Yongna Yuan

Prediction of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas based on the heuristic method, support vector machine and projection pursuit regression.

Quantitative structure-activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas using linear free energy relationship (LFER). Eight molecular descriptors selected by the heuristic method (HM) in CODESSA were used as inputs to perform multiple linear regression (MLR), support vector machine (SVM) and projection pursuit regression (PPR) studies. Compared with MLR model, the SVM and PPR models give better results with the predicted correlation coefficient (R(2)) of 0.867 and 0.834 and the squared standard error (s(2)) of 0.095 and 0.119 for the training set and R(2) of 0.732 and 0.726 and s(2) of 0.210 and 0.207 for the test set, respectively. It indicates that the SVM and PPR approaches are more adapted to the set of molecules we studied. In addition, methods used in this paper are simple, practical and effective for chemists to predict the human CCR5 chemokine receptor.

Identifying the most influential spreaders in complex networks by an Extended Local K-Shell Sum

Identifying influential spreaders is crucial for developing strategies to control the spreading process on complex networks. Following the well-known K-Shell (KS) decomposition, several improved measures are proposed. However, these measures cannot identify the most influential spreaders accurately. In this paper, we define a Local K-Shell Sum (LKSS) by calculating the sum of the K-Shell indices of the neighbors within 2-hops of a given node. Based on the LKSS, we propose an Extended Local K-Shell Sum (ELKSS) centrality to rank spreaders. The ELKSS is defined as the sum of the LKSS of the nearest neighbors of a given node. By assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performance between the ELKSS centrality and other six measures. The results show that the ELKSS centrality has a better performance than the six measures to distinguish the spreading ability of nodes and to identify the most influential spreaders accurately.

A BPSO-SVM algorithm based on memory renewal and enhanced mutation mechanisms for feature selection

Abstract Feature selection (FS) is an essential component of data mining and machine learning. Most researchers devoted to get more effective method with high accuracy and fewer features, it has become one of the most challenging problems in FS. Certainly, some algorithms have been proven to be effectively, such as binary particle swarm optimization (BPSO), genetic algorithm (GA) and support vector machine (SVM). BPSO is a metaheuristic algorithm having been widely applied to various fields and applications successfully, including FS. As a wrapper method of FS, BPSO-SVM tends to be trapped into premature easily. In this paper, we present a novel mutation enhanced BPSO-SVM algorithm by adjusting the memory of local and global optimum (LGO) and increasing the particles’ mutation probability for feature selection to overcome convergence premature problem and achieve high quality features. Typical simulated experimental results carried out on Sonar, LSVT and DLBCL datasets indicated that the proposed algorithm improved the accuracy and decreased the number of feature subsets, comparing with existing modified BPSO algorithms and GA.

Using Deep Learning for Compound Selectivity Prediction

Compound selectivity prediction plays an important role in identifying potential compounds that bind to the target of interest with high affinity. However, there is still short of efficient and accurate computational approaches to analyze and predict compound selectivity. In this paper, we propose two methods to improve the compound selectivity prediction. We employ an improved multitask learning method in Neural Networks (NNs), which not only incorporates both activity and selectivity for other targets, but also uses a probabilistic classifier with a logistic regression. We further improve the compound selectivity prediction by using the multitask learning method in Deep Belief Networks (DBNs) which can build a distributed representation model and improve the generalization of the shared tasks. In addition, we assign different weights to the auxiliary tasks that are related to the primary selectivity prediction task. In contrast to other related work, our methods greatly improve the accuracy of the compound selectivity prediction, in particular, using the multitask learning in DBNs with modified weights obtains the best performance.

Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible–infectious–recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

Link prediction via layer relevance of multiplex networks

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

Link prediction based on local weighted paths for complex networks

As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.