Zong-Xian Yin

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When analyzing the results of microarray experiments, biologists generally use unsupervised categorization tools. However, such tools regard each time point as an independent dimension and utilize the Euclidean distance to compute the similarities between expressions. Furthermore, some of these methods require the number of clusters to be determined in(More)
—This paper presents a new fuzzy cover-based clustering algorithm. In the proposed algorithm, the concept of fuzzy cover and objective function are employed to identify holding points in the dataset, and we associate these holding points together to build up the backbones of the final clusters. Three specific objectives underlie the presentation of the(More)
This paper presents a new partitioning algorithm, adaptive c-population clustering (ACP) algorithm, which can be used to identify natural subgroups, as well as influential minor prototypes, in the unlabeled data set. The traditional fuzzy c-mean clustering algorithm is primarily a prototype-based clustering algorithm, whereas the ACP algorithm is able to(More)
OBJECTIVE Existing bioinformatics databases such as KEGG (Kyoto Encyclopedia of Genes and Genomes) provide a wealth of information. However, they generally lack a user-friendly and interactive interface. METHODOLOGY The study proposes a web service system for exploring the contents of the KEGG database in an intuitive and interactive manner. In the(More)
—The Possibilistic Latent Variable (PLV) clustering algorithm is a powerful tool for the analysis of complex datasets due to its robustness toward data distributions of different types and its ability to accurately identify the inherent clusters within the data. The scaling coefficient in the PLV algorithm plays a key role in reducing the effects of noise,(More)
We have developed a gene-gene (G-G) relation browser that combines fuzzy sequential pattern mining and information-extraction model to extract from biomedical literature knowledge on gene-gene interactions. Our approach aims to detect associated G-G relations that are often discussed in documents. Integration of the related relations will lead to an(More)
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