Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profiles

Abstract

This study focuses on the development, metastasis, and spread of cancer diseases. It is therefore very desirable to establish deep learning method that classify cancerlectin proteins function efficiently and effectively. We used feature extraction model for physicochemical properties, such as Cancerlectins protein structure, functions, and other compounds. We propose a computational method, namely, cancerlectin two-dimensional convolutional neural networks (Lectin2D-CNN), for predicting cancerlectin proteins. Additionally, we conduct the cross-validation experiments. In addition to this approach, our paper proposes using cancerlectin two-dimensional convolutional neural networks (Lectin2D-CNN) to do image-based classification. The results indicate the proposed method Lectin2D-CNN achieved high accuracy and satisfactory specificity for comparison data sets and was superior to the compared methods. Various classifiers were used to predict cancerlectin protein functions. We developed a prediction model based on the 2D-CNN architecture to increase the recognition sensitivity and accuracy for cancerlectins. Results provide a basis for the estimation of cancer lectins and demonstrate deep learning approaches in in computational proteomics. When the Cross-validation using 2D-CNN random number generator has produced accuracy score obtain 0.7169%, Sensitivity score obtain 0.7012%, Specificity score obtain 0.7326%, MCC score obtain 0.4428%, ROC-AUC score is 0.76%, respectively, then we know we've attained a reliable result. When the Independent datasets using 2D-CNN random number generator has produced accuracy score obtain 0.6375%, Sensitivity score obtain 0.6160%, Specificity score obtain 0.6589%, MCC score obtain 0.2851%, and ROC (auc) score is 0.76%, respectively.

LGC-DBP: the method of DNA-binding protein identification based on PSSM and deep learning

“…In this section, to further illustrate the effectiveness of the proposed LGC-DBP, we will compare it with state-of-the-art methods. On the UniSwissTst test dataset, we compared our model with TargetDBP, iDNAProt-ES Chowdhury et al (2017) , TargetDBP+, MsDBP Du et al (2019) , RF-SVM Zhang et al (2022) , TPSO-DBP Sikander et al (2023) , and DBPboost. All the methods mentioned in this study utilize UniSwiss-Tr as the training dataset and Uniswiss-test as the independent test set.…”