Abstract: A counting engine for a Bayesian sequential partition system in a D-dimensional data space is provided. The counting engine includes a filtering module and a counting module. The filtering module is used for comparing at least one under-test data point with D boundary information corresponding to a sub-region, and consequently generating D flag sets. The counting module is connected with the filtering module. The counting module determines whether the at least one under-test data point lies in the sub-region, and consequently generates a result signal. A counting value corresponding to the sub-region is selectively accumulated by the counting module according to the result signal.

Abstract: A data storage device is in communication with a host through a bus. The data storage device includes a storage medium and a controlling unit. The controlling unit is connected with the host and the storage medium for receiving an analysis data, or storing a write data into the storage medium or retrieving a read data from the storage medium to the host according to a command from the host. The controlling unit includes an arithmetic logic unit. The arithmetic logic unit has a built-in algorithm for analyzing and processing the analysis data, the write data or the read data, thereby generating an analysis result. Moreover, the algorithm may be updated or expanded by the host.

Abstract: A characteristic intensity of a feature in image data generated by scanning a microarray probe is determined. A set of pixels of the image data that nominally represent the feature is identified. The pixels each have an value (such as an intensity value) associated therewith. For each of a plurality of subsets of the set of pixels, a variation statistic value is determined that corresponds to a variation in the values associated with the pixels of that subset. One of the subsets of pixels is chosen based on the determined variation statistic values. A method is also described to relate a first expression array of probes to a second expression array of probes. A subset of the probe for the arrays is determined based on a comparison of the ordering of the subset of the probes of the second array, according to a particular characteristic of the probes, to the ordering of corresponding probes in the first array according to the particular characteristic of the probes.

Abstract: A characteristic intensity of a feature in image data generated by scanning a microarray probe is determined. A set of pixels of the image data that nominally represent the feature is identified. The pixels each have an value (such as an intensity value) associated therewith. For each of a plurality of subsets of the set of pixels, a variation statistic value is determined that corresponds to a variation in the values associated with the pixels of that subset. One of the subsets of pixels is chosen based on the determined variation statistic values. A method is also described to relate a first expression array of probes to a second expression array of probes. A subset of the probe for the arrays is determined based on a comparison of the ordering of the subset of the probes of the second array, according to a particular characteristic of the probes, to the ordering of corresponding probes in the first array according to the particular characteristic of the probes.