Anticancer Peptides Prediction: A Deep Learning Approach
Keywords:Deep Learning, Anticancer Peptides, KerasTuner, Hyperparameter Optimization.
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 .
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This is an open Access Article published by Research Center of Computing & Biomedical Informatics (RCBI), Lahore, Pakistan under CCBY 4.0 International License