Tao Hou

A New Hand Shape Recognition Algorithm Based on Delaunay Triangulation

In this paper, we present a new hand shape recognition algorithm based on Delaunay triangulation. When collecting hand shape images by a non-contact acquisition equipment, the degree of stretching of fingers may cause finger root contour deformation, which leads to unstable central axis and width features. Thus, we propose to form a more robust and non-parametric finger central axis extraction algorithm, by using a Delaunay triangulation algorithm. We show that our robust algorithm achieves the recognition rate of 99.89% on our database, while the mean time of feature extraction is 0.09 s.

Unsupervised Binning of Metagenomic Assembled Contigs Using Improved Fuzzy C-Means Method

Metagenomic contigs binning is a necessary step of metagenome analysis. After assembly, the number of contigs belonging to different genomes is usually unequal. So a metagenomic contigs dataset is a kind of imbalanced dataset and traditional fuzzy c-means method (FCM) fails to handle it very well. In this paper, we will introduce an improved version of fuzzy c-means method (IFCM) into metagenomic contigs binning. First, tetranucleotide frequencies are calculated for every contig. Second, the number of bins is roughly estimated by the distribution of genome lengths of a complete set of non-draft sequenced microbial genomes from NCBI. Then, IFCM is used to cluster DNA contigs with the estimated result. Finally, a clustering validity function is utilized to determine the binning result. We tested this method on a synthetic and two real datasets and experimental results have showed the effectiveness of this method compared with other tools.

A link partition approach for finding overlapping functional modules in the transcriptional regulatory network

The transcriptional regulation of cellular functions is carried out by the overlapping functional modules of a complex network. In this paper, a statistical approach for detecting functional modules in the transcriptional regulatory networks (TRNs) is studied. The proposed method defines modules as groups of links rather than nodes since nodes naturally belong to more than one module. Furthermore, the proposed algorithm is evaluated on the Escherichia coli TRN. The experimental results demonstrate that it detected a suitable number of overlapping modules that were biologically meaningful without any prior knowledge about the modules.