ライフサイエンスコーパス: dictionary

1 for the construction of a biomedical symbol dictionary.

2 natural image processing based on a learned dictionary.

3 ise patch-based approach and an MRI-CT atlas dictionary.

4 eby adding these words to the brain's visual dictionary.

5 y noisy images by comparing with a reference dictionary.

6 mbiguity and variability of the terms in the dictionary.

7 esign and the inclusion of data from the HLA Dictionary.

8 based on a general word dictionary and an NE dictionary.

9 learning a string similarity measure from a dictionary.

10 has good performance in recovering molecule dictionaries.

11 that is centered around and exploits the Bio-Dictionary, a collection of amino acid patterns that com

12 e annotation method that is based on the Bio-Dictionary, a comprehensive collection of amino acid pat

13 We evaluated our algorithm using two large dictionaries: a human gene/protein name dictionary built

14 Of the 135 n-grams in the dictionary, an earlier written instance was identified i

15 ed as well as the XML representations of PDB dictionaries and data files.

16 However, sparse availability of data dictionaries and lack of adherence to standard data elem

17 Additional documentation, such as the dictionaries and the reference sets, are available at th

18 Speech (POS) tagging based on a general word dictionary and an NE dictionary.

19 cently released versions of the PDB Exchange dictionary and the PDB archival data files in XML format

20 The correspondences between the PDB dictionary and the XML schema metadata are described as

21 ervices (EVS) provide controlled vocabulary, dictionary and thesaurus services.

22 DLINE document set, result in a high quality dictionary and toolset to disambiguate abbreviation symb

23 atted files, parsing them into usable Python dictionary- and list-based data structures, making acces

24 novel system that combines a pre-processing dictionary- and rule-based filtering step with several s

25 NLProt is a novel system that combines dictionary- and rule-based filtering with several suppor

26 To validate this hypothesis, we built a dictionary- and rule-based system to mine Medline for re

27 Our work proves the applicability of the dictionary approach to understanding the structure of a

28 We have therefore taken a "domain dictionary" approach to characterize representatives for

29 ocumentation, software tools and the DDL and dictionaries are available from http://ndbserver.rutgers

30 putational cost discourages its use when the dictionaries are large or when real time processing is r

31 a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases.

32 assified as "fairly accessible" and had data dictionaries available.

33 9% and recall = 70.5%) compared to a popular dictionary based approach (precision = 97.5% and recall

34 MetaMap and the dictionary based approach are available through the What

35 We propose and develop a novel dictionary based motif finding algorithm, which we call

36 y Definition Language (DDL) and an extensive dictionary based on this DDL for describing macromolecul

37 The Bio-Dictionary-based Gene Finder was used to reassess the co

38 We use dictionary-based information extraction to identify medi

39 The library provides an intuitive dictionary-based interface with which Python programs ca

40 An over-complete dictionary-based model was learned for the image-specifi

41 xing performance by exploiting: (i) a set of dictionary-based models for object morphologies learned

42 erm ambiguity and variability are very high, dictionary-based Named Entity Recognition (NER) is not a

43 Three dictionary-based systems (MetaMap, NCBO Annotator, and C

44 Therefore, we developed ENVIRONMENTS, a fast dictionary-based tagger capable of identifying Environme

45 arge dictionaries: a human gene/protein name dictionary built from BioThesaurus and a disease name di

46 y built from BioThesaurus and a disease name dictionary built from UMLS.

47 position technique based on an over-complete dictionary called matching pursuit (MP), and show that i

48 rial was performed using the data sets, data dictionary, case report file, and statistical analysis p

49 The Chemical Component Dictionary (CCD) is a chemical reference data resource t

50 ATH and Dali) to generate a consensus domain dictionary (CDD).

51 OREMPdb is the semantic dictionary created as a result, which is made of 7360 sp

52 op" records represented as individual Python dictionary data structures).

53 This realization led to a new Dictionary Definition Language (DDL) and an extensive di

54 folk psychology, it is useful to re-examine dictionary definitions of 'habit'.

55 ZFIN provides an anatomical atlas and dictionary, developmental staging criteria, research met

56 del generalizes by learning a compact set of dictionary elements for image distributions typically en

57 y encoded using neuron activities and stored dictionary elements.

58 he folds in the testing set, suggesting that dictionary entries reflect general features of protein s

59 r bioinformatics studies mining these domain dictionaries for globular protein properties.

60 os.cnb.uam.es/EUCLID, with the corresponding dictionaries for the generation of three, eight and 14 f

61 alian Phenotype Ontology, and the Anatomical Dictionary for Mouse Development and the Adult Anatomy.

62 recorded and coded according to the Medical Dictionary for Regulatory Activities (MedDRA) and risk f

63 The Medical Dictionary for Regulatory Activities (MedDRA) is a compr

64 onstructed based on the terms in the Medical Dictionary for Regulatory Activities (MedDRA) that appea

65 were categorized with the use of the Medical Dictionary for Regulatory Activities classification.

66 The use of the Medical Dictionary for Regulatory Activities nomenclature for AE

67 ts' Collaboration were identified by Medical Dictionary for Regulatory Activities preferred terms.

68 Standardized Medical Dictionary for Regulatory Activities queries and extende

69 With use of specified terms from the Medical Dictionary for Regulatory Activities we identified pneum

70 r fracture sites, defined by MedDRA (Medical Dictionary for Regulatory Activities).

71 We classified AEs using the Medical Dictionary for Regulatory Activities.

72 hips have high accuracy and provide a simple dictionary for the quantitative conversion of experiment

73 rative query operations over a large indexed dictionary, for instance, from large genome collections

74 amework for building an English-Chinese term dictionary from discharge summaries in the two languages

75 nterest to chemists directly as it defines a dictionary from electronic structure to spin Hamiltonian

76 Dictionaries generated using the training set had high c

77 s of biological sequences, in terms of k-mer dictionaries, has a well established role in genomic and

78 Dictionary HMMs are a technique in which a dictionary is

79 ding to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities t

80 Dictionary HMMs were implemented in Java.

81 two new methods for this task: semiCRFs and dictionary HMMs.

82 it existing and additional Named Entity (NE) dictionaries in statistical NER.

83 uristic rules, which enables us to look up a dictionary in a constant time regardless of its size.

84 overing a list of normalization rules from a dictionary in a fully automated manner.

85 ds (CRFs) that enables more effective use of dictionary information as features.

86 ion of these XML files is driven by the data dictionary infrastructure in use at the PDB.

87 The Dali Domain Dictionary is a numerical taxonomy of all known structur

88 Availability and implementation: This domain dictionary is available at www.dynameomics.org.

89 We show that the dictionary is compact and descriptive, capable of descri

90 Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes p

91 The dictionary is used for the resolution of abbreviations o

92 Presumably, addition of NEs to an NE dictionary leads to better performance.

93 We call this method MOLecular Dictionary Learning ( MOLDL: ).

94 Here, we present a dictionary learning method that deconvolves spectra of d

95 Unlike standard dictionary learning methods which assume Gaussian-distri

96 In addition, we found that dictionary look-up already provides competitive results

97 A dictionary look-up system using the similarity measures

98 outperforms existing similarity measures in dictionary look-up tasks.

99 ed it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary

100 Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator.

101 As such their "dictionary" may not follow Zipf's law (a power law) whic

102 On one hand, the dictionary model specifies a probability for the entire

103 er uses both regular expression patterns and dictionaries of gene symbols and names compiled from mul

104 ors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of

105 abstracts and linguistic methods to build a dictionary of abbreviation/sense pairs.

106 ed on the Gellus corpus and supported with a dictionary of cell line names.

107 The Oxford Dictionary of English defines habit as "a settled or reg

108 anguage for gene regulation and to compile a dictionary of enhancers which form its words.

109 Our approach is based on a dictionary of haplotypes that is used to efficiently dis

110 sparse representation of the DNA pools in a dictionary of haplotypes.

111 We also report an update of the CATH Dictionary of homologous structures (CATH-DHS) which now

112 ies has been made available through the CATH Dictionary of Homologous Structures (DHS).

113 An improved Dictionary of Homologous superfamilies (DHS) containing

114 The CATH Dictionary of Homologous Superfamilies (DHS), which cont

115 ion resources (PDBsum, CATH database and the Dictionary of Homologous Superfamilies).

116 The associated Dictionary of Homologous Superfamilies, which provides m

117 gical Interest (ChEBI) is a freely available dictionary of molecular entities focused on 'small' chem

118 gical Interest (ChEBI) is a freely available dictionary of molecular entities focused on 'small' chem

119 and a statistical model which consists of a dictionary of motifs and a grammar specifying their usag

120 set of DNA sequences into the most probable dictionary of motifs or words.

121 a useful adjunct to the widely used PROSITE dictionary of patterns.

122 ars will see the compilation of a definitive dictionary of protein families and their related functio

123 t this representation can be used to build a dictionary of repetitive behavioral motifs in an unbiase

124 -supporting reads and reads matching a large dictionary of sequence motifs.

125 nostic resource that complements the PROSITE dictionary of sites and patterns.

126 omated methods to systematically construct a dictionary of supersecondary structures that can be used

127 e been influenced by the compensatory use of dictionaries or thesauri, let alone by later editorial i

128 This dictionary overcomes important limitations of previous e

129 base and its new supplement, the Dali Domain Dictionary, present a continuously updated classificatio

130 ropose a paradigmatic formalization of k-mer dictionaries, providing two different and complementary

131 Existing approaches are based on dictionaries, rules and machine-learning.

132 The Simple and Robust Abbreviation Dictionary (SaRAD) provides an easy to implement, high p

133 cent versions of three common protein domain dictionaries (SCOP, CATH and Dali) to generate a consens

134 on/sense pairs, previously extracted for the dictionary set-up.

135 Different dictionary sets can be trained and stored in the same sy

136 using several large-scale gene/protein name dictionaries showed that the logistic regression-based s

137 We describe the first implementation of dictionary-style models to the study of transcription fa

138 approaches utilize knowledge sources such as dictionaries, taxonomies, and semantic networks, and inc

139 Details of the DDL, the dictionaries that have been developed, and software for

140 tration Database (TargetDB), organizing data dictionaries that will define the specification for the

141 set of standard relational tables and a data dictionary that form an initial ontology for proteomic p

142 ere released publicly after we built the Bio-Dictionary that is used in our experiments.

143 ancements to ZFIN include: (i) an anatomical dictionary that provides a controlled vocabulary of anat

144 finding entities that actually appear in the dictionary-the measure of most interest in our intended

145 r, we propose a novel concept, the "K-string dictionary", to solve this high-dimensional problem.

146 The dictionary was constructed by aligning representative st

147 ual's behavior and the elements in the motif dictionary, we create a fingerprint that can be used to

148 in result is a quantitative statics-dynamics dictionary, which could allow the experimental explorati

149 We have built a dictionary with 1,200 words for the 6, 000 upstream regu

150 creased to 78.72 after enriching the tagging dictionary with test set protein names.

151 aring in the training data to the POS tagger dictionary without any model retraining.

152 e the NER performance by adding NEs to an NE dictionary without retraining.

153 utomatic methods such as FSSP or Dali Domain Dictionary, yield divergent classifications, for reasons