pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 2, 2025

Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including costs substantial side effects. Recently the selectivity of peptides has garnered attention from scientists their reliable targeted actions minimal adverse Furthermore, keeping outcomes existing computational models, we propose highly effective model namely, pACP-HybDeep accurate prediction anticancer peptides. In this model, training are numerically encoded using an attention-based ProtBERT-BFD encoder extract semantic features along with CTDT-based structural information. k-nearest neighbor-based binary tree growth (BTG) algorithm is employed select optimal feature set multi-perspective vector. The selected vector subsequently trained CNN + RNN-based deep learning model. Our proposed demonstrated accuracy 95.33%, AUC 0.97. To validate generalization capabilities our achieved accuracies 94.92%, 92.26%, 91.16% on independent datasets Ind-S1, Ind-S2, Ind-S3, respectively. efficacy, reliability test establish it as valuable tool researchers in academia pharmaceutical drug design.

Язык: Английский

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Leveraging Quantum LSTM for High-Accuracy Prediction of Viral Mutations

IEEE Access, Год журнала: 2025, Номер 13, С. 25282 - 25300

Опубликована: Янв. 1, 2025

Язык: Английский

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N6-methyladenine identification using deep learning and discriminative feature integration

BMC Medical Genomics, Год журнала: 2025, Номер 18(1)

Опубликована: Март 29, 2025

N6-methyladenine (6 mA) is a pivotal DNA modification that plays crucial role in epigenetic regulation, gene expression, and various biological processes. With advancements sequencing technologies computational biology, there an increasing focus on developing accurate methods for 6 mA site identification to enhance early detection understand its significance. Despite the rapid progress of machine learning bioinformatics, accurately detecting sites remains challenge due limited generalizability efficiency existing approaches. In this study, we present Deep-N6mA, novel Deep Neural Network (DNN) model incorporating optimal hybrid features precise identification. The proposed framework captures complex patterns from sequences through comprehensive feature extraction process, leveraging k-mer, Dinucleotide-based Cross Covariance (DCC), Trinucleotide-based Auto (TAC), Pseudo Single Nucleotide Composition (PseSNC), Dinucleotide (PseDNC), Trinucleotide (PseTNC). To optimize eliminate irrelevant or noisy features, unsupervised Principal Component Analysis (PCA) algorithm employed, ensuring selection most informative features. A multilayer DNN serves as classification identify accurately. robustness Deep-N6mA were rigorously validated using fivefold cross-validation two benchmark datasets. Experimental results reveal achieves average accuracy 97.70% F. vesca dataset 95.75% R. chinensis dataset, outperforming by 4.12% 4.55%, respectively. These findings underscore effectiveness reliable tool detection, contributing research advancing field biology.

Язык: Английский

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6G digital twin and CPS system promote the development of rural architectural planning

Evolving Systems, Год журнала: 2025, Номер 16(2)

Опубликована: Апрель 16, 2025

Язык: Английский

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A novel CTGAN-ENN hybrid approach to enhance the performance and interpretability of machine learning black-box models in intrusion detection and IoT

Future Generation Computer Systems, Год журнала: 2025, Номер unknown, С. 107882 - 107882

Опубликована: Май 1, 2025

Язык: Английский

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