5105796
Two-stage normalization using background intensities in cDNA microarray data
BackgroundIn the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process.ResultsIn this paper, we propose a two-stage method adjusting for the effect of background intensities in the normalization process. The first stage fits a regression model to adjust for the effect of background intensities and the second stage applies the usual normalization method such as a nonlinear LOWESS method to the background-adjusted intensities. In order to carry out the two-stage normalization method, we consider nine different background measures and investigate their performances in normalization. The performance of two-stage normalization is compared to those of global median normalization as well as intensity dependent nonlinear LOWESS normalization. We use the variability among the replicated slides to compare performance of normalization methods.ConclusionsFor the selected background measures, the proposed two-stage normalization method performs better than global or intensity dependent nonlinear LOWESS normalization method. Especially, when there is a strong relationship between the background intensity and the signal intensity, the proposed method performs much better. Regardless of background correction methods used in the image analysis, the proposed two-stage normalization method can be applicable as long as both signal intensity and background intensity are available.
731257
A Novel Framework for Discovering Robust Cluster Results
We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various clustering algorithms. The proposed method combines partitions of various clustering algorithms by means of newly-proposed the selection and the crossover operation of the genetic algorithm (GA) during the evolutionary process.
10239106
Programmable ultrasound imaging using multimedia technologies: a next-generation ultrasound machine
High computational and throughput requirements in modern ultrasound machines have restricted their internal design to algorithm-specific hardware with limited programmability. The authors have architected a programmable ultrasound processing system, Programmable Ultrasound Image Processor (PUIP), to facilitate engineering and clinical ultrasound innovations. Multiple high-performance multimedia processors were used to provide a computing power of 4 billion operations per second. Flexibility was achieved by making the system programmable and multimodal, e.g., B-mode, color flow, cine and Doppler data can be processed. They have successfully designed and implemented the PUIP to fit within an ultrasound machine. It provides a platform for rapid testing of new concepts in ultrasound processing and enables software upgrades for future technologies. Current and future clinical applications include extended fields of view, quantitative measurements, three-dimensional ultrasound reconstruction and visualization, adaptive persistence, speckle reduction, edge enhancement, image segmentation, and motion analysis. The PUIP is a significant step in the evolution of ultrasound machines toward more flexible and generalized systems bridging the gap between many innovative ideas and their clinical use in ultrasound machines.
1231793
An effective data clustering measure for temporal selection and projection queries
Abstract Temporal databases (TDBs) allow users to record and retrieve time-varying data objects. Since TDBs usually manage a huge amount of underlying data objects, efficient disk accesses are essential for fast response time in temporal query processing. Data clustering is one of the most effective techniques that can improve performance of TDB systems. However, clustering measures for conventional data objects are not appropriate to temporal data objects because it is important to exploit temporal properties of underlying data objects and temporal queries as data clustering criteria. In this paper, we propose a data clustering measure called temporal affinity that can be used for effective temporal data clustering. The temporal affinity, which is based on the semantics of temporal operators, reflects the closeness among temporal data objects with respect to temporal query processing. We perform experiments to show the effectiveness of the proposed temporal data clustering measure. The experimental results indicate that a data clustering method based on the temporal affinity works better than other methods.
1710897
A low latency router supporting adaptivity for on-chip interconnects
The increased deployment of system-on-chip designs has drawn attention to the limitations of on-chip interconnects. As a potential solution to these limitations, networks-on-chip (NoC) have been proposed. The NoC routing algorithm significantly influences the performance and energy consumption of the chip. We propose a router architecture which utilizes adaptive routing while maintaining low latency. The two-stage pipelined architecture uses look ahead routing, speculative allocation, and optimal output path selection concurrently. The routing algorithm benefits from congestion-aware flow control, making better routing decisions. We simulate and evaluate the proposed architecture in terms of network latency and energy consumption. Our results indicate that the architecture is effective in balancing the performance and energy of NoC designs.
6065142
On effective data clustering in bitemporal databases
Temporal databases provide built-in supports for efficient recording and querying of time-evolving data. In this paper, data clustering issues in temporal database environment are addressed. Data clustering is one of the most effective techniques that can improve performance of a database system. However, data clustering methods for conventional databases do not perform well in temporal databases because there exist crucial differences between their query patterns. We propose a data clustering measure, called Temporal Affinity, that can be used for the clustering of temporal data. The temporal affinity, which is based on the analysis of query patterns in temporal databases, reflects the closeness of temporal data objects in viewpoints of temporal query processing. We perform experiments to evaluate the proposed measure. The experimental results show that a data clustering method with the temporal affinity works better than other methods.
18235969
Learning Relations and Information Extraction Rules for Protein Annotation
Protein annotation is a task that describes protein X in terms of topic Y Until now, most of protein annotation work has been done manually by human annotators. However, as the number of biomedical papers grows ever rapidly, manual annotation becomes difficult, and there is increasing need to automate the protein annotation process. Recently, Information Extraction (IE) has been used to solve this problem. Typically, IE requires pre-defined relations and hand-crafted IE rules or annotated corpora, and these requirements are difficult to satisfy in real world domains such as the biomedical domain. In this paper, we describe an IE system which requires only sentences labeled relevant or not to a given topic by domain experts.