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The problem of defining the clustering structure in DNA methylation expressions is considered. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation data array. The model allows automatic learning of the cluster structure parameters such as the cluster mixing proportion, the models of each cluster, and especially the number of clusters. To enable the learning,...
Transcriptional regulation by transcription factors (TFs) controls when and how much RNA is created. Due to technical limitations, the protein level expressions of TFs are usually unknown, making computational reconstruction of transcriptional network a difficult task. We proposed here a novel Bayesian non-negative factor analysis approach, which is capable to estimate both the non-negative abundances...
The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) coupled with its ICM solution is proposed. BSCRFM models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes and it admits prior knowledge...
The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes. The model admits prior knowledge from existing database regarding...
60 GHz ultra wide-band (UWB) communication is an emerging technology for high speed short range communications. However, the requirement of high-speed sampling increases the cost of receiver circuitry such as analog-to-digital converter (ADC). In this paper, we propose to use a compressive sensing framework to achieve a significant reduction of sampling rate. The basic idea is based on the observation...
Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for sparse signals. For large wireless sensor networks, the events are relatively sparse compared with the number of sources. Because of deployment cost, the number of sensors is limited, and due to energy constraint, not all the sensors are turned on all the time. In this paper, the first contribution...
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