ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides - 19/12/20
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Graphical abstract |
Highlights |
• | An artificial neural network model ENNAACT was created for the pre-diction of anticancer peptides (ACPs). |
• | Compositional and physicochemical properties was extracted from bio-logical sequences. |
• | Machine learning classifiers such as SVM, RF and neural networks were employed for classification. |
• | The ENNAACT model was evaluated by 10-fold cross-validation and in-dependent test datasets. |
• | ENNAACT exceeded the predictive performance of existing state-of-the-art methods. |
Abstract |
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.
Le texte complet de cet article est disponible en PDF.Keywords : Neural network, Machine learning, Anticancer drugs, Peptides, In silico screening
Plan
Vol 133
Article 111051- janvier 2021 Retour au numéro
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