Background: Lysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation p...
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Background: Lysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. However, traditional methods, experimental approaches, are labor-intensive and time-consuming. Computational prediction methods have been proposed recent years, and they are popular because of their convenience and high speed. In this study, we developed a new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acid composition, with a feature selection scheme (information gain). And then, it was trained using SVM (Support Vector Machine) and an ensemble learning algorithm. Results: The performance of this method was measured with an accuracy of 89.14% and a MCC (Matthew Correlation Coefficient) of 0.79 using 10-fold cross validation on training dataset and an accuracy of 84.5% and a MCC of 0.2 on independent dataset. Conclusions: The conclusions made from this study can help to understand more of the succinylation mechanism. These results suggest that our method was very promising for predicting succinylation sites.
In order to enhance navigation safety and promote environmental protection, this paper takes the problem of energy management in a ship-integrated energy system into consideration. According to the characteristics of ...
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In order to enhance navigation safety and promote environmental protection, this paper takes the problem of energy management in a ship-integrated energy system into consideration. According to the characteristics of navigation, an intelligent ship energy management model, simultaneously considering the social and economic benefits, has been proposed. Meanwhile, this paper analyzes a distributed optimal scheduling problem which considers renewable generation devices and an energy storage system. Combined with an electricity-power system and thermal-power system, we propose an optimal scheduling scheme to accurately meet the actual load demand based on the pre-results analyzed by the ensemblelearning short-term load forecasting algorithm. In addition, the related stability analysis is given. Further, a series of simulation results have been presented, which denote that the proposed load forecasting algorithm can accurately analyze the short-term load demand trend, and the proposed optimization algorithm can effectively coordinate economic and environmental protection.
Soil organic carbon (SOC) and total nitrogen (TN) are essential elements in agricultural soil and play an important role in many biological and chemical activities for plant growth. The assessment of these parameters ...
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Soil organic carbon (SOC) and total nitrogen (TN) are essential elements in agricultural soil and play an important role in many biological and chemical activities for plant growth. The assessment of these parameters in the soil is crucial in agriculture. The problem with traditional chemical analysis methods for SOC and TN is that they are time and resource consuming. In recent years, near-infrared (NIR) spectroscopy has been used as an alternative for SOC and TN determination. Accordingly, in this study, a new approach based on the ensemblelearning modelling (ELM) algorithm is used to predict SOC and TN. This approach uses six partial least squares regression (PLS) models with six pre-processing methods as learners for this method. The output of this approach is computed by averaging the predicted values computed by its constituent learners. This algorithm is used to predict the amount of SOC and TN of Moroccan soil collected from four agricultural regions using NIR. The performance of this algorithm is compared with separated regression models, namely PLS, back-propagation neural network (BPNN) with and without variable selection (VS) algorithms. using three metrics, R-2, root mean square error (RMSE), and the ratio of performance to deviation (RPD) calculated by a validation dataset. The results show that the ELM outperformed all PLS models and BPNN with and without VS for both elements. Furthermore, BPNN without VS and PLS provided better performance than PBNN with VS for SOC prediction. However, for TN, PLS gave a moderate performance according to other models (R-2 = 0.80 and RPD = 2.77). The best predictions were obtained with the E-L model for SOC (R-2 = 0.96, RMSE = 1.92, and RPD = 4.87) and TN (R-2 = 0.94, RMSE = 0.57, and RPD = 4.91), which classified the model as an excellent one for SOC and TN prediction. The proposed method ELM has the advantage of wider applicability and better performance for TN and SOC quantification by NIR spectroscopy in comparis
The reservoir rocks in the volcanic strata of the Jilin oil field are characterized by great complexity and diversity in composition and structure of lithology. To enhance the rate of lithology identification in subsu...
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The reservoir rocks in the volcanic strata of the Jilin oil field are characterized by great complexity and diversity in composition and structure of lithology. To enhance the rate of lithology identification in subsurface is very laborious. However, lithology identification is often ignored in quantitative studies, though it is the basis for reservoir characterization. In this paper, an ensemble learning algorithm named gradient boosting decision tree (GBDT) was used to establish the classification model for the volcanic lithology identification of the Lower Cretaceous Yingcheng Formation in the Songliao Basin, NE China. At the same time, support vector machine (SVM), logistic regression (LR) and decision tree (DT) classification models were also adopted in contrast with the classification accuracy of GBDT model. Subsequently, the optimal key parameters for each model were determined by employing validation curves and GridSearchCv. These results indicate that the GBDT model is superior to the single classifier and can accurately distinguish the lithologic interface of breccia tuff and rhyolite. Moreover, it also has better recognition ability for thin layer. It was concluded that the ensemble learning algorithm GBDT has significantly enhanced the accuracy of lithology identification and can be used as a lithologic identification technology.
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