Qinghua Fang inhibits high-fat diet-induced non-alcoholic fatty liver disease by modulating gut microbiota.

Background: Disturbance of the gut microbiota may play a critical role in the progression of nonalcoholic fatty liver disease (NAFLD) induced by high-fat diet (HFD). Changes in gut microbiota were analyzed in a rat HFD-induced NAFLD model following treatment with Qinghua Fang (QHF), a Chinese herbal formula currently used in the clinical practice of traditional Chinese medicine.

Methods: Sixty Wistar rats were randomly divided into 6 groups (n=10): blank group [normal chow (NC) group], model group (HFD group), control group (BG group), Qinghua Fang high-dose group [QHF(H) group], QHF mid-dose group [QHF(M) group], QHF low-dose group [QHF(L) group].

Learning misclassification costs for imbalanced classification on gene expression data.

Background: Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights.

A Hybrid Ensemble Algorithm Combining AdaBoost and Genetic Algorithm for Cancer Classification with Gene Expression Data.

The diversity of base classifiers and integration of multiple classifiers are two key issues in the field of ensemble learning. This paper puts forward a hybrid ensemble algorithm combining AdaBoost and genetic algorithm(GA) for cancer classification with gene expression data. The decision group is designed to increase the diversity of base classifier pool, and the GA is used to assign weight to each base classifier, thus to improve the classification performance by avoiding local extrema.

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences.

Flexible focus function consisting of convex function and image enhancement filter.

We propose a new focus function Λ that, like many of the existing focus functions, consists of a convex function and an image enhancement filter. Λ is rather flexible because for any convex function and image enhancement filter, it is a focus function. We proved that Λ is a focus function using a model and Jensen's inequality.