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This paper gives an introduction to the Bayesian networks for the exploration of implementing a Bayesian belief network for an automated breast cancer detection support tool. It is intuitive that Bayesian networks can be employed as one viable option for computer-aided detection by representing the relationships between diagnoses, physical findings, laboratory test results, and imaging study findings...
Mass detection is one of the main computer-aided mammographic breast cancer detection techniques. Precisely selecting the regions that contain masses is an important step in mass segmentation using mammographic computer-aided detection. In this paper, an algorithm for extracting mass regions in digital mammograms is proposed, in which we use adaptive histogram equalization to enhance mammograms, use...
Due to experimental constraints, most microarray observations are obtained through irregular sampling. In this paper three popular spectral analyzing schemes, i.e., Lomb-Scargle, Capon and the missing data amplitude and phase estimation (MAPES), are compared in terms of their ability and efficiency to recover the periodically expressed genes. The in silico experiments based on microarray measurements...
Recent advances in high throughput microarray data have enabled the learning of the structure and operation of gene regulatory networks. This paper proposes a novel approach for reconstruction of gene regulatory networks based on the posterior probabilities of gene regulations. Built within the framework of Bayesian statistics and exploiting efficient computational Monte Carlo techniques, the proposed...
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