Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 229, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.chemolab.2022.104639
Keywords
DNA -binding proteins; Extremely randomized trees; Convolutional neural network
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Funding
- Deanship of Scientific Research at King Khalid University [RGP.2/198/43]
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In this study, a deep learning-based predictor (DBP-DeepCNN) is proposed to improve the prediction of DNA-binding proteins (DBPs). By using a novel feature extraction method and training with various models, the predictor achieved higher accuracies on both training and independent datasets, indicating its potential for large scale DBP prediction and promising therapeutic strategies for chronic diseases.
DNA-binding proteins (DBPs) are highly concerned with several types of cancers (lung, breast, and liver), other fatal diseases (AIDS/HIV, asthma), and are used in the designing of drug. A series of predictors were constructed for identification of DBPs. However, a more accurate computational predictor is still essential for further per-formance improvement. In this work, a deep learning-based predictor (DBP-DeepCNN) is proposed for improving DBPs prediction. The salient features are derived by a novel method, namely, R-PSSM-DWT (Reduced position -specific scoring matrix-discrete wavelet transform) as well as Lead-BiPSSM (Lead-bigram-position specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), ED-PSSM (Evolutionary difference position specific scoring matrix), and F-PSSM (Filtered position specific scoring matrix). Further, the models are trained with 2D CNN (two-dimensional convolutional neural network), XGB (eXtreme gradient boosting), Adaboost, and ERT (extremely randomized trees). 2D CNN-based model with R-PSSM-DWT produced 6.92% and 1.32% higher accuracies than existing approach on training and independent datasets, respectively. These outcomes verified the superlative success rate of our novel predictor over the existing studies. In addition to being a promising method for large scale prediction of DBPs. DBP-DeepCNN would be fruitful for establishing more promising therapeutic strategies for chronic disease treatment.
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