Shunyao Wu

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

Network Propagation with Dual Flow for Gene Prioritization

Based on the hypothesis that the neighbors of disease genes trend to cause similar diseases, network-based methods for disease prediction have received increasing attention. Taking full advantage of network structure, the performance of global distance measurements is generally superior to local distance measurements. However, some problems exist in the global distance measurements. For example, global distance measurements may mistake non-disease hub proteins that have dense interactions with known disease proteins for potential disease proteins. To find a new method to avoid the aforementioned problem, we analyzed the differences between disease proteins and other proteins by using essential proteins (proteins encoded by essential genes) as references. We find that disease proteins are not well connected with essential proteins in the protein interaction networks. Based on this new finding, we proposed a novel strategy for gene prioritization based on protein interaction networks. We allocated positive flow to disease genes and negative flow to essential genes, and adopted network propagation for gene prioritization. Experimental results on 110 diseases verified the effectiveness and potential of the proposed method.

Weighted Bipartite Network Projection for Personalized Recommendations

Abstract—To reduce the difficulty of personalized recommendations, the traditional network-based method constructed bipartite networks with stronger links (higher ratings). However, weaker links and link weights were almost ignored. Although the existing method effectively mined users’ preferences, it was impossible to catch users’ disgusts. Therefore, this paper proposed a novel method to effectively discover users’ preferences and disgusts. Experimental results on the MovieLens dataset demonstrated that the proposed method was much more superior to the baseline method under the diversity index.

Enteromorpha Prolifera Detection with MODIS Image Using Semi-supervised Clustering

In recent years, enteromorpha prolifera detectionhas received increasing attention. Supervised learning withremote sensing images can achieve satisfactory performancesfor green tide monitoring. However, data distributions betweenimages obviously differ, and it would be too costlyto label a massive amount of images for enteromorphaprolifera detection. Thus, this paper focuses on detectingenteromorpha prolifera using not only limited labelled data,but also a large amount of unlabelled data. We propose aneffective semi-supervised clustering framework for enteromorphaprolifera detection, which can reduce the labellingcost and alleviate the overfitting problem. Experimentalresults prove the effectiveness and potential of our approach,with almost a 15% increase from baseline. In addition, theproposed approach can provide quantitative assessments forband data of moderate resolution imaging spectroradiometer(MODIS) images, and several often ignored bands, such asbands 5, 6, and 7, are shown to be useful for enteromorphaprolifera detection.