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This paper integrates the signal, context, and structure features for genome-wide human promoter recognition, which is important in improving genome annotation and analyzing transcriptional regulation without experimental supports of ESTs, cDNAs, or mRNAs. First, CpG islands are salient biological signals associated with approximately 50 percent of(More)
Many cellular processes exhibit periodic behaviors. Hence, one of the important tasks in gene expression data analysis is to detect subset of genes that exhibit cyclicity or periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length, with very few periods, irregularly(More)
—The batch latent Dirichlet allocation (LDA) algorithms play important roles in probabilistic topic modeling, but they are not suitable for processing big data streams due to high time and space compleixty. Online LDA algorithms can not only extract topics from big data streams with constant memory requirements, but also detect topic shifts as the data(More)
This paper presents a novel human body part tracker called sequential Markov random fields (SMRFs), which can be used to extract spatiotemporal features in human action recognition. Given a video sequence of human action in the monocular settings, SMRF can effectively detect the key spatiotemporal feature points on human body parts. We also develop(More)