In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.
Today, online stores collect a lot of customer feedback in the form of surveys, reviews, and comments. This feedback is categorized and in some cases responded to, but in general it is underutilizedeven though customer satisfaction is essential to the success of their business. In this paper, we introduce several new techniques to interactively analyze customer comments and ratings to determine the positive and negative opinions expressed by the customers. First, we introduce a new discrimination-based technique to automatically extract the terms that are the subject of the positive or negative opinion (such as price or customer service) and that are frequently commented on. Second, we derive a Reverse-Distance-Weighting method to map the attributes to the related positive and negative opinions in the text. Third, the resulting high-dimensional feature vectors are visualized in a new summary representation that provides a quick overview. We also cluster the reviews according to the similarity of the comments. Special thumbnails are used to provide insight into the composition of the clusters and their relationship. In addition, an interactive circular correlation map is provided to allow analysts to detect the relationships of the comments to other important attributes and the scores. We have applied these techniques to customer comments from real-world online stores and product reviews from web sites to identify the strength and problems of different products and services, and show the potential of our technique.
The aim of the present study was to evaluate whether an exercise intervention, nutrition education, or the combination of both were effective in weight reduction and maintenance for rural school children. Two hundred twenty-nine primary school children aged 9 to 12 years determined as overweight/obese were randomly assigned to 1 of 4 groups: exercise intervention, nutrition education, combination of both, and control. Nutrition education and rope-skipping sessions were performed for 2 months. Anthropometric measurements were administered at baseline, after 2 months (postintervention), and 1 year later (follow-up). The order of change from high to low in the body mass index standard deviation scores (BMI-SDS) between postintervention and baseline was combined intervention, exercise intervention, and nutrition education. The BMI-SDS between following-up and baseline was for combined intervention, exercise intervention, and nutrition education. The combined intervention had the best short-term and long-term effects. The exercise intervention had a better short-term effect than nutrition education, while nutrition education had a better long-term effect than the exercise intervention.
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