LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion

Chen, Cheng; Zhang, Qingmei; Ma, Qin; Yu, Bin

doi:10.1016/j.chemolab.2019.06.003

Cited by 190 publications

(106 citation statements)

References 78 publications

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“…The main parameters which affect the performance of the LightGBM model are the number of leaves, the learning rate, etc., instead of being obtained through training, these parameters need to be manually adjusted. These parameters were defined as hyper-parameters [28]. Traditional methods of hyper-parameter optimization include grid searching, random searching, and so on.…”

Section: Bayesian Hyper-parameter Optimizationmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

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Section: Bayesian Hyper-parameter Optimizationmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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“…In order to reduce computation and optimize feature vectors, feature selection algorithms were used for each feature. The features after fusion were normalized, and the final feature vectors were the optimal representations of the sequence (Chen et al, 2019b). Figure 1 illustrates the structure of the model.…”

Section: Model Architecturementioning

confidence: 99%

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

et al. 2020

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Motivation: The biological function of N 6-methyladenine DNA (6mA) in plants is largely unknown. Rice is one of the most important crops worldwide and is a model species for molecular and genetic studies. There are few methods for 6mA site recognition in the rice genome, and an effective computational method is needed. Results: In this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets.

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“…Proposed method 92/96 95.83 Ding's work [30] 89/96 92.71 Shen's work [35] 73/96 76.04 Zhou's work [36] 87/96 90.63 Chen's work [29] 89/96 92.71…”

Section: Wnt-related Network Proportion Accuracy (%)mentioning

confidence: 99%

“…Nowadays, the energy of the graph has been used in chemistry, bioinformatics, and related fields [27,28]. In the literature, increasing studies have shown that the physicochemical properties of amino acids can improve the prediction performances of PPIs [16,29]. The contact information among amino acids is also significant for prediction of PPIs [30].…”

Section: Introductionmentioning

confidence: 99%

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information

Xu¹,

Xu²,

Zhang³

et al. 2020

Molecules

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Identification of protein-protein interactions (PPIs) plays an essential role in the understanding of protein functions and cellular biological activities. However, the traditional experiment-based methods are time-consuming and laborious. Therefore, developing new reliable computational approaches has great practical significance for the identification of PPIs. In this paper, a novel prediction method is proposed for predicting PPIs using graph energy, named PPI-GE. Particularly, in the process of feature extraction, we designed two new feature extraction methods, the physicochemical graph energy based on the ionization equilibrium constant and isoelectric point and the contact graph energy based on the contact information of amino acids. The dipeptide composition method was used for order information of amino acids. After multi-information fusion, principal component analysis (PCA) was implemented for eliminating noise and a robust weighted sparse representation-based classification (WSRC) classifier was applied for sample classification. The prediction accuracies based on the five-fold cross-validation of the human, Helicobacter pylori (H. pylori), and yeast data sets were 99.49%, 97.15%, and 99.56%, respectively. In addition, in five independent data sets and two significant PPI networks, the comparative experimental results also demonstrate that PPI-GE obtained better performance than the compared methods.

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LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion

Cited by 190 publications

References 78 publications

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information

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