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As the mechanism of the effective traditional Chinese medical science treatment remains confounding, researchers seek to analyze the therapies using traditional Chinese medicine (TCM) with advanced techniques. In this paper we take advantage of the technique of data mining with the software SPSS (Statistical Product and Service Solutions), in particular the Apriori algorithm for association rules...
OpenFOAM is a widely used opensource CFD application. Based on mesh partitioned, applications can run in parallel to achieve better performance in OpenFOAM. When mesh generated from the liquid field is large, performance of partitioning algorithms will heavily affect the execution efficiency of the whole application. In this paper, we investigate the four partitioning algorithms implemented in OpenFOAM-Simple,...
Vehicle Routing Problem (VRP) is one of critical problems in modern logistics service. In order to overcome the shortcomings of the basic Ant Colony Optimization (ACO) algorithm, which has long searching time and easily jumps into local optimal solution, a hybrid behavior ACO algorithm is presented for solving the VRP problem. A series of rules of the ants' behaviors are defined. The simulation results...
Traditional methods for microarray datasets analysis often find the co-expression genes. However, these methods may miss the genes which are differential co-expression patters under different datasets. Mining these differential co-expression patterns is more valuable for inferring regulator. In this paper, we develop an algorithm, MSPattern, to mine maximal subspace differential co-expression patterns...
We present a high performance and memory efficient hardware implementation of matrix multiplication for dense matrices of any size on the FPGA devices. By applying a series of transformations and optimizations on the original serial algorithm, we can obtain an I/O and memory optimized block algorithm for matrix multiplication on FPGAs. A linear array of processing elements (PEs) is proposed to implement...
Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. Almost all the current biclustering algorithms find bicluster in one microarray dataset. In order to reduce the noise influence and find more biological biclusters, we propose an algorithm, FDCluster, to mine frequent closed discriminative bicluster in multiple microarray datasets. FDCluster uses...
Many of the previous studies show convincing arguments that mining frequent subgraphs is especially useful. Many hidden frequent patterns which are very interesting can not be found by mining single graph. Previous studies as Quasi-Clique have little success with the hub problem. In this paper, we introduce a new conception Correlated-Quasi-Clique and develop a novel algorithm, CoClique, to address...
The implementation of learning activity in online teaching directly influences the online learning quality. In this paper, we discussed the design of learning activity in online teaching, and conducted step design according to the learning activity designing pattern. And finally, we carried out a case study in order to explore how to improve online teaching quality.
The prediction of protein function is one of the most challenging problems in bioinformatics. Several studies have shown that the prediction using PPI is promising. However, the PPI data generated from high-throughput experiments are very noisy, which renders great challenges to the existing methods. In this paper, we propose an algorithm, MFC, to efficiently mine maximal frequent dense subgraphs...
Microarray technology has created a revolution in the field of biological research. Association rules can not only group the similarly expressed genes but also discern relationships among genes. However, the efficiency of traditional method to generate association rules is not very well. We develop a novel algorithm, SAW, to generate strong association rules by combining the paired rules, which can...
The prediction of protein function is one of the problems arising in the recent progress in bioinformatics. A common used approach is to derive clusters from PPI dataset. However, such results often contain false positives. In this study, we propose a novel algorithm, EVDENSE, to efficiently mine frequent dense subgraphs from PPI networks. Instead of using summary graph, EVDENSE produces frequent...
This paper makes a systematic study on disambiguating sentiment ambiguous adjectives within context in real text, which is an interaction between word sense disambiguation and sentiment analysis. We firstly address the issue of inter-annotator agreement on assigning semantic orientations to word occurrences in real text. Secondly we demonstrate that co-occurring sentiment monosemous adjectives can...
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