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.
2306091
Classifying Protein Fingerprints
Protein fingerprints are groups of conserved motifs which can be used as diagnostic signatures to identify and characterize collections of protein sequences. These fingerprints are stored in the prints database after time-consuming annotation by domain experts who must first of all determine the fingerprint type, i.e., whether a fingerprint depicts a protein family, superfamily or domain. To alleviate the annotation bottleneck, a system called PRECIS has been developed which automatically generates prints records, provisionally stored in a supplement called preprints. One limitation of PRECIS is that its classification heuristics, handcoded by proteomics experts, often misclassify fingerprint type; their error rate has been estimated at 40%. This paper reports on an attempt to build more accurate classifiers based on information drawn from the fingerprints themselves and from the SWISS-PROT database. Extensive experimentation using 10-fold cross-validation led to the selection of a model combiing the ReliefF feature selector with an SVM-RBF learner. The final models error rate was estimated at 14.1% on a blind test set, representing a 26% accuracy gain over PRECIS handcrafted rules.
6458402
METIS: multiple extraction techniques for informative sentences
SUMMARY
METIS is a web-based integrated annotation tool. From single query sequences, the PRECIS component allows users to generate structured protein family reports from sets of related Swiss-Prot entries. These reports may then be augmented with pertinent sentences extracted from online biomedical literature via support vector machine and rule-based sentence classification systems.


AVAILABILITY
http://umber.sbs.man.ac.uk/dbbrowser/metis/
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.
12050804
On the asymmetry of random regular graphs and random graphs
This paper studies the symmetry of random regular graphs and random graphs. Our main result shows that for all 3 ≤ d ≤ n - 4 the random d-regular graph on n vertices almost surely has no nontrivial automorphisms. This answers an open question of N. Wormald [13].
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.
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.
1514813
Temporal Query Processing Using Spatially-Partitioned Method
In this paper, we propose a general spatiallypartitioned temporal operation model, called 3D-TOM, and partition-based join algorithms for various types of temporal join in the proposed model. The temporal operators addressed in this paper include overlap, precede, follow, and equal. Some factors which a ect the performance of the proposed method are also discussed.
6225887
Hamiltonian decompositions of random bipartite regular graphs
We prove a complete hamiltonian decomposition theorem for random bipartite regular graphs, thereby verifying a conjecture of Robinson and Wormald. The main step is to prove contiguity (a kind of asymptotic equivalence) of two probabilistic models of 4-regular bipartite graphs; namely, the uniform model, and the model obtained by taking the union of two independent, uniformly chosen bipartite Hamilton cycles, conditioned on forming no multiple edges. The proof uses the small subgraph conditioning method to establish contiguity, while the differential equation method is used to analyse a critical quantity.