ESM, ProtBERT-BFD, ProtT5, extract attention mechanism-based contextual semantic features. A differential evolution-based weighted feature integration method is utilized construct multiview vector. Additionally, two-tier selection strategy, comprising MRMD SHAP analysis, developed identify select optimal Finally, the network (CapsNet) trained selected set. The proposed pNPs-CapsNet model achieved remarkable predictive accuracy 98.10% an AUC 0.98. To validate generalization capability independent reported 95.21% 0.96. outperforms existing state-of-the-art demonstrating 4% 2.5% improved for training data sets, respectively. demonstrated efficacy consistency underline its potential valuable robust tool advancing drug discovery academic research.

Language: Английский

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Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools

Molecular Diversity, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

Language: Английский

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A comprehensive review and evaluation of machine learning-based approaches for identifying tumor T cell antigens

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 108440 - 108440

Published: April 1, 2025

Language: Английский

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