ライフサイエンスコーパス: inference

1 ry evidence, a phenomenon known as "circular inference".

2 can improve sample efficiency and quality of inference.

3 n, and mitochondrial DNA (mtDNA) copy number inference.

4 her statistical power than Fieldtrip cluster inference.

5 ng for confounding bias is crucial in causal inference.

6 mulation or relatively abstract theory-based inference.

7 e generalized case-control methods to causal inference.

8 istent across triads, thus supporting faster inference.

9 ensory inference and decisions based on such inference.

10 s and RCTs relating to design, analysis, and inference.

11 rmalizing remapping in terms of hidden state inference.

12 ile being presented here is not used for any inference.

13 The observational design limits causal inference.

14 ethodology and the classifier used for their inference.

15 between the two regions support model-based inference.

16 dden Markov model searching and phylogenetic inference.

17 es, thereby making it well-suited to network inference.

18 ation primarily impacts accurate drug target inference.

19 sing multivariate genetic designs for causal inference.

20 lumn to perform sampling-based probabilistic inference.

21 re thought to negatively impact phylogenetic inference.

22 rence with predicted outcomes postprediction inference.

23 bootstrap to conduct differential expression inference.

24 he brain as performing hierarchical Bayesian inference.

25 ness values and adjusted p-values for target inference.

26 ated using Bayesian Markov Chain Monte Carlo inference.

27 rely confound discrete trait phylogeographic inference.

28 ovide two contributions for ancestral genome inference.

29 t of multivariate methods for lesion-deficit inferences.

30 ds of both false-positive and false-negative inferences.

31 e true prevalence of FFV and epidemiological inferences.

32 olutions, enabling users to make more robust inferences.

33 easure for identifying potentially erroneous inferences.

34 n procedures that permit valid and efficient inferences.

35 debate surrounding the reliability of these inferences(2-5) and, to date, this critical question rem

36 For statistical inference, a Bayesian hierarchical model is used to stud

37 characterized by alterations of the brain's inference about the causes of socially relevant sensory

38 AR dysfunction impairs hierarchical Bayesian inference about the world's statistical structure.

39 ach, which enables researchers to make valid inferences about biological entities of interest, even i

40 , hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that migh

41 sefulness of observational meta-analyses for inferences about etiology and treatment planning.

42 ce that these effects are mediated mostly by inferences about other's intentions made from strategy a

43 nderstanding of how people perceive and make inferences about others' beauty.

44 re research, as they allow for more accurate inferences about past human population dynamics when usi

45 Making inferences about the computations performed by neuronal

46 resolved in our reconstruction, we can make inferences about the connection between ectodomain and m

47 These distributions allow inferences about the general nature of interface residue

48 Within this framework, one can make inferences about the mind in a statistically principled

49 ing seasons, and assessed the reliability of inferences about the occurrence of rescue drawn from iso

50 and software (HeIST) for making statistical inferences about the probability of hemiplasy and homopl

51 vestigated whether infants can make rational inferences about when and how to try on a novel problem-

52 sentation predicted faster and more accurate inference (AC) decisions.

53 delivering the same high-level training and inference accuracies as those delivered by a software ne

54 ensemble averaging can successfully increase inference accuracy in physically implemented neural netw

55 physical implementations from achieving high inference accuracy.

56 roducing error which can significantly limit inference accuracy.

57 he similarity in two GRNs, and may sacrifice inference accuracy.

58 ications decisions; and (3) conducting joint inference across all specifications.

59 the maximum information content for ancestry inference, admixture mapping, forensic applications, and

60 Finally, we show that active inference agents learn models that are parsimonious, tai

61 ss the problem, we propose a Boolean network inference algorithm which is able to infer accurate Bool

62 by applying a dynamic Bayesian network (DBN) inference algorithm, genist, or a regression tree-based

63 nse data that have been processed by network inference algorithms, which further improves convergence

64 d the development of gene regulatory network inference algorithms.

65 usal network, which can be learned by causal inference algorithms.

66 ensities over 4 years further supported this inference, although Dolly Varden were a minority (29% of

67 Here we report a causal inference analysis investigating the effects of prediabe

68 ated with diabetes complications, the causal inference analysis revealed that prediabetes is only cau

69 sect function or use associations for causal inference analysis.

70 omputational techniques merging statistical, inference and artificial intelligence tools.

71 ince it is related to the generic perceptual inference and belief updating mechanisms, this approach

72 lly, by using single-cell regulatory network inference and clustering (SCENIC) algorithm, we were abl

73 sive processes, are also involved in sensory inference and decisions based on such inference.

74 rapidly developing field of simulation-based inference and identify the forces giving additional mome

75 al framework for reliable training, scalable inference and interpretable explanation of the DNA repai

76 al methods for ancestral recombination graph inference and machine-learning methods for the predictio

77 Gene network inference and master regulator analysis (MRA) have been

78 rophysiological processes reflecting sensory inference and memory in parietal-occipital regions, whil

79 In the light of new multivariate lesion inference and network approaches, we critically revisit

80 gene transmission may confound phylogenetic inference and obscure our ability to accurately infer th

81 ects for covariates, and permits statistical inference and penalized regression.

82 reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been

83 udy by expanding its spatiotemporal scope of inference and recommend this integrative methodology as

84 cal connections: dynamical systems, Bayesian inference and reinforcement learning.

85 sing model simulations-to carry out Bayesian inference and retrieve the full space of parameters comp

86 using computationally efficient variational inference and supports flexible sparsity constraints, al

87 and linear analogue switching for efficient inference and training.

88 eCellMix or EDec and (iii) guided biological inference and validation of deconvolution results with t

89 sophisticated yet remarkably early-emerging inferences and communicative behaviors that allow us to

90 ht also help increase the reproducibility of inferences and improve peer review.

91 sual stimulus locations), decisional (causal inference), and motor response dimensions.

92 ysed these with maximum likelihood, Bayesian inference, and a multispecies coalescent summary method,

93 cted by reinforcement learning than Bayesian inference, and that older adults rely more on reinforcem

94 e computational function-fast sampling-based inference-and predict further properties of these motifs

95 A section is devoted to network-based inference applications, i.e., prediction methods based o

96 versity, LefSe, and the Piphillin functional inference approach to estimate functional capacity.

97 is fast and accurate, outperforming existing inference approaches.

98 ns, and use it for benchmarking phylogenetic inference approaches.

99 nt of social epidemiology with formal causal inference approaches." The formidable, but not insurmoun

100 Crucially, our inferences are based only on a well-established mathemat

101 tations of predictive coding view perceptual inference as an NMDAR-dependent process of minimizing hi

102 C species delimitation methods support their inference as distinct (undescribed) species.

103 sizes pose a challenge in the estimation and inference, as network differences may be driven by diffe

104 Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes.

105 ataset, we demonstrate the feasibility of an inference attack on differentially private query results

106 red genomic data obtained from the attribute inference attack to infer the membership of a target in

107 on-specialists, make gene regulatory network inference available to any researcher, helping to deciph

108 t response and emergence of drug resistance: inference based on genomic, transcriptomic, epigenomic a

109 ng applications, may offer opportunities for inference based on optical and photonic systems.

110 Using G-computation, a causal inference-based method, we then estimated changes in mea

111 omes-wide analysis) used to establish causal inference between religion and health, epidemiologists n

112 omparisons between the hierarchies, enabling inferences between novel pairs.

113 in brachycephalic dogs and explore differing inferences between univariable and multivariable results

114 s the phylogenetic relationships by Bayesian inference (BI) and maximum likelihood (ML) searches and

115 mensional suppressive drive might ameliorate inference bias.

116 tion can be described as Bayesian perceptual inference but how are these Bayesian computations instan

117 th confounder variables reduces the error of inference by 30-35%, and that selection of cell-type inf

118 This framework guides mechanistic inference by directing functional validation studies to

119 TATEMENT While perceiving the world, we make inferences by learning the statistics present in the sen

120 presence of contamination, genetic ancestry inference can be substantially biased with existing meth

121 cientific goal, and that quantitative causal inference can learn from social epidemiology's methodolo

122 indicate that models learned through active inference can support adaptive behaviour in spite of, an

123 Inferences can be drawn from evolutionary analysis by co

124 ing (e.g., genetic liability), and no causal inferences can be made based on this study alone.

125 r causality on the basis of different causal inference concepts).

126 nome features from DNA sequence - to support inference concerning the regulatory effects of disease-a

127 Yet, new methodologies of selective inference could potentially improve power while retainin

128 e that single-cell analyses based on network inference coupled with quantitative computations can rev

129 of studies have begun to explore the reverse inference: creating brain-to-behaviour prediction models

130 Sound principles of statistical inference dictate that uncertainty shapes learning.

131 Phylogenetic and network inferences, drug resistant mutations (DRMs), subtypes an

132 We formulate the direct transmission inference (DTI) problem for inferring transmission trees

133 OFC during cue-cue learning abolished value inference during the probe test, inference subsequently

134 and (2) improving its accuracy by correcting inference errors using pedigree structure.

135 with electroencephalography, gauged sensory inference explicitly by behaviorally recording sensory s

136 d multivariable analyses generated differing inference for 11/30 (30.67%) disorders.

137 random effect with a large (fixed) variance, inference for random-slope models becomes feasible with

138 ous disease 2019 (COVID-19), as well as draw inferences for future investigations.

139 endence statistically, potentially affecting inferences for the relationships between environmental c

140 In active inference formulations of such models, the relative infl

141 such as periodicities or hierarchies, whose inferences fosters performance.

142 The active inference framework offers an attractive starting point

143 to cancer, we applied a recent computational inference framework to data from perturbation experiment

144 We adopt an empirical Bayes inference framework to fit the proposed hierarchical mod

145 n techniques, are more adapted to the causal inference framework.

146 ained by the lack of a coherent, model-based inference framework.

147 is likely protective based on these data and inference from human genetic analyses.

148 3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse.

149 lack full-length support and instead rely on inference from short reads that do not span the full len

150 me biogenesis in a framework that integrates inference from these models with experimental data.

151 Our findings complement and extend previous inferences from both the fossil record and initial molec

152 Cognitive maps enable efficient inferences from limited experience that can guide novel

153 expression of pathway genes which makes MOA inferences from transcriptional signatures (TSeses) a di

154 n of dual threshold optimization and network inference greatly expands the high-confidence TF network

155 lementary model in which "interoceptive self-inference" guides the estimation of expected uncertainty

156 ch for making energy-efficient deep learning inference hardware.

157 Trajectory inference has radically enhanced single-cell RNA-seq res

158 social epidemiology and quantitative causal inference have been seemingly at odds over the years.

159 rations at distinct levels of the perceptual-inference hierarchy may explain why hallucinations and d

160 Artificial intelligence inference, however, especially for visual computing appl

161 ference panels may be needed to draw correct inference in ancestrally diverse cohorts.

162 whether humans perform near-optimal Bayesian inference in contour integration, as opposed to using so

163 t can improve the accuracy of local ancestry inference in large pedigrees by: (1) using an existing a

164 Inference in our model is performed in a Bayesian framew

165 argue that many applications of statistical inference in psychology fail to meet this basic conditio

166 dynamics underlying statistical learning and inference in stable and volatile environments, in a popu

167 thology of evidence accumulation and ensuing inference in the brain.

168 use that relationship to correct subsequent inference in the validation set.

169 These tests offer guidelines for making inferences in future RNA structural studies with similar

170 Perception is a process of inference, integrating sensory inputs with prior expecta

171 bining quantitative modeling and statistical inference is a concrete way to investigate biological pr

172 ion of statistically significant Rt changes, inference is highly sensitive to the function choice.

173 pproximation, both population and cell-level inference is supported.

174 However, such inference is valuable because it reveals the genetic bas

175 Downstream of trajectory inference, it is vital to discover genes that are (i) as

176 article, we develop a new admixture network inference method called GTmix.

177 this work, we present scPADGRN, a novel DGRN inference method using "time-series" scRNA-seq data.

178 Previously, we developed a statistical inference method, robust co-evolutionary analysis (RoCA)

179 Causal inference methods (Interrupted Time Series and Differenc

180 ent applications including benchmarking tree inference methods and evaluating common inheritance patt

181 Statistical inference methods for such analyses must be scalable, an

182 latives are generally unreported and current inference methods typically detect only the degree of re

183 ughput profiling and transcriptional network inference methods: from activities of individual genes t

184 Hierarchical perceptual-inference models of psychosis may provide a holistic fra

185 roviding support for hierarchical perceptual-inference models of psychosis.

186 sing a range of gene sampling strategies and inference models to identify factors that may have contr

187 e social epidemiology more causal and causal inference more social.

188 early Homo has been fuelled by contradictory inferences obtained using different methodologies.

189 ans learn a strategy consistent with optimal inference of a hidden state.

190 on, based on parsimony analysis and Bayesian inference of a new morphological dataset.

191 performs coalescent-based maximum likelihood inference of admixture networks with inferred local gene

192 e binomial distribution that allows flexible inference of both within-lineage and between-lineage dif

193 mmunity, we develop a benchmark pipeline for inference of cell-type proportions and implement it in t

194 tochondrial DNA (mtDNA) mutations enable the inference of clonal relationships among cells.

195 for experimental detection and computational inference of CNVs from SNP array and next-generation seq

196 re, we develop a machine-learning algorithm, Inference of Connected expression quantitative trait loc

197 scale metabolic models have also enabled the inference of cooperative and competitive metabolic inter

198 BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hi

199 ractions is insufficient to support a strong inference of decanalization, but we argue that the adven

200 chical Bayesian latent variable model, where inference of differential misclassification is accomplis

201 teins is a powerful means to enable accurate inference of DNA-binding specificities.

202 While current technology permits inference of dynamic brain networks over long time perio

203 It highlights that the inference of ecological niches from geographical distrib

204 y explicit (i.e. continuous) phylogeographic inference of fast-evolving pathogens such as RNA viruses

205 thodology provides a framework for efficient inference of Gaussian mixture process noise models, with

206 The inference of gene regulatory networks (GRNs) from DNA mi

207 king transition between all cell states, and inference of genes that drive transitions.

208 s high diversity and dispersal confounds the inference of genetic structure, with multi-level samplin

209 Inference of genome ancestry identifies ~23% of the Gala

210 nments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, a

211 This inference of individual immunologic configurations based

212 duce a new software tool SONIA to facilitate inference of individual-specific computational models fo

213 (MPF-BML), for performing fast and accurate inference of maximum entropy model parameters, which was

214 lated Network Estimation (COZINE) method for inference of microbial networks which addresses these cr

215 se of PathFinder will enable a more reliable inference of migration histories and their posterior pro

216 brings about significant improvement in the inference of natural selection.

217 netic analysis, evolutionary rate estimates, inference of natural selective pressures, recombination

218 cation of protein and RNA makes possible the inference of past, present, and future cell states from

219 uld be inferred across the phylogeny, direct inference of phylogeny from rearrangement data in MLGO r

220 spaced genetic data allow for more accurate inference of population genetic parameters and hypothesi

221 Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which integrates pr

222 d collection of confounders have limited the inference of results.

223 literature involves the realization that the inference of selection from DNA sequence data first requ

224 n, estimation of selection coefficients, and inference of selection on polygenic traits.

225 is, temporally sampled sequences enable the inference of sequence divergence times.

226 MapGL makes phylogenetic inference of species-specific sequence gain and loss eas

227 MapGL simplifies phylogenetic inference of the evolutionary history of short genomic s

228 We report Spark-based INFERence of the molecular mechanisms of NOn-coding gene

229 genome sequences, permitting high-resolution inference of transmission.

230 Metagenomic inferences of bacterial strain diversity and infectious

231 analysis of transcriptional effects yielded inferences of high confidence for effects on splicing ev

232 Furthermore, inferences of horizontal gene transfer connected viral l

233 es a learned dictionary optimized for sparse inference on a D-Wave quantum annealer.

234 fined in a spatial context may be useful for inference on different types of relational knowledge.

235 g-computation, unlike WQS regression, allows inference on mixture effects that is unbiased with appro

236 r experimental results show that we can make inference on new structures given only five labeled samp

237 Finally, performing inference on the activity of circuits pushed far out of

238 A central processing unit-based model inference on the MR scanner took less than 1 second for

239 sts working with DNNs may need to base their inferences on groups of multiple network instances.

240 scent reports describing SARS-CoV-2, we make inferences on the basis of the parallel pathophysiologic

241 e, the temporal order of TMS effects allowed inferences on the dynamics of information exchange betwe

242 the family remain controversial, preventing inferences on the evolution of characters such as sexual

243 ies, making it difficult to make generalized inferences on the role of reproductive competition in dr

244 from systematic camera arrays have informed inferences on the spatiotemporal outcomes of predator-pr

245 Maximum-likelihood and Bayesian inference phylogenetic analyses of 13 PCGs and 68 termin

246 We use Maximum Caliber as an inference principle.

247 orld relies inherently on solving the causal inference problem, deciding whether sensory signals aris

248 proaches the maximum possible power for this inference problem.

249 We evaluate the performance of our Bayesian inference procedure through extensive simulations, showi

250 rns of brain-behavior associations drive the inference procedure.

251 PerturbNet inference procedures extract a detailed description of h

252 As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals

253 fects and develop efficient likelihood-based inference procedures.

254 ter interfaces that feed information to core inference processes and structure their behavioural expr

255 aepithelial neoplasia (CIN); however, causal inference remains uncertain.

256 to the biological mechanisms underlying the inference results, suggesting that enzyme processivity a

257 uping raw samples can significantly bias the inference results.

258 s that are "above the skin." Although causal inference should be a key goal for social epidemiology,

259 ished value inference during the probe test, inference subsequently shown in control rats to be sensi

260 Evolutionary inferences suggested a trend toward the acquisition of g

261 vated cognition, but recent work in Bayesian inference suggests that belief maintenance can be compat

262 hine-learning tool called "Subtype and Stage Inference" (SuStaIn) and to evaluate the utility of SuSt

263 we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algo

264 f human participants performing a predictive-inference task with two conditions that had different so

265 e dataset and can be used to perform several inference tasks; we provide an open-source implementatio

266 Contrary to standard parameter inference techniques, SMCE identifies sets of parameters

267 cell-specific transcriptomic data and causal inference testing, we identify examples where site-speci

268 however, imposes a limit to the strength of inference that can be drawn from such data.

269 This primary H/D KIE leads to the inference that hole-driven scission of the O-H bond in H

270 We build an approach for postprediction inference that naturally fits into the standard machine-

271 with three tooth rows supports the previous inference that the specimen is not of a juvenile.

272 adequately addressed in quantitative causal inference, that identifying causes is a worthy scientifi

273 We show that during successful inference, the mammalian brain uses a hippocampal prospe

274 We illustrate PCS inference through neuroscience and genomics projects of

275 n under five minutes with reconstruction and inference times < 7 min.

276 ith five clinical features by using Bayesian inference to develop probability-ranked differential dia

277 onent of computational accounts from sensory inference to higher cognition, the context effects found

278 ere we show that infants apply a disjunctive inference to identify the hidden object and use this log

279 This is the first study to use causal inference to measure the impact of longer shifts on sick

280 le have been optimised to enable statistical inferences to be drawn.

281 rough language descriptions and/or cognitive inference, to that of sighted individuals whose color-kn

282 Finally, we updated the genomic tracks, inference tool, and TF-binding profile similarity cluste

283 With literature-guided causal inference tools, >300 site-specific signaling relations

284 Conditional inference tree analysis revealed that ventral tissue bri

285 In order to perform inference under this model, we present an efficient Mark

286 e develop methods for correcting statistical inference using outcomes predicted with arbitrarily comp

287 We introduce PhISCS-BnB (phylogeny inference using SCS via branch and bound), a branch and

288 sing a novel computational algorithm, target inference via physical connection (TIPC).

289 Importantly, inference was accompanied by representations of associat

290 Inference was calculated using the nonparametric bootstr

291 Statistical inference was tested by means of a paired Students t-tes

292 hich bridges information theory and Bayesian inference, we derive a maximum entropy model of people's

293 parent discrepancies between smFRET and SAXS inferences, we integrated SAXS data with NMR data and re

294 d natural experiments that strengthen causal inference when assessing the association between unemplo

295 sults challenge leading models of perceptual inference where sensory uncertainty estimates depend onl

296 true network dynamics and the model used for inference, which is inevitable when modeling the real wo

297 e, and proposing novel methods for parameter inference with neural networks that incorporate the esti

298 We call inference with predicted outcomes postprediction inferen

299 edictive model and trained it using Bayesian inference with the longitudinal data from two published

300 on study interpretation) and performs causal inference with type I error control.

301 king use of approximate Bayesian statistical inference, with experimental measurements carried out af