Drug discovery is a cost expensive and time consuming process. Approved drugs have favorable or validated pharmacokinetic properties and toxicological profiles. Therefore repurposing approved drugs to new diseases can potentially avoid expensive costs associated with the early-stage testing. In this work, we propose to integrate drug-drug, disease-disease, gene-gene and drug-disease associations to reposition the approved drugs. Firstly, multiple sources of data are integrated into three layers: the chemical/phenotype layer, the gene/machanism layer and the treatment layer. Secondly, the drug-drug and disease-disease similarities in three layers are respectively computed and then combined together. Finally, based on the hypothesis that similar drugs may treat similar diseases, we model the drug repositioning problem as an optimal problem and propose a semi-supervised graph cut (SSGC) algorithm to solve the problem. The experimental results show that integrating multiple sources of data can achieve better performances than only considering single kind of data, and our method outperforms three representative approaches. Moreover, the predicted top-ranked repositioned relations have been reported in literature, illustrating the usefulness of our method in practice. The predicted results are available at https://github.com/wgs666/SSGC.git.

Prediction the interactions between proteins (targets) and small molecules (ligands) is a critical task for the drug discovery in silico. In this work, we consider the target binding site instead of the whole target and propose a pairwise input neural network (PINN) for constructing the site-ligand interaction prediction model. Different with the ordinary artificial neural network (ANN) with one vector as input, the proposed PINN can accept a pair of vectors as the input, corresponding to a binding site and a ligand respectively. The 5-CV evaluation results show that PINN outperforms other representative target-ligand interaction prediction methods.

In this paper, we propose a novel method to estimate the slant of omnidirectional image sensors which can acquire images of an environment with a FOV close to 360/spl deg/ /spl times/ 180/spl deg/. The proposed method consists of two steps: a low resolution voting followed by a least squares method (LSM). First, the direction of each edge pixel in the omnidirectional image is computed and projected to the plane of Z = 1. The biggest peak formed by the vertical edges, which are prevalent in indoor and urban scenes, is detected. Then, edge directions whose projections to the voting plane pass near the peak are used to estimate the slant of the sensor by LSM. Experimental results in a real environment show the effectiveness of the proposed method.

This paper describes a novel algorithm for robust object detection and segmentation, which is based on a new robust dissimilarity measure called as radial reach correlation (RRC). The capability of detecting moving objects from a complex background is one of the most fundamental technology for intelligent surveillance systems. The RRC is a new robust dissimilarity measure and has a well-formed probabilistic model of binary or normal density. The RRC evaluates the local texture between a background image and the current scene and realize robust object detection under poor conditions. To demonstrate the effectiveness of our approach, we present experimental results from real world all-directional images provided by the stereo omni-directional system (SOS).

In this research, we introduce a reasonable noise model for range data which is obtained by a laser radar range finder, and derive two simple approximate solutions of optimal local plane fitting the range data under the noise model. We compare our methods with general least-squares based methods, such as Z-function fitting, the eigenvalue method, the maximum likelihood estimation method, and the renormalization method, an iterative method to obtain the optimal fitting of planes of range data under the noise model. All the methods are compared and evaluated using both synthetic range data and real range data with ground truth. From the experimental evaluation results, the proposed methods are shown to be effective, and the general least-squares-based methods are shown to be unsuitable for the assumed noise model.

A new approach is proposed to deal with the stereo matching problem. Based on the assumption that the scene is composed of some piecewise continuous surfaces, two stereo images can be considered to be composed of patches which can be matched continuously between two images. To find the matching of such patches, we divide the range of the depth in the scene into small intervals, and find continuous matchings of segments into the epipolar line in each small interval. Then the continuously matched segments are merged into patches, by considering the continuity between epipolar lines. The matchings of patches obtained in individual intervals, which may partially conflict or overlap with each other, are integrated to obtain the matchings of patches in the whole scene by considering the consistency among the matchings of patches.

An efficient graph matching approach is proposed for finding region correspondence between two images of the same scene but taken from different viewpoints. Regions and their relations in an image are represented with region adjacency graph (RAG), which is a kind of attributed planar graph. The problem to find an optimal region correspondence, which matches the regions in two images with maximal similarity in region features and region relations, is formulated into the problem to find the optimal inexact matching between two RAGs. The properties specific to planar graph and that of the region adjacency relations are utilized to invent an efficient algorithm to solve the problem. Experimental results on various kinds of images show the effectiveness of the method.>

A thinning algorithm is proposed for gray-scale images, which applies a combination of sequential and parallel conditions for pixel removal. The authors found that the algorithm developed by Salari and Siy (1984) produces unsuitable results in certain cases, due to its pure sequential processing. The proposed algorithm in this paper is an improvement of theirs and, at the same time, is a gray-image equivalent of Hilditch's thinning algorithm. Two versions are available for deriving either 4-connected or 1-connected core lines.>

A gray-scale thinning algorithm is proposed which is applicable to the case where a whole given image should be considered as the domain for thinning. The proposed algorithm is a modified version of the one proposed by the authors, which was intended for the case where a region to be thinned is specified in a given image. As an application of the method, extraction of a 'thin' edge curve from an edge-intensity image is presented.>