13639231
Extracting contrastive information from negation patterns in biomedical literature
Expressions of negation in the biomedical literature often encode information of contrast as a means for explaining significant differences between the objects that are so contrasted. We show that such information gives additional insights into the nature of the structures and/or biological functions of these objects, leading to valuable knowledge for subcategorization of protein families by the properties that the involved proteins do not have in common. Based on the observation that the expressions of negation employ mostly predictable syntactic structures that can be characterized by subclausal coordination and by clause-level parallelism, we present a system that extracts such contrastive information by identifying those syntactic structures with natural language processing techniques and with additional linguistic resources for semantics. The implemented system shows the performance of 85.7% precision and 61.5% recall, including 7.7% partial recall, or an F score of 76.6. We apply the system to the biological interactions as extracted by our biomedical information-extraction system in order to enrich proteome databases with contrastive information.
6080724
BioContrasts: extracting and exploiting protein-protein contrastive relations from biomedical literature
MOTIVATION
Contrasts are useful conceptual vehicles for learning processes and exploratory research of the unknown. For example, contrastive information between proteins can reveal what similarities, divergences and relations there are of the two proteins, leading to invaluable insights for better understanding about the proteins. Such contrastive information are found to be reported in the biomedical literature. However, there have been no reported attempts in current biomedical text mining work that systematically extract and present such useful contrastive information from the literature for exploitation.


RESULTS
Our BioContrasts system extracts protein-protein contrastive information from MEDLINE abstracts and presents the information to biologists in a web-application for exploitation. Contrastive information are identified in the text abstracts with contrastive negation patterns such as 'A but not B'. A total of 799 169 pairs of contrastive expressions were successfully extracted from 2.5 million MEDLINE abstracts. Using grounding of contrastive protein names to Swiss-Prot entries, we were able to produce 41 471 pieces of contrasts between Swiss-Prot protein entries. These contrastive pieces of information are then presented via a user-friendly interactive web portal that can be exploited for applications such as the refinement of biological pathways.


AVAILABILITY
BioContrasts can be accessed at http://biocontrasts.i2r.a-star.edu.sg. It is also mirrored at http://biocontrasts.biopathway.org.


SUPPLEMENTARY INFORMATION
Supplementary materials are available at Bioinformatics online.
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.
15504838
Utilization-based heuristics for statically mapping real-time applications onto the HiPer-D heterogeneous computing system
Real-time applications continue to increase in importance as they are employed in various critical areas, such as command and control systems. These applications have traditionally required custom-made systems to execute them. Recently, with the widespread use of increasingly powerful commercial off-the-shelf (COTS) products, some real-time system designers have started a shift from custom development to COTS-based systems to achieve lower costs and more flexible systems. This research investigates the problem of allocating real-time applications to a set of COTS heterogeneous machines connected together by a COTS high-speed network. The heuristics were implemented on the High Performance Distributed Computing Program's (HiPer-D) Naval Surface Warfare Center (NSWC) testbed. At the specification of NSWC, the goal of this study is to design static resource allocation heuristics that balance the utilization of the computation and network resources in the HiPer-D system based on the system information provided. The broader goal is to maximize the time before dynamic reallocation is required for managing an increased workload at runtime.
13912277
The Use of Speech Retrieval Systems: A Study Design
What criteria for relevance do users apply when selecting a speech recording? What attributes of the recording do they rely on for each criterion? This paper proposes a qualitative research study design to explore those questions. A conceptual framework is presented, research questions are introduced, and the study design is described. The paper concludes with some observations on how the results of the study might inform the design of future systems.
16087620
A Corpus-Based Learning Method of Compound Noun Indexing Rules for Korean
In Korean information retrieval, compound nouns play an important role in improving precision in search experiments. There are two major approaches to compound noun indexing in Korean: statistical and linguistic. Each method, however, has its own shortcomings, such as limitations when indexing diverse types of compound nouns, over-generation of compound nouns, and data sparseness in training. In this paper, we propose a corpus-based learning method, which can index diverse types of compound nouns using rules automatically extracted from a large corpus. The automatic learning method is more portable and requires less human effort, although it exhibits a performance level similar to the manual-linguistic approach. We also present a new filtering method to solve the problems of compound noun over-generation and data sparseness.
8536996
Goal and scenario based domain requirements analysis environment
Identifying and representing domain requirements among products in a product family are crucial activities for a successful software reuse. The domain requirements should be not only identified based on the business goal, which drives marketing plan, product plan, and differences among products, but also represented as familiar notations in order to support developing a particular product in the product family. Thus, our proposal is to identify the domain requirements through goals and scenarios, and represent them as variable use cases for a product family. Especially, for identification of the domain requirements, we propose four abstraction levels of requirements in a product family, and goal and scenario modeling. For representation of them, variable use case model is suggested, and also the use case transfer rules are proposed so as to bridge the gap between the identification and representation activity. The paper illustrates the application of the approach within a supporting tool using the HIS (Home Integration System) example.