Jian-Sheng Wu

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Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the vast majority of proteins can only be annotated computationally. Nature often brings several domains together to form multidomain and multi-functional proteins with a vast number of possibilities, and each domain may fulfill its own function(More)
Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we(More)
—Recently, co-clustering has become a topic of much interest because of its applications to many problems. It has been proved more effective than one-way clustering methods. But the existing co-clustering approaches just treat the document as a collection of words, disregarding the word sequences. They only consider the co-occurrence counts of words and(More)
The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand how miRNAs silence their target genes. It is difficult to use existing computational method to predict miRNA-binding residues in proteins due to the lack of training examples. To address this issue, unlabeled data may be exploited to help construct a computational(More)
By always mapping data from lower dimensional space into higher or even infinite dimensional space, kernel k-means is able to organize data into groups when data of different clusters are not linearly separable. However, kernel k-means incurs the large scale computation due to the representation theorem , i.e. keeping an extremely large kernel matrix in(More)
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