Rapid progresses in RNA-Seq and computational methods have assisted in quantifying A-to-I RNA editing and altered RNA editing sites have been widely observed in various diseases. Nevertheless, functional characterization of the altered RNA editing sites still remains a challenge. Here, we developed perturbations of RNA editing sites (PRES; http://bio-bigdata.hrbmu.edu.cn/PRES/) as the webserver for decoding functional perturbations of RNA editing sites based on editome profiling. After uploading an editome profile among samples of different groups, PRES will first annotate the editing sites to various genomic elements and detect differential editing sites under the user-selected method and thresholds. Next, the downstream functional perturbations of differential editing sites will be characterized from gain or loss miRNA/RNA binding protein regulation, RNA and protein structure changes, and the perturbed biological pathways. A prioritization module was developed to rank genes based on their functional consequences of RNA editing events. PRES provides user-friendly functionalities, ultra-efficient calculation, intuitive table and figure visualization interface to display the annotated RNA editing events, filtering options and elaborate application notebooks. We anticipate PRES will provide an opportunity for better understanding the regulatory mechanisms of RNA editing in human complex diseases.

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets.

Relevance feedback and semantic retrieval have received extensive attention recently in the computer vision community. In this article, we present a semantic image query system with integrated feedback mechanism. Our system has two major components: the low-level feature space and the semantic space. In the low-level feature space, images are described by multidimensional vectors and are clustered based on the similarity of their contents. In the semantic space, the relationship among keywords is captured by a semantic hierarchy built by the aid of WordNet. Based on our system architecture, we propose a novel feedback solution for semantic retrieval called semantic feedback, which allows our system to interact with users directly at the semantic level. The short-term and long-term learning process of the semantic feedback substantially improves the image retrieval and annotation performance of our system. We demonstrate the effectiveness of our approach with experiments using 5,000 images from Corel database. The significant contribution of this article is in the scenario of having a relatively small training data set compared to the testing data set. Some of the previous work in the same direction has chosen a very large training set and a very small testing set. Clearly, the problem that we try to solve is more realistic and challenging.

In this paper, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. We then build a Bayesian model to solve the classification problem. To preserve the in-variation in the training data and reduce the noises, we also propose to estimate the class conditional probabilities in the feature subspace constructed by complement components analysis (CCA). We demonstrate the effectiveness of our approach through experiments in terms of annotation precision and recall.

In this paper, we propose an interactive image annotation system. The proposed system has two connected components, the low-level feature space and the semantic space. Experiments show that our system is able to provide accurate annotation for images by learning the connections between the two spaces through statistical modelling, natural language processing, and users' interaction.

In this paper we present a semantic image retrieval system with integrated feedback mechanism. In our system, we propose a novel feedback solution for semantic retrieval: semantic feedback, which allows our system to interact with users directly at the semantic level. The learning process of the semantic feedback substantially improves the image retrieval performance of the proposed system. We demonstrate the effectiveness of our approach with experiments using 5,000 images from Corel database.

In an annotated image database, keywords are usually associated with images instead of individual regions, which poses a major challenge for any region based image annotation algorithm. In this paper, we propose to learn the correspondence between image regions and keywords through Multiple-Instance Learning (MIL). After a representative image region has been learned for a given keyword, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. The classification problem is then addressed using the Bayesian framework. The proposed image annotation method is evaluated on an image database with 5,000 images.