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primary topic: Efficient prediction of anticancer peptides through deep learning

Efficient prediction of anticancer peptides through deep learning
type: AcademicArticle
type: Article
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
volume: 10
is part of: PeerJ Computer Science
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Efficient prediction of anticancer peptides through deep learning (Texto)
has document: Efficient prediction of anticancer peptides through deep learning (Otro)
subject: Engineering

Efficient prediction of anticancer peptides through deep learning (Texto)
type: Document
has file: peerj-cs-2171.pdf
has part: peerj-cs-2171.pdf

PeerJ Computer Science
type: Collection
issn: 23765992

Eduardo Garcia Villena
type: Person
given name: Eduardo
family name: Garcia Villena

Faizan Ullah
type: Person
given name: Faizan
family name: Ullah

Izaz Ahmad Khan
type: Person
given name: Izaz
family name: Ahmad Khan

Farhan Amin
type: Person
given name: Farhan
family name: Amin

Isabel de la Torre
type: Person
given name: Isabel
family name: de la Torre

Ángel Gabriel Kuc Castilla
type: Person
given name: Ángel Gabriel
family name: Kuc Castilla

Abdu Salam
type: Person
given name: Abdu
family name: Salam

Efficient prediction of anticancer peptides through deep learning (Otro)
type: Document
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Efficient prediction of anticancer peptides through deep learning (Otro)
type: Document
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Efficient prediction of anticancer peptides through deep learning (Otro)
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Efficient prediction of anticancer peptides through deep learning (Otro)
type: Document
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Efficient prediction of anticancer peptides through deep learning (Otro)
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primary topic: Efficient prediction of anticancer peptides through deep learning

Engineering
type: Concept
preferred label: Ingeniería @es