Autism is a complex neuropsychiatric disorder with high heritability and an unclear etiology. The identification of key genes related to autism may elucidate its etiology. The current study provides an approach to predicting autism susceptibility genes. Genes are first extracted from the biomedical literature, and some autism susceptibility genes are then recognized as seeds by the prior knowledge. As candidates, the remaining genes are predicted by creating association rules between the seeds and candidates. In an evaluated data set, 27 autism susceptibility genes (type "Y") are extracted and 43 possible autism susceptibility genes (type "P") are predicted. The sum of "Y" and "P" genes accounts for 93.3% of the data set that are not contained in the typical database of autism susceptibility genes. Our approach can effectively extract and predict autism susceptibility genes from the biomedical literature. These predicted results complement the typical database of autism susceptibility genes. The web portal for the predicted results, which is freely available at http://biolab.hyit.edu.cn/ar, can be a valuable resource in studies of diseases related to genes.

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In this article, we propose a novel model to overcome the drawbacks of the modified Chan–Vese (MCV) model. Our model is devoted to find an optimal partition of inhomogeneous regions accurately and computationally efficient. MCV model was proposed on the concept of using one level‐set function for one region. It needs fewer numbers of iterations and improves the efficiency of image segmentation in contrast to the multiphase Chan–Vese model. The MCV model, however, is highly dependent on initial curves placement and often leads to erroneous segmentations on images with intensity inhomogeneity. In our model, to eliminate the affection of background information on the curve evolution and speed up the curve evolution, we first use the k‐means algorithm to presegment the image to get the initial curves and then add the local image information to the total energy functional of MCV model to deal with the intensity inhomogeneity. Finally, extensive experiments are done and the segmentation results on homogeneous multiphase images verify that the proposed method has the better accuracy and efficiency comparing to MCV model. Moreover, we show results on challenging multiphase inhomogeneous image to illustrate the robust and accurate segmentation that are possible with this novel model. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 103–113, 2012

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To improve the visual quality and the embedding rate of the existing reversible image watermarking algorithm, an improved reversible image watermarking algorithm based on difference expansion is proposed. First, the watermark information is divided into groups, and the information value of each group is calculated. The watermark group number and the corresponding carrier image block number are mapped, and the corresponding coefficient position of each corresponding carrier block is identified according to the value of the watermark information in each group. Second, the identified location map is compressed and embedded in the original image through the difference expansion. Through circular searching the suitable pixel position, the embedding rate can be effectively improved without sacrificing any visual quality. The experimental results show that the proposed algorithm not only has high embedding rate but also has a high visual quality and can achieve full recovery of the original image. Compared with other algorithms, the algorithm has certain advantages.

A saliency detection algorithm based on manifold-based similarity adaptation is proposed. The proposed algorithm is divided into three steps. First, we segment an input image into superpixels, which are represented as the nodes in a graph. Second, a new similarity measurement is used in the proposed algorithm. The weight matrix of the graph, which indicates the similarities between the nodes, uses a similarity-based method. It also captures the manifold structure of the image patches, in which the graph edges are determined in a data adaptive manner in terms of both similarity and manifold structure. Then, we use local reconstruction method as a diffusion method to obtain the saliency maps. The objective function in the proposed method is based on local reconstruction, with which estimated weights capture the manifold structure. Experiments on four bench-mark databases demonstrate the accuracy and robustness of the proposed method.

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