Xiaoli Lin

3D Protein Structure Prediction with Local Adjust Tabu Search Algorithm

The protein folding structure prediction is computationally challenging and has been shown to be NP-hard when the 3D off-lattice AB model is employed. In this paper, the local adjustment tabu search (LATS) algorithm has been used to search the ground state of 3D AB off-lattice model for protein folding structure. A kind of optimization about the neighborhood scale and the annealing mechanism has been presented, where a local adjustment strategy has also been used to enhance the searching ability for the global minimum within theAB off-lattice model. Experimental results demonstrate that the proposed algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Identification of Hot Regions in Protein-Protein Interactions Based on Detecting Local Community Structure

Hot regions can help proteins to exert their biological function and contribute to understand the molecular mechanism, which is the foundation of drug designs. In this paper, combining protein biological characteristics, a new method is proposed to predict protein hot regions. Firstly, we used support vector machine to predict the hot spots. Then, the local community structure detecting algorithm based on the identification of boundary nodes was proposed to predict the hot regions in protein-protein interactions. The experimental results demonstrate that the proposed method improves significantly the predictive accuracy and performance of protein hot regions.

Identification of Hotspots in Protein-Protein Interactions Based on Recursive Feature Elimination.

The study of protein-protein interactions and protein structure through computational methods is critical to understand protein function. Hot spot residues play an important role in bioinformatics to reveal life activities. However, conventional hot spots prediction methods may face great challenges. This paper proposes a hot spot prediction method based on feature selection method SVM-RFE to improve the training performance. SMOTE based oversampling is used to adds new samples to avoid an overfitting classifier. SVM-RFE is then invoked to obtained optimal feature subset. Finally, a feature-based SVM is created to predict the hot spots. Experimental results indicate that the performance of hot spots prediction has been significantly improved compared with the previous methods.

A Novel Image Denoising Algorithm Based on Non-subsampled Contourlet Transform and Modified NLM.

A novel image denoising algorithm based on non-subsampled contourlet transform (NSCT) and modified non-local mean (NLM) is proposed. First, we utilize NSCT to decompose the images to obtain the high frequency coefficients. Second, the high frequency coefficients are used for modified NLM denoising. Finally, the NLM weight values are calculated by modified bisquare function instead of Gaussian kernel function of the traditional NLM, and each noise coefficient is corrected to get the denoised image. According to results of the simulation experiment, the denoising results of the proposed algorithm obtain higher peak signal-to-noise ratio (PSNR) and better retains structural information of image in subjective vision.

Protein Hot Regions Feature Research Based on Evolutionary Conservation

The hot regions of protein interactions refer to the activity scope where hot spots are found to be buried and tightly packing with other residues. The discovery and understanding of hot region is an important way to uncover protein functional activities, such as cell metabolism and signaling pathway, immune recognition and DNA replication, protein synthesis. In this study, machine learning method is used to discover the three aspects features of hot region from sequence conservation, structure conservation and energy conservation, which create conservation scoring algorithm though multiple sequence alignment, module substitute matrix, structural similarity and molecular dynamics simulation. This study has important theoretical and practical significance on promoting hot region research, which also provides a useful way to deeply investigate the functional activities of proteins.

Effective Identification of Hot Spots in PPIs Based on Ensemble Learning

The experiment of alanine scanning has shown that most of the binding energies in protein-protein interactions are contributed by a few significant residues at the protein-protein interfaces, and those important residues are called hot spot residues. On the basis of protein-protein interaction, hot spot residues tend to get together to form modules, and those modules are defined as hot regions. So, hot spot residues play an important role in revealing the life activities of organisms. Therefore, how to predict hot spot residues and non-spot residues effectively and accurately is a vital research direction. A new method is proposed combining protein amino acid physicochemical features and structural features to predict the hot spot residues based on the ensemble learning. The experimental results demonstrate that this method of prediction hot spot residues has a good effect.

Classification of Hub Protein and Analysis of Hot Regions in Protein-Protein Interactions

Proteins are fundamental to most biological processes, which accomplish a vast amount of functions by interacting with other proteins. The research of PPI (protein-protein interaction) and its network has developed into a great importance part in bioinformatics. In the protein-protein interaction networks, most proteins interact with only a few partners, and small number of proteins interact with many partners, these proteins are called hub proteins. The hub proteins can be divided into party hub and date hub. Therefore, in this paper, we do some works about hub proteins. In addition, this paper uses the connectivity and betweenness to classify the hub protein in protein-protein interaction network. On the other hand, the paper studies hub proteins from another perspective (interfaces conformation), which reflects the organization of hot spot residues in hub protein interface.

Effective Protein Structure Prediction with the Improved LAPSO Algorithm in the AB Off-Lattice Model

Protein structure prediction is defined as predicting the tertiary structure from the primary structure of the protein sequence. Because the real protein structure is very complex, it is necessary to adopt the simplified structure model for studying protein 3D space structure. In this paper, we introduce a kind of 3D AB off-lattice model for protein structure prediction, and the amino acids are labeled as two hydrophobic amino acids and hydrophilic amino acids. When the protein model is simplified, the optimization algorithm is also needed to use for searching the lowest energy conformation of the protein sequence based on the hypothesis theory. In this paper, a hybrid algorithm which combines PSO algorithm based on local adjust strategy (LAPSO) and genetic algorithm, was proposed to search the space structure of the protein with AB off-lattice model. Experimental results show that the minimal energy values obtained by the improved LAPSO are lower than those obtained by previous methods. The performance of our improved algorithm is better, and it can effectively solve the search problem of the protein space folding structure.

Tourism Network Comments Sentiment Analysis and Early Warning System Based on Ontology

Mine Tourism information and opinion, intelligent analysis user emotion, to improve tourism products and services, is the key to the success of tourism e-commerce. This paper embarks from the tourism network review information, researches how to build the microblog emotional vocabulary ontology and how to classify emotion based on Naive Bayes classification algorithm, implements a tourism network comments sentiment analysis and early warning system based on ontology. It not only save a large amount of manpower and material resources, but also have a certain reference value to establish reasonable tourism policy.

Identification of Hot Regions in Protein-Protein Interactions Based on SVM and DBSCAN

Hot regions are the key factor to maintain stability and coordination of protein-protein interactions. In this paper, combining evolutionary information and support vector machine (SVM), we have developed an improved method for predicting binding sites in a protein sequence. The prediction models developed in this study have been trained and tested on binding protein chains and evaluated using fold cross validation technique. The performance of this SVM model further improved. Based on the predicted hot spots, DBSCAN method is used to predict the hot regions in protein-protein interactions. The experimental results demonstrate that the proposed method improves the predictive accuracy of hot regions and is more reliable compared with previous method.