Lihong Peng

Learn More
BACKGROUND Increasing evidence shows that schizophrenia patients with long-term exposure to antipsychotic medications have decreased bone mass, which suggests that they are at a high risk of osteoporosis. However, the mechanism underlying this remains unclear. In this study, we selected two bone turnover markers to explore whether atypical antipsychotics(More)
In this paper, we propose a Fast Locality-constrained low-rank sparse coding for image classification. The low-rank coding seeks the homogeneousness and correlation of local features, encodes jointly and globally, based on the traditional low-rank coding, we incorporate locality constraints to enforce the local features sharing the same representation.(More)
We presented a network comparison algorithm for predicting the conservative interaction regions in the cross-species protein-protein interaction networks (PINs). In the first place, We made use of the correlated matrix to represent the PINs. Then we standardized the matrix and changed it into a unique representation to facilitate to judge whether the(More)
Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified.(More)
FAST TCP is a newly developed transfer control protocol for future networks with high bandwidth delay product. At present, analyzing the stability of FAST TCP in different networks is an active research area. We establish a group of nonlinear delay differential equations to describe the window adjustment algorithm of FAST TCP and the queuing behavior of(More)
Efficient mining of high-throughput data has become one of the popular themes in the big data era. Existing biology-related feature ranking methods mainly focus on statistical and annotation information. In this study, two efficient feature ranking methods are presented. Multi-target regression and graph embedding are incorporated in an optimization(More)
This paper analyses the intrinsic relationship between the BP network learning ability and generalization ability and other influencing factors when the overfit occurs, and introduces the multiple correlation coefficient to describe the complexity of samples; it follows the calculation uncertainty principle and the minimum principle of neural network(More)
Traditional congestion control algorithms exhibit low convergence speed to equilibrium in high BDP (Bandwidth Delay Product) networks. The Fast Max-Min Kelly Control (FMKC) is a new and promising protocol that performs well especially in fairness convergence speed. FMKC utilizes packet loss to switch temporarily into a fairing mode and thereby improve the(More)
In this paper, we propose a problem named as MSP, prove its NP-completeness, and design an algorithm to solve it. To prove the algorithm, we define a linear order to align all the instances of the problem, and also define a so-called splitting transform to get a smaller graph. Using the linear order, we prove the non-existence of the smallest graph which(More)
Inferring drug-target interaction (DTI) candidates for new drugs or targets without any interaction information is a critical challenge for modern drug design and discovery. A few approaches are applied to solve this problem. Results from these applications indicate that the existing DTI inference methods necessitate further improvement. The sparse,(More)