ライフサイエンスコーパス: grammar

1 airing correlations (stochastic context-free grammars).

2 rror) but not the context-sensitive (repeat) grammar.

3 tic data (PLD), onto linguistic knowledge, a grammar.

4 e fraction of TFBS outside of the regulatory grammar.

5 ations revealed some aspects of a regulatory grammar.

6 cluded measures of phonology, semantics, and grammar.

7 by a recursive, self-embedding, context-free grammar.

8 or a text-based Phyletic Pattern Expression grammar.

9 e of a broad coverage syntax-semantic hybrid grammar.

10 hile the learner has to infer the underlying grammar.

11 yntax and semantic information from a single grammar.

12 s a mathematical description of language and grammar.

13 ge and the biological evolution of universal grammar.

14 ate the sample sentences is called universal grammar.

15 tween a context-free and a context-sensitive grammar.

16 he conditions for the evolution of universal grammar.

17 rings generated by an otherwise context-free grammar.

18 that algorithm and a formal transformational grammar.

19 upporting the existence of genes specific to grammar.

20 sts equate knowing a language with knowing a grammar.

21 computation, optimization, for the theory of grammar.

22 eir peers with normal hearing with regard to grammar.

23 asurements can be used to dissect regulatory grammar.

24 e equal-tempered scales using a finite-state grammar.

25 op as a word in a formal grammar, the R-loop grammar.

26 Here, we review the literature on enhancer grammar.

27 erface to the flexible Gosling visualization grammar.

28 ciation are dictated by linguistic rules, or grammar.

29 It is based on a Parsing Expression Grammar.

30 cholinguistic data to processing rather than grammar.

31 is enriched dependency structure, as in Word Grammar.

32 then priming offers no special insight into grammar.

33 learning to process, rather than inducing, a grammar.

34 ammars, learners acquire a single systematic grammar.

35 ow the expected productivity of a rule-based grammar.

36 e that monkeys can spontaneously master such grammars.

37 ignments and profile stochastic context-free grammars.

38 uisition which can learn a restricted set of grammars.

39 le CFG synchronized with a number of regular grammars.

40 quisition can only learn a restricted set of grammars.

41 ne grammar out of a limited set of candidate grammars.

42 and competition between different universal grammars.

43 e ResNets fail to learn simulated regulatory grammars.

44 re profiles based on stochastic context-free grammars.

45 e-molecule chromatin states for unseen motif grammars.

46 at uncovers IDR-specific and IDRome-spanning grammars.

47 (IDRs) of proteins are defined by molecular grammars.

48 with both spatially constrained and flexible grammars.

49 umans tested with the same task learned both grammars.

50 go beyond the level of complexity of regular grammars.

51 both structurally ambiguous and unambiguous grammars.

52 encoding topological information, the R-loop grammar accurately predicts R-loop formation on plasmids

53 during the initial to intermediate stages of grammar acquisition in a new target language.

54 ndation model designed to uncover regulatory grammars across 213 human fetal and adult cell types(1,2

55 Artificial grammars (AG) are designed to emulate aspects of the str

56 predicted by one single joint probabilistic grammar ('all-at-once').

57 which models SVs using a simple and flexible grammar, allowing users to easily define standard and cu

58 ers novel insights into the relation between grammar and cognition.

59 ection of grammars within the same universal grammar and competition between different universal gram

60 our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for

61 utilizes a specially developed context-free grammar and lexicon.

62 e whether they are correct in the domains of grammar and logical reasoning.

63 er in nature, with virtually every aspect of grammar and of language affected.

64 language acquisition assume a single target grammar and one PLD source, the central question being w

65 , and no significant difference was found in grammar and punctuation (risk difference [RD], -0.006 [9

66 defined by overall z score across 5 domains (grammar and punctuation, reading, writing, spelling, and

67 able to classify new patterns defined by the grammar and reliably exclude agrammatical patterns.

68 e problem of discovering and deciphering the grammar and syntax of gene regulation in eukaryotes.

69 enerally act in an additive manner with weak grammar and that most enhancers increase expression from

70 a logic grammar formalism called Basic Gene Grammars and a bidirectional chart parser DNA-ChartParse

71 s grammar class encompasses the context-free grammars and goes beyond to generate pseudoknotted struc

72 nces based on hybrid Stochastic Context-Free Grammars and Markov Random Fields (SCFG/MRF).

73 omputational framework based on context-free grammars and mutual information that systematically expl

74 ations with variation, i.e., multiple target grammars and PLDs.

75 ut more generally to stochastic context free grammars and RNA structure prediction.

76 Basic Gene Grammars and the DNA-ChartParser allowed different sourc

77 ents 2 and 3 used more flexible Markov-chain grammars and were designed to generalize the effect to 1

78 ndings argue for both a similar operational 'grammar' and shared protein domains in the sensing and l

79 Words, grammar, and phonology are linguistically distinct, yet

80 Longitudinal vocabulary, grammar, and receptive language scores.

81 as a valuable tool to those developing phylo-grammars, and as a means for the exploration and dissemi

82 mnesic patients for dot patterns, artificial grammars, and cartoon animals.

83 omain have used full parses, domain-specific grammars, and large knowledge bases encoding domain know

84 tive performance was assessed using implicit grammar- and motor-learning tasks and a detailed neurops

85 l data are directly relevant for determining grammar architecture, we present one main objection to t

86 We demonstrated that simulated regulatory grammars are best learned in the penultimate layer of th

87 ov chain substitution models with stochastic grammars, are powerful models for annotating structured

88 How universal grammar arose is a major challenge for evolutionary biol

89 We implement this grammar as a Bioconductor/R package called plyranges.

90 s to explore the suitability of Construction Grammar as an appropriate framework for a schema-based l

91 ic accessibility, and we discover regulatory grammars associated with ubiquitous, germline, and somat

92 and sequence lengths were unable to master a grammar at this higher, "phrase structure grammar" level

93 The pseudoknot grammar avoids the use of general context-sensitive rule

94 ient progressive alignment algorithm using a grammar based sequence distance particularly useful in a

95 l to evaluate the explanatory power of child grammars based not on abstract rules but on concrete wor

96 (to facilitate the development of stochastic grammars based on phylogenetic trees, also known as Stat

97 Goslin is the first grammar-based computational library for the recognition/

98 posed progressive alignment algorithm uses a grammar-based distance metric to determine the order in

99 hy of neural processing timescales underlies grammar-based internal construction of hierarchical ling

100 In addition to grammar-based modeling, insilicoSV provides built-in sup

101 f cross-validation experiments, we show that grammar-based secondary structure prediction methods for

102 ), each learner receives PLD from one target grammar but different learners can have different target

103 is field are expressed using Definite Clause Grammars but these have computational limitations which

104 ision of its signal or the complexity of its grammar, but also from its links to cognition.

105 the probability parameters of the stochastic grammar, but also the instantaneous mutation rates of th

106 of the fundamental principles and molecular grammar by which biological molecules may phase separate

107 he central question being whether the target grammar can be acquired from the PLD.

108 Universal grammar cannot be learned; it must be in place when the

109 tilizing a lexical analyzer and context free grammar (CFG), we demonstrate that efficient parsers can

110 known ncRNA search is based on context-free grammar (CFG), which cannot effectively model pseudoknot

111 We show that context-free grammars (CFG) can formalize these design principles.

112 No major content or grammar changes were identified during the cognitive int

113 if, is replaced with a zygotic TSS selection grammar characterized by broader patterns of dinucleotid

114 This grammar class encompasses the context-free grammars and

115 The two TSS-defining grammars coexist, often physically overlapping, in core

116 as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology,

117 l differences in perceptual rhythm skill and grammar competency in children.

118 f the system was measured by calculating the grammar complexity of the observed sequences, which show

119 than the initial level during subsequent new grammar conditions.

120 finger tapping tasks-also explain the rhythm-grammar connection in 150 healthy young adults.

121 contentious, example is whether a universal grammar constrains syntactic diversity in human language

122 ates that modulating 5' UTR length and motif grammar contributes to translation initiation dynamics.

123 tween damage to the frontal aslant tract and grammar deficits suggests that verbal fluency and gramma

124 IDRs with exceptional grammars, defined as sequences with high-scoring non-ran

125 sentence repetition task was used to assess grammar-dependent verbal working memory, and an auditory

126 ciency of ResNets in learning the regulatory grammar depends on the nature of the prediction task.

127 ociated perturbations in ARID1B IDR sequence grammars disrupt cBAF function in cells.

128 on Grammar (TCG), a new form of construction grammar distinguished by its use of SemRep to express se

129 f transcription factors, i.e. the regulatory grammar, drives enhancer activity have been proposed, ra

130 Both species evolved their LoPS grammars during learning, progressing from simpler to mo

131 The context-free grammar editor is part of the GenoCAD application.

132 elationships between certain binding sites ("grammar elements") can be identified in all sparkling or

133 identifies key compensatory mechanisms and 'grammar' elements that are critical for maintaining func

134 ethod can accurately retrieve the regulatory grammar even when there is heterogeneity in the enhancer

135 Hence, an extended molecular grammar expands the role of arginine and polar residues

136 servation of the epigenomic and the sequence grammar features.

137 roof, the library was converted into GenoCAD grammar files to allow users to import and customize the

138 nces under different stochastic context-free grammar folding models.

139 SummarizedBenchmark defines a general grammar for benchmarking and allows for easier setup and

140 We introduce Goslin, a polyglot grammar for common lipid shorthand nomenclatures based o

141 re the importance of an orientation-specific grammar for CTCF binding sites.

142 ntal query and reference data sets, select a grammar for lipid name normalization, and then process t

143 NEMO is recognized by a grammar for transcription network motifs using lex and y

144 rly understood and cannot inform accounts of grammar for two reasons.

145 way to estimate the parameters of stochastic grammars for biological sequence analysis.

146 a new DNA parsing system, comprising a logic grammar formalism called Basic Gene Grammars and a bidir

147 en & Chater (C&C) perspective needs a formal grammar framework capturing word-by-word incrementality,

148 ntitative tests justified the distinction of grammar from speech abnormalities and the desirability o

149 a data-driven approach for extracting action grammars from basic ethograms, exemplified with respect

150 mat) and automated parameterization of those grammars from training data (via the Expectation Maximiz

151 ayesian procedure to extract such item-based grammars from transcriptions of 28+ h of each of two chi

152 fferent levels of complexity: a context-free grammar generating sequences following a mirror structur

153 AB | BA, ABC | CBA) and a context-sensitive grammar generating sequences following a repeat structur

154 A molecular grammar governing low-complexity prion-like domain phase

155 A context-free grammar has been developed to model the structure of con

156 ral cognitive capacities, certain aspects of grammar have an autonomous psychological and neural basi

157 While syntax grammars have generally been tested over more documents,

158 ly been tested over more documents, semantic grammars have outperformed them in precision and recall.

159 Despite this profound impairment in grammar, he displayed simple causal reasoning and ToM un

160 language model to understand the underlying grammar, i.e. the arrangement and frequencies of amino a

161 F sites make two predictions for multi-motif grammar: (i) insulation strength depends on the number o

162 erty of the stimulus" arguments suggest that grammar identification is an intractable inductive probl

163 ial variability regarding this aspect of the grammar, (ii) this variability is attested between speak

164 lant tract in relation to verbal fluency and grammar impairment in primary progressive aphasia.

165 oun" and "verb" represent the basic units of grammar in all human languages, and the retrieval of cat

166 e of mathematical calculations from language grammar in the mature cognitive system.

167 ired by incremental parsing for context-free grammars in computational linguistics, our alternative d

168 Most formal grammars in this field are expressed using Definite Clau

169 t sequence learning paradigm (an "artificial grammar") in which humans and monkeys were exposed to se

170 model, called CoNSEPT, that learns enhancer 'grammar' in an unbiased manner.

171 s flanking the WRCH motifs (i.e., a sequence grammar) in determining mutational outcomes.

172 r, human language entails more sophisticated grammars, incorporating hierarchical structure.

173 Here, we introduce grammars inferred using NARDINI+ (GIN) as a resource tha

174 Understanding the nature of protein grammar is critical because amino acid substitutions in

175 ry independently suggests that the choice of grammar is driven in part by a process operating interna

176 The conservation of grammar is examined in seven divergent Drosophila genome

177 who are linguistically homogenous and whose grammar is known to the researcher.

178 Their claim that grammar is merely acquired language processing skill can

179 However, the nature of this sequence grammar is poorly understood.

180 Formally a context-sensitive grammar is required, which would impose a large increase

181 imum error tolerance for which a predominant grammar is stable.

182 Grammar is the basis of the unlimited expressibility of

183 Grammar is the computational system of language.

184 A graph grammar is used to both contextualize physical propertie

185 The use of Basic Gene Grammars is demonstrated in representing many formulatio

186 fy sequences from recursive, centre-embedded grammars is not uniquely human.

187 tside algorithm (for stochastic context-free grammars), is a powerful way to estimate the parameters

188 xposure language is compatible with multiple grammars, learners acquire a single systematic grammar.

189 Artificial grammar learning (AGL) is one of the most extensively em

190 antage for this group was noted for implicit grammar learning (ANOVA, p = 0.021).

191 imuli is the principal feature of artificial grammar learning and prototype learning, then these form

192 d on artificial grammar learning, artificial grammar learning with transfer to novel lettersets, and

193 g, perceptuomotor skill learning, artificial grammar learning, and prototype abstraction; (ii) cortic

194 3 patients with PD were tested on artificial grammar learning, artificial grammar learning with trans

195 l framework for the evolutionary dynamics of grammar learning.

196 the population dynamics of communication and grammar learning.

197 a grammar at this higher, "phrase structure grammar" level.

198 ental cerebellar functions of prediction and grammar-like rule extraction from sequences, that underp

199 they share remarkably similar cis-regulatory grammars, marked by enrichment of K50 homeodomain bindin

200 This cis-regulatory "grammar" may aid the identification of enhancers regulat

201 analytical techniques, including a novel MP grammars method to mathematically model putative regulat

202 ar that contrasts with mainstream generative grammar (MGG) in that (a) it treats phonology, syntax, a

203 Technically, the stochastic version of the grammar model can be as simple as an SCFG.

204 We introduce a new grammar modeling approach for RNA pseudoknotted structur

205 Priming implies a network structure, so the grammar must be a network and so must sentence structure

206 ave been made in understanding the molecular grammar of a few scaffold proteins that make up these ph

207 to have correctly deciphered the underlying grammar of antimicrobial peptide sequences, as demonstra

208 networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of ke

209 del, to extract the sequence preferences and grammar of CTCF contributing to genome folding.

210 the ability of DNNs to learn the regulatory grammar of enhancers.

211 We conclude that grammar of gene control regions is pervasively used in t

212 follows "tidy" data principles, we create a grammar of genomic data transformation, defining verbs f

213 e and companion web application built on the grammar of graphics system.

214 Advances include the development of a grammar of graphics, deeper understanding of human perce

215 genomic locations characterized by a unique grammar of homeodomain binding motifs.

216 with positional notation that resembles the grammar of natural languages.

217 ar grammar rather than the context sensitive grammar of the original TOPS model.

218 Children acquire the grammar of their native language without formal educatio

219 ask how accurate children have to learn the grammar of their parents' language for a population of i

220 ealing key differences in the cis-regulatory grammar of these cell types.

221 ns of linguistic theory: What is it that the grammars of all languages share, and how may they differ

222 baboons' capacity to learn two supra-regular grammars of different levels of complexity: a context-fr

223 GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.

224 Here, we investigate the regulatory 'grammar' of nearly 100 characterized enhancers for devel

225 observed between slow speech with simplified grammar on the one hand, and grammatical and speech soun

226 ally consistent with two possible underlying grammars--one more similar to English in terms of the li

227 We argue that grammar originated as a simplified rule system that evol

228 evaluate the sample sentences and choose one grammar out of a limited set of candidate grammars.

229 The language-based method employed a link grammar parser combined with semantic patterns derived f

230 usical rhythm was correlated with expressive grammar performance (r = 0.41, p < 0.001).

231 criptome and that a confluent RFX regulatory grammar plays a significant role in the genetic componen

232 This grammar plays only a small role for genomic sequences, a

233 c regulatory activity, reveal cis-regulatory grammar, prioritize genetic variants and design syntheti

234 Phylo-grammars, probabilistic models combining Markov chain su

235 letter transformation, and novel artificial grammar problems, which were expected or unexpected.

236 ar deficits suggests that verbal fluency and grammar processing rely on distinct anatomical networks.

237 First, those who view performance as grammar + processing will always be able to attribute ps

238 y and a sentence-ordering task that assesses grammar production.

239 heir selective aspects, including phonology, grammar, prosody and semantics.

240 n's language is consistent with a productive grammar rather than memorization of specific word combin

241 , to give a model characterized by a regular grammar rather than the context sensitive grammar of the

242 ar and logistic mixed models and regression (GRAMMAR), regional heritability mapping (RHM) and haplot

243 eir internal architecture and cis-regulatory grammar remain elusive.

244 obabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensi

245 Initially, NLP relied on hard-coded grammar rules and dictionaries, which were labor-intensi

246 To investigate more intensively the grammar rules for active regulatory sequence, we made li

247 s successfully using stochastic context-free grammars (SCFG) adapted from computational linguistics;

248 ch (RNAalifold) or a Stochastic Context-Free Grammars (SCFGs) approach (PPfold).

249 More recently, stochastic context-free grammars (SCFGs) have emerged as an alternative probabil

250 ith SSD without a cochlear implant had worse grammar scores than the group with implants (-0.76; 95%

251 al associations between different rhythm and grammar skills.

252 Universal grammar specifies the mechanism of language acquisition.

253 Universal grammar specifies the restricted set of languages learna

254 ich consists of a dictionary of motifs and a grammar specifying their usage.

255 d structures based on parallel communicating grammar systems (PCGS).

256 ht, the Iowa gambling task and an artificial grammar task.

257 ion of sentences using Template Construction Grammar (TCG), a new form of construction grammar distin

258 rchitecture, an approach to the structure of grammar that contrasts with mainstream generative gramma

259 allenge by introducing a human-interpretable grammar that encodes multicellular systems biology model

260 terances minimally requires a 'context-free' grammar that is more complex than the 'finite-state' gra

261 Here, we propose a conceptualized grammar that makes it easier to create visual 3D represe

262 ere we present CaCoFold-R3D, a probabilistic grammar that predicts these RNA 3D motifs (also termed m

263 We identify one aspect of grammar that varies unpredictably across a population of

264 vors the emergence of rule-based, generative grammars that underlie complex language.

265 que, n-gram analysis, to probe the "proteome grammar"-that is, the rules of association of domains th

266 gh the use of syntactic rules (or generative grammars) that describe the acceptable structure of utte

267 in the framework of stochastic context-free grammars, that emulates the kinetics of RNA folding in a

268 involves the ability to master supra-regular grammars, that go beyond the level of complexity of regu

269 we represent an R-loop as a word in a formal grammar, the R-loop grammar.

270 t Inventories (CDI) to assess vocabulary and grammar; the A-not-B test to assess object permanence; a

271 that is more complex than the 'finite-state' grammars thought sufficient to specify the structure of

272 The model uses a probabilistic grammar to characterize modality-independent representat

273 hich specifies the condition for a universal grammar to induce coherent communication within a popula

274 We used this set of grammars to create new, unnatural AmP sequences.

275 ad, transFold employs multi-tape S-attribute grammars to describe all potential conformations, and th

276 a formal language and built a set of regular grammars to describe this language.

277 molecules must possess appropriate sequence grammars to drive phase separation.

278 We then use these grammars to parse all of the unique multiword utterances

279 n the motifs in order to learn a regulatory 'grammar' to improve predictions.

280 Evolution of grammars toward efficiency results in word-order pattern

281 e theory of this restricted set is universal grammar (UG).

282 al language and show that a "quasi-universal grammar" underlies the evolution of domain architectures

283 a context toward understanding the molecular grammar underlying lncRNA biology.

284 eler, cBAF, and establish distinct "sequence grammars" underlying each contribution.

285 developed a graphical editor of context-free grammars usable by biologists without prior exposure to

286 ic stimuli were composed based on artificial grammars using scales with different levels of symmetry.

287 des means for the rapid development of phylo-grammars (using a simple file format) and automated para

288 e of generating an infinite set of outputs ("grammars") vary in generative power.

289 pt And XML) web interface to xrate providing grammar visualization tools as well as access to xrate's

290 However, they were dissociable, as the grammar was similarly learned whether a repeating sequen

291 d of autonomous representations specified by grammar, we propose that contextual representations emer

292 coordinately regulated genes share a common "grammar," we have examined the distribution of Dorsal re

293 y symbols and characterized by a statistical grammar which varies with external situational context a

294 t this with the framework of Universal Moral Grammar, which has sought a descriptively adequate accou

295 in the domains of phonology, semantics, and grammar, which have been closely linked with neuroanatom

296 Here we further introduce the R3D grammars, which also exploit helix covariation that cons

297 els of complexity and ambiguity and simulate grammars with optimized word-order parameters on large-s

298 r graphics platform that combines generative grammars with visual perception, we accessed the mind's

299 rget locations followed one of the above two grammars, with rare violations.

300 We study selection of grammars within the same universal grammar and competiti