Anticancer Peptides Prediction: A Deep Learning Approach

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ID: 313265
2022
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Abstract
Anticancer peptides play a vital role in the treatment of cancer, due to that it has gained a lot of attention. Several machine learning and deep learning algorithms were developed for the prediction of anticancer peptides. Machine learning algorithms involves features extraction from the dataset and then model is trained to make predictions. In machine learning algorithms features extraction and the training of the model takes a lot of time and efforts, this is a complex process for biologists and biochemists. On the other hand deep learning algorithms require a large amount of dataset for training and accurate predictions. This study has proposed a deep learning algorithm which can be trained on smaller dataset because it uses hyperparameter optimization framework for the accurate predictions of anticancer peptides. The deep learning model has outperformed all the other algorithms and achieved the optimal 99% Acc and 0.982 MCC on Main dataset, 98% Acc and 0.972 MCC on Alternative dataset. The code is available at Github for validation purposes [33].
Reference Key
imported_1777060128_69ebc920132bc Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Noman Khalid
Journal Journal of Computing & Biomedical Informatics
Year 2022
DOI
10.56979/302/2022/81
URL
https://jcbi.org/index.php/Main/article/view/81
Keywords Keywords not found

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