ライフサイエンスコーパス: dictionary

1 mbiguity and variability of the terms in the dictionary.

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

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

4 learning a string similarity measure from a dictionary.

5 for the construction of a biomedical symbol dictionary.

6 ictionary for Regulatory Activities (MedDRA) dictionary.

7 Linguistic Inquiry and Word Count (LIWC-22) dictionary.

8 f 99.64% for complete and 95.87% for reduced dictionary.

9 s meeting criteria for a minimum viable data dictionary.

10 y noisy images by comparing with a reference dictionary.

11 natural image processing based on a learned dictionary.

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

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

14 e parameters inferred with a high-resolution dictionary.

15 has good performance in recovering molecule dictionaries.

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

17 To address this gap, we created the Immune Dictionary, a compendium of single-cell transcriptomic p

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

19 To fill this gap, we developed a threat dictionary, a computationally derived linguistic tool th

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

21 onality of BioFt by allowing the creation of dictionaries according to different criteria.

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

23 performed much better than English-language dictionary analysis (r = 0.20 to 0.30) at detecting psyc

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

25 ished between 1941 and 2019, and searches of dictionaries and grey literature, as well as hand-search

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

27 ents that complement current enzyme database dictionaries and provide bridgeheads for the annotation

28 The dictionaries and the annotation datasets associated with

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

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

31 Using a predefined data dictionary and analysis plan, variables ranging from pat

32 The expression dictionary and barcoding matrix described here are immed

33 rminologies are searched from the biological dictionary and biology websites.

34 pproach on average by 3.48% for the complete dictionary and by 5.82% for the reduced one.

35 al cytokine stimulation data from the Immune Dictionary and cell-level scores are computed using a mo

36 n grouping coding, which reduces the size of dictionary and enables lossless compression without disr

37 noTagger, a hybrid method that combines both dictionary and machine learning-based methods to recogni

38 Finally, the dictionary and machine learning-based prediction results

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

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

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

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

43 eport, 4 (6%) had incomplete or missing data dictionaries, and 20 (29%) were missing anonymization or

44 These laws are assembled into class-specific dictionaries, and new series are projected onto them to

45 ng BILA, a dataset including 1,574 bilingual dictionaries, and showing that it confirms 147 out of 16

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

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

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

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

50 inimum and maximum values stated in the data dictionary; and more.

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

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

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

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

55 om various multimodal sources and constructs dictionaries at different learning levels, which enables

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

57 9% and recall = 70.5%) compared to a popular dictionary based approach (precision = 97.5% and recall

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

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

60 coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF alg

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

62 propose to go back to the basics and adopt a dictionary-based approach that enables both an immediate

63 We developed a dictionary-based approach using a pre-built large collec

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

65 We use dictionary-based information extraction to identify medi

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

67 us works that address the task typically use dictionary-based matching methods, which can achieve hig

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

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

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

71 examples are provided in the applications of dictionary-based signal recovery, CT imaging, and arbitr

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

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

74 fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural n

75 Using hidden Markov models, we established a dictionary between activity patterns and actions.

76 This approach yields a dictionary between logical states in the bulk and the cr

77 The large and consistent dictionaries built this way increase the accuracy of Kal

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

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

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

81 how that computational analyses of bilingual dictionaries can be used to test claims about lexical el

82 Detecting such comprehensive motor control dictionaries can improve our understanding of skilled mo

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

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

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

86 dels were examined, differing in the type of dictionary components (word length, step, context) as we

87 The next stage focused on exploring the dictionary components and attempting to optimize it, emp

88 s a tool, dbGaPCheckup ensures that the data dictionary contains all fields required by dbGaP, and ad

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

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

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

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

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

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

95 To associate the dictionary elements with biological properties of the co

96 long-range interaction patterns (long-range dictionary elements).

97 mportantly, it enables the interpretation of dictionary elements, which serve as cluster representati

98 y encoded using neuron activities and stored dictionary elements.

99 UniSpec learned with a peptide fragmentation dictionary encompassing 7919 fragment peaks.

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

101 In simulation at SNR 60 dB, the dictionary estimate had a mean percent error of 0.4-1.0%

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

103 This approach develops a more refined dictionary for DNA sequences, utilizes it for BERT pre-t

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

105 g resistance mutations, provides a reference dictionary for mutations that are sensitized to specific

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

107 and their normalization through the Medical Dictionary for Regulatory Activities (MedDRA) dictionary

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

109 g the retinal disorders Standardized Medical Dictionary for Regulatory Activities (MedDRA) Query, whi

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

111 In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug la

112 safety assessments using a standard Medical Dictionary for Regulatory Activities basket.

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

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

115 se outcomes, were directly mapped to Medical Dictionary for Regulatory Activities preferred terms in

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

117 ted, the event was categorized using Medical Dictionary for Regulatory Activities primary system orga

118 Standardized Medical Dictionary for Regulatory Activities queries and extende

119 adverse event reports, Standardized Medical Dictionary for Regulatory Activities queries for events

120 ences of cardiac events overall (the Medical Dictionary for Regulatory Activities system organ class)

121 Reports were classified according to Medical Dictionary for Regulatory Activities terminology.

122 verse events, which were coded using Medical Dictionary for Regulatory Activities terminology.

123 of TKI adverse effects using uniform Medical Dictionary for Regulatory Activities terms and comprehen

124 tion-related adverse events from the Medical Dictionary for Regulatory Activities toxic/septic shock

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

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

127 and infestations (defined using the Medical Dictionary for Regulatory Activities, version 21.0), mos

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

129 within VAERS using a combination of Medical Dictionary for Regulatory Activity queries and Preferred

130 events identified via a standardized Medical Dictionary for Regulatory Archives HF query.

131 t text was performed using the Valence Aware Dictionary for sEntiment Reasoning (VADER), a validated

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

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

134 Reducing the dictionary from 1024 to 145 changed BACC to 96.49% and t

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

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

137 ovel methodology for the extraction of k-mer dictionaries, from multiple sets of biological sequences

138 An additional key development is the use of dictionary functions derived from noise-corrupted invers

139 Dictionaries generated using the training set had high c

140 Our dictionary generates new hypotheses for cytokine functio

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

142 Dictionary HMMs are a technique in which a dictionary is

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

144 Dictionary HMMs were implemented in Java.

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

146 A cell-type-centric view of the dictionary identified more than 66 cytokine-driven cellu

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

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

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

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

151 Through the use of dictionary information, one can also associate the conta

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

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

154 In general, each data element in the data dictionary is associated with a third party controlled v

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

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

157 The dictionary is used for the resolution of abbreviations o

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

159 By reconstructing dictionaries learned at different levels, we integrate b

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

161 Dictionary learning (DL), implemented via matrix factori

162 el DL algorithm called online convex network dictionary learning (online cvxNDL).

163 ependent component analysis (ICA) and sparse dictionary learning (sDL).

164 Using a dictionary learning algorithm the first step estimates t

165 enhancement framework using a detailed-based dictionary learning and camera response model (CRM).

166 Moreover, we demonstrate how dictionary learning can be combined with sketching techn

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

168 We formalize this hypothesis using a sparse dictionary learning method, which we use to extract moto

169 Traditional deep dictionary learning methods for texture recognition ofte

170 Unlike standard dictionary learning methods which assume Gaussian-distri

171 l method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK)

172 Dictionary learning will effectively address several enh

173 It combines dictionary learning with edge-aware filter-based detail

174 Recent advances in sparse dictionary learning, such as the sparse identification o

175 then refines the estimation via novel shared dictionary learning.

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

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

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

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

180 for unsupervised representation learning by dictionary lookup.

181 In vivo, dictionary matching and curve fitting showed no statisti

182 study establishes the feasibility of using a dictionary matching approach as a new and faster way of

183 Here, a dictionary matching approach is proposed as an alternati

184 Curve fitting and dictionary matching were applied to simulated data using

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

186 In vivo, the dictionary method performed over 140-fold faster than cu

187 faster than the nonlinear least squares and dictionary methods, respectively.

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

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

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

191 We curated dictionaries of terms, collected articles of interest, a

192 onal language was measured using a validated dictionary of 139 science-specific terms.

193 so, we extracted time series for a validated dictionary of 19 absolutist words, from which the ATI wa

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

195 arsimonious governing equations from a large dictionary of basis functions.

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

197 cell abundance by integrating a gene pattern dictionary of copy number alterations and expression cha

198 In this paper, we show that using a dictionary of diverse kernels with complex shapes learne

199 It builds a dynamic dictionary of encoded representation keys with a queue a

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

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

202 These results expand our conception of the dictionary of features encoded in the cortex, and the ap

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

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

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

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

207 An improved Dictionary of Homologous superfamilies (DHS) containing

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

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

210 The associated Dictionary of Homologous Superfamilies, which provides m

211 LST method to learn directly from the data a dictionary of local, or small-scale, geophysical feature

212 s the objective of creating an interpretable dictionary of long-range interaction patterns that accur

213 as specified in the National Health Service dictionary of medicines and devices.

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

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

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

217 We compiled a dictionary of pathogen genetic variation, including sero

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

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

220 S) no longer serves the community, while the Dictionary of Protein Secondary Structure (DSSP) annotat

221 navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences c

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

223 The results also indicate that the dictionary of secondary structure of proteins (DSSP) alg

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

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

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

227 ng and comparing 2 NER models using a custom dictionary of terms, including lesion type, location, si

228 )F-paraGEST data set, we generated a de novo dictionary of ~2500 combinations of Ln(3+) mixtures, res

229 Moreover, the dictionary offers predictive insights on US society's sh

230 regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave qu

231 high-order correlations between them into a dictionary optimized for sparse reconstruction.

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

233 ults from our study indicate that the visual dictionary, or visual image pattern, obtained from unsup

234 defined by their relations to other words in dictionaries, our understanding of word meaning presumab

235 ng for large sequence files, JSON and Python dictionary output, and built-in sequence filtering.

236 This dictionary overcomes important limitations of previous e

237 o select potential phases for Hough-based or dictionary pattern matching and is not well suited for p

238 rd of the year in 2016 by the Oxford English Dictionary, "post-truth" refers to "relating to or denot

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

240 g of the subject phenotype data set and data dictionary prior to dbGaP submission.

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

242 To address this issue, we propose a dictionary-reconstruction-based deep learning approach b

243 A cytokine-centric view of the dictionary revealed that most cytokines induce highly ce

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

245 Traditional NER methods rely on dictionaries, rules, or curated corpora, which may not a

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

247 tooling and fully leveraging available data dictionary schemas.

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

249 In this study, Valence Aware Dictionary sEntiment Reasoner (VADER) and word frequency

250 Valence Aware Dictionary sEntiment Reasoner produced Negative, Neutral

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

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

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

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

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

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

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

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

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

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

261 ly characterised in the over-complete detail dictionary that was learned from many training detail pa

262 n Avro and encapsulates a data model, a data dictionary, the data itself, and pointers to third party

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

264 ariables match between the data set and data dictionary; there are no duplicated variable names or de

265 ample learning, to train these reconstructed dictionaries, thereby improving feature learning and tra

266 ipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic dat

267 unction to reorder the variables in the data dictionary to match the order listed in the data set).

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

269 Using three language analysis techniques (dictionary, topic, and word embeddings), we found that t

270 is validated using data from both the Immune Dictionary via stratified cross-validation and external

271 The dictionary was constructed by aligning representative st

272 This dictionary was later used for computational pattern reco

273 The curated dictionaries we leverage to detect these trends were for

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

275 Based on this dictionary, we developed companion software, Immune Resp

276 ltiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datas

277 , NLP relied on hard-coded grammar rules and dictionaries, which were labor-intensive and lacked flex

278 Scupa leverages data from the Immune Dictionary, which characterizes cytokine-driven polariza

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

280 concepts and synonyms in HPO to construct a dictionary, which is then used to automatically build a

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

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

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

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

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