eprintid: 14369 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/43/69 datestamp: 2024-09-24 23:30:15 lastmod: 2024-09-24 23:30:16 status_changed: 2024-09-24 23:30:15 type: article metadata_visibility: show creators_name: Salam, Abdu creators_name: Ullah, Faizan creators_name: Amin, Farhan creators_name: Ahmad Khan, Izaz creators_name: Garcia Villena, Eduardo creators_name: Kuc Castilla, Ángel Gabriel creators_name: de la Torre, Isabel creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: angel.kuc@uneatlantico.es creators_id: title: Efficient prediction of anticancer peptides through deep learning ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica full_text_status: public keywords: Anticancer peptides, Protein identification, Biological sequence analysis, Machine learning, Artificial intelli-gence, Neural networks, Natural language processing, Disease diagnosis, Image classification abstract: Background Cancer 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. Objective This 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. Methods A 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). Results The 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. Conclusion The 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. date: 2024-07 publication: PeerJ Computer Science volume: 10 pagerange: e2171 id_number: doi:10.7717/peerj-cs.2171 refereed: TRUE issn: 2376-5992 official_url: http://doi.org/10.7717/peerj-cs.2171 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Abierto Inglés Background Cancer 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. Objective This 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. Methods A 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). Results The 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. Conclusion The 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. metadata Salam, Abdu; Ullah, Faizan; Amin, Farhan; Ahmad Khan, Izaz; Garcia Villena, Eduardo; Kuc Castilla, Ángel Gabriel y de la Torre, Isabel mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, angel.kuc@uneatlantico.es, SIN ESPECIFICAR (2024) Efficient prediction of anticancer peptides through deep learning. PeerJ Computer Science, 10. e2171. ISSN 2376-5992 document_url: http://repositorio.uneatlantico.es/id/eprint/14369/1/peerj-cs-2171.pdf