Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm

Abstract

The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods.

DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform

“…Several methods have been implemented for the identification of DBPs. The proposed work is compared with past studies including iDNA-Prot [ 22 ], iDNA-Prot|dis [ 23 ], TargetDBP [ 59 ], MsDBP [ 60 ], PDBP-CNN [ 29 ], and XGBoost [ 30 ] and summarized the results in Table 4 . Our proposed study improved the accuracy by 4.82%, sensitivity by 10.58%, and MCC by 0.09 than the best predictor (PDBP-CNN).…”

“…Consequently, Li et al extracted features by a convolutional neural network (CNN) and Bi-LSTM [ 29 ]. Onward, Zhao et al the features of the proteins are analyzed by six methods and classification is performed with XGBoost [ 30 ]. Each computational method contributed well to enhancing the prediction of DBPs.…”

Immune landscape-based machine-learning–assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma

“…It can be used for regression, classification, and ranking problems. It is a ML model that integrates multiple weak learners to achieve a stronger learning effect ( 43 ). Compared with other traditional ML algorithms, XGBoost is highly scalable and flexible.…”

A Real‐time Lithological Identification Method based on SMOTE‐Tomek and ICSA Optimization

“…Different from the GBDT algorithm, which only uses the first derivative when optimizing the loss function, XGB is a second-order Taylor expansion of the loss function and adds the regular term to the objective function. Since the loss function of XGB can introduce regularization terms, the complexity of the model can be controlled, the variance of the model can be reduced, and the overfitting of the model can be prevented (Zhao et al, 2022). After each iteration, the learning rate is assigned to leaf nodes to reduce the weight of each tree, reduce the influence of each tree, and provide a better learning space for the following.…”