<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Efficient prediction of anticancer peptides through deep learning"^^ . "Background\nCancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides.\n\nObjective\nThis study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods.\n\nMethods\nA diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).\n\nResults\nThe proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model’s effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences.\n\nConclusion\nThe findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment."^^ . "2024-07" . . . "10" . . "PeerJ Computer Science"^^ . . . "23765992" . . . . . . . . . . . . . . . . . . . . . . . . . "Izaz"^^ . "Ahmad Khan"^^ . "Izaz Ahmad Khan"^^ . . "Eduardo"^^ . "Garcia Villena"^^ . "Eduardo Garcia Villena"^^ . . "Farhan"^^ . "Amin"^^ . "Farhan Amin"^^ . . "Faizan"^^ . "Ullah"^^ . "Faizan Ullah"^^ . . "Isabel"^^ . "de la Torre"^^ . "Isabel de la Torre"^^ . . "Ángel Gabriel"^^ . "Kuc Castilla"^^ . "Ángel Gabriel Kuc Castilla"^^ . . "Abdu"^^ . "Salam"^^ . "Abdu Salam"^^ . . . . . . "Efficient prediction of anticancer peptides through deep learning (Texto)"^^ . . . "peerj-cs-2171.pdf"^^ . . . "Efficient prediction of anticancer peptides through deep learning (Otro)"^^ . . . . . . "indexcodes.txt"^^ . . . "Efficient prediction of anticancer peptides through deep learning (Otro)"^^ . . . . . . "lightbox.jpg"^^ . . . "Efficient prediction of anticancer peptides through deep learning (Otro)"^^ . . . . . . "preview.jpg"^^ . . . "Efficient prediction of anticancer peptides through deep learning (Otro)"^^ . . . . . . "medium.jpg"^^ . . . "Efficient prediction of anticancer peptides through deep learning (Otro)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #14369 \n\nEfficient prediction of anticancer peptides through deep learning\n\n" . "text/html" . . . "Engineering"@en . "Ingeniería"@es . .