ライフサイエンスコーパス: probabilistic model

1 et of pairing probabilities with a posterior probabilistic model.

2 s different tissues explicitly via a unified probabilistic model.

3 nted using statistical inference in a single probabilistic model.

4 oviding a novel method based on a parametric probabilistic model.

5 didates are scored and ranked using a simple probabilistic model.

6 est values (0.861, 0.955, and 0.991) for the probabilistic model.

7 M, or any other model, with respect to three probabilistic models.

8 subpopulations by constructing and comparing probabilistic models.

9 ge is the development of stable and accurate probabilistic models.

10 transcription factor binding sites based on probabilistic models.

11 owledge into computational rules, as well as probabilistic models.

12 single-cell omics datasets using pretrained probabilistic models.

13 n factors are most commonly represented with probabilistic models.

14 etic modifiers influence risk, supporting a 'probabilistic' model.

15 olecular genetics, stochastic simulation and probabilistic modelling.

16 The second involves probabilistic models, also known as splicing codes, whic

17 Probabilistic model analyses were performed to predict t

18 , we developed the Stubb program that uses a probabilistic model and a maximum likelihood approach to

19 We develop a general probabilistic model and an associated inference algorith

20 We combine a comprehensive probabilistic model and an extended data set, including

21 observed physical interactions into a simple probabilistic model and from it derive an interaction-me

22 ECHO is based on a probabilistic model and is able to assign a quality scor

23 e we improve this analysis by using a simple probabilistic model and the framework provided by scan s

24 The mathematical framework of probabilistic models and Bayesian inference can describe

25 thm based on supervised learning in flexible probabilistic models and find that it performs better th

26 te-of-the art molecular simulation, Bayesian probabilistic models, and high-throughput computation.

27 We formulate two general probabilistic models, and we propose computationally eff

28 A probabilistic modeling approach was used to assess the r

29 more use should be made of optimisation and probabilistic modelling approaches that have been succes

30 Two novel features of our probabilistic model are: (i) correlations between bindin

31 Recently, indel probabilistic models are mostly based on either hidden M

32 Generally probabilistic models are reasonably good approximations,

33 based on importance sampling, using simpler probabilistic models as proposal distributions.

34 We show how an elementary probabilistic model based on extreme value theory ration

35 We describe a probabilistic model based on network clustering and asyn

36 A probabilistic model based on pseudo free energies obtain

37 The probabilistic model-based approach proposed here provide

38 In addition, as a probabilistic model-based approach, BREM-SC is able to q

39 Here we propose a probabilistic model-based approach, Transcript Estimatio

40 Here, we present a probabilistic model-based method named MAAPER to utilize

41 The proposed probabilistic model-based synapse detector accepts molec

42 Here we describe a probabilistic-modeling-based method called CscoreTool-M

43 um probability models may supersede existing probabilistic models because they account for behaviour

44 ighest accuracy was achieved by the Bayesian probabilistic model (BRCAPRO).

45 val of gene expression experiments, we use a probabilistic model called product partition model, whic

46 Here we propose a probabilistic model called target identification from pr

47 finally suggest that inference for the full probabilistic model can be approximated with good perfor

48 More importantly, the resulting probabilistic model can be directly used for homology se

49 We demonstrate how the probabilistic model can be used to estimate credibility

50 Here we show how probabilistic modeling can provide a platform for the qu

51 We demonstrate that probabilistic models can be used to predict pharmacology

52 The use of probabilistic models can overcome this limitation.

53 semantics using Microsoft's GEC tool and the probabilistic model checker PRISM, demonstrating their a

54 ithms, and model properties calculated using probabilistic model checking.

55 Phylo-grammars, probabilistic models combining Markov chain substitution

56 In this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic

57 Recently, a simple probabilistic model considering the role of strong seed-

58 In particular, we consider a general probabilistic model described in Siepel and Haussler tha

59 To address this challenge, we present a new probabilistic model, DLCoal, that defines gene duplicati

60 The central nervous system therefore employs probabilistic models during sensorimotor learning.

61 Finally, we developed a probabilistic model for assigning spatial scores to matc

62 We have integrated this measure with a new probabilistic model for beta-contact prediction, which i

63 variables and propose a simple yet flexible probabilistic model for CCA in the form of a two-layer l

64 Considering our findings, we propose a probabilistic model for cell fate choice at the neural p

65 Here we develop a Bayesian probabilistic model for CFC-11, 12, and 113 banks and th

66 Here, we present a generic method based on a probabilistic model for clustering this type of data, an

67 With our dataset, we established a probabilistic model for cross-entity comparison of the p

68 and extend this solution to real data with a probabilistic model for errors.

69 ities reported in HTS assays, we developed a probabilistic model for estimating cumulative exposure o

70 We developed a probabilistic model for estimating the value of membrane

71 sis allows for incorporation of a reasonable probabilistic model for generating data.

72 We develop a new probabilistic model for genotype calling and haplotype p

73 anscription Start sites Tracking Program), a probabilistic model for identifying active miRNA TSSs fr

74 den Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks fr

75 Recently we have presented a probabilistic model for peptide identification that uses

76 ngle expression experiment, based on a joint probabilistic model for promoter sequence and gene expre

77 combination of a Hamming graph and a simple probabilistic model for sequencing errors.

78 urrently missing from thesauri, we develop a probabilistic model for the construction of synonym term

79 This is achieved by first developing a probabilistic model for the prevalence of vp1 sequences

80 ility of estimated mutation rates, we used a probabilistic model for the statistical analysis.

81 the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory n

82 e simple assumptions, we develop a dynamical probabilistic model for these misannotation chains.

83 We have developed and validated new probabilistic models for 3D motif sequences based on hyb

84 Historically, probabilistic models for decision support have focused o

85 Simple probabilistic models for food web structure, however, ar

86 in goal in this paper is to develop accurate probabilistic models for important functional regions in

87 work enables the construction of very useful probabilistic models for protein families that allow for

88 Probabilistic models for these subcomponents provide new

89 The ability to infer probabilistic models for timing may allow mice to adapt

90 A probabilistic model found reduced integration efficiency

91 Our probabilistic modeling framework combines a previously p

92 By adopting a probabilistic modeling framework compatible with the fam

93 Here we introduce a probabilistic modeling framework, Analysis of CRISPR-bas

94 an process regression (GPR) is used to fit a probabilistic model from which replicates may then be dr

95 etypal statistical framework - the Graphical Probabilistic Model (GPM).

96 A probabilistic model has been developed by considering th

97 developments on computational side based on probabilistic modeling have shown promising direction to

98 Over time, several probabilistic models have been proposed that account for

99 Diffusion probabilistic models have generated realistic images fro

100 Diffusion probabilistic models have made their way into a number o

101 oding of the chemical shift information in a probabilistic model in Markov chain Monte Carlo simulati

102 Examination of the accuracy of another indel probabilistic model in the light of our formulation indi

103 ty of the object are best accounted for by a probabilistic model in which the perceived boundary of t

104 We used probabilistic modelling in conjunction with protein stru

105 the clarity of descriptions and reporting of probabilistic models in phylogenetic studies, ultimately

106 The apparent limitations of probabilistic models in representing complex nucleotide

107 Its probabilistic model includes site-specific parameters co

108 The probabilistic model is a dynamic Bayes network whose par

109 Additionally, a probabilistic model is developed to account for both ale

110 Moreover, we show that the probabilistic model is not as sensitive to various exper

111 ass automatic extracting information through probabilistic modeling is adaptable for blending with ma

112 ilarity, motifs, profiles, protein folds and probabilistic models - it is possible to develop charact

113 stallographic Map Interpreter), which uses a probabilistic model known as a Markov field to represent

114 oposed approach is based on a discriminative probabilistic model known as conditional random fields t

115 This probabilistic model leads to near-perfect classification

116 Furthermore, the flexibility in the probabilistic models makes it possible to extend this fr

117 Dynamic Regulatory Events Miner (mirDREM), a probabilistic modeling method that uses input-output hid

118 Probabilistic modeling methods akin to those used in spe

119 An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to

120 Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajecto

121 Modeling is presented that creates a compact probabilistic model of a given target network, which can

122 Here we propose a probabilistic model of AD in which three variants of AD

123 Based on this probabilistic model of binding data, we further pursue i

124 g reward contingencies, we derived a unified probabilistic model of CA1 representations centered on a

125 Clone size and composition support a probabilistic model of cell fate allocation and in silic

126 Here, we show that a size-based probabilistic model of cell-size control at the G2/M tra

127 tional priors in the context of a generative probabilistic model of ChIP data and genome sequence.

128 ngle-cell data, we developed a quantitative, probabilistic model of cone cell decisions in the retina

129 mportance sampling algorithm that combines a probabilistic model of DNA sequencing data with a enumer

130 A probabilistic model of each dichotomous outcome, derived

131 ed the goodness-of-fit of each theory with a probabilistic model of exon/intron evolution and multipl

132 We describe a probabilistic model of expression-magnitude distortions

133 Here, we use a probabilistic model of genome evolution that accounts fo

134 an attempt to improve the goodness of fit, a probabilistic model of late loss was created on the basi

135 the global FLUXNET 2015 database aided by a probabilistic model of microbial growth to examine the e

136 ed by modifying miRDeep, which is based on a probabilistic model of miRNA biogenesis in animals, with

137 braries, we adapted miRDeep, which employs a probabilistic model of miRNA biogenesis, to analyze the

138 ear model for functional genomic data with a probabilistic model of molecular evolution.

139 We present a probabilistic model of NAHR and demonstrate its ability

140 ructured internal dynamics in a hierarchical probabilistic model of natural images.

141 been reconstructed here by using an explicit probabilistic model of network evolution.

142 We derive a probabilistic model of noise-corrupted and replicated ti

143 s, known collectively as Riptide, comprise a probabilistic model of peptide fragmentation chemistry.

144 We show that a probabilistic model of phylogenetic profiles, trained fr

145 We present a generative, probabilistic model of RNA polymerase that fully describ

146 ultiple alignment which couples a generative probabilistic model of sequence and structure with an ef

147 Here, we present a probabilistic model of species discovery to assess the u

148 ring of the immune repertoire, and provide a probabilistic model of the dynamics of antibody memory f

149 tion from noisy measurements is fused with a probabilistic model of the environment.

150 We introduce a general probabilistic model of the gene structure of human genom

151 Our aim in this article is to develop a probabilistic model of the rearrangement process and a B

152 has been suggested to reflect learning of a probabilistic model of the sensory world.

153 ; and (iii) the assembly is polished using a probabilistic model of the signal-level data.

154 ance of NRE, WNLR, and Patlak analysis for a probabilistic model of time-activity curves.

155 In this work, we present a probabilistic model of transcription dynamics which is f

156 Using a probabilistic model of transgenerational TL and populati

157 We develop a probabilistic model of transport and mixing of tracer pa

158 Quantum theory is a powerful framework for probabilistic modeling of cognition.

159 Integrated probabilistic modeling of gametogenesis developed in res

160 Probabilistic modeling of loss-of-function screens and m

161 Models TF (CSHMM-TF) method which integrates probabilistic modeling of scRNA-Seq data with the abilit

162 Probabilistic modeling of the transcriptomic data identi

163 Probabilistic modeling of these lag times revealed that

164 a distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific asp

165 la chromatin states derived from data-driven probabilistic modelling of dependencies between chromati

166 hromosomal conformation capture coupled with probabilistic modelling of experimental noise to resolve

167 Probabilistic models of adaptive immune repertoire seque

168 mensional scaling, or using explicit spatial probabilistic models of allele frequency evolution.

169 ackground of other conserved sequences using probabilistic models of expected mutational patterns in

170 To test our second hypothesis, we used probabilistic models of expected species co-occurrence p

171 ations of human hematopoietic cells and used probabilistic models of gene expression and analysis of

172 ate that lateral gene transfers, detected by probabilistic models of genome evolution, can be used as

173 ling networks on a genome scale using unique probabilistic models of molecular interactions on a per-

174 Brain-inspired probabilistic models of neural network can explicitly ha

175 ic-HMM model which generalizes the classical probabilistic models of Neyman and Felsenstein.

176 ant problem is how to formulate and estimate probabilistic models of observed genotypes that account

177 roach also makes it possible to develop full probabilistic models of pseudoknotted structures to allo

178 important because it means we can build full probabilistic models of RNA secondary structure, includi

179 We present a family of probabilistic models of sound change as well as algorith

180 Here, this question is explored using simple probabilistic models of test behaviour.

181 Generating and updating probabilistic models of the environment is a fundamental

182 orary views propose that the brain maintains probabilistic models of the world to minimize surprise a

183 ies suggest that organisms learn and exploit probabilistic models of their environment.

184 We use probabilistic models of trait evolution to investigate t

185 d message passing method for the solution of probabilistic models on networks such as epidemic models

186 ing major surgery to develop a multivariable probabilistic model optimized for nonlinearity of serum

187 mosomal splicing, in individual reads, using probabilistic models or a database of known splice sites

188 Based on a probabilistic model, our method recursively partitions t

189 De-noising diffusion probabilistic model outperformed deep discriminative mod

190 e (Lipschitz) continuous with regards to the probabilistic modeling parameters, B) convergent metabol

191 Here we describe a probabilistic modeling pipeline that accounts for biolog

192 The probabilistic model predicted more trials correctly than

193 The probabilistic model predictions are weighted based on po

194 Our probabilistic model predicts the generation probability

195 Probabilistic models provide a low-error, compressed rep

196 Formal probabilistic models provide significantly greater accur

197 This probabilistic model provides a new global tool for scree

198 Probabilistic modelling provides a framework for underst

199 lity of transmission per coital act, using a probabilistic model published elsewhere.

200 LDA, a probabilistic model, reduces assemblages to sets of dist

201 human, mouse and Drosophila genes using 1017 probabilistic models representing over 600 different tra

202 All iterations of the probabilistic model returned cost-effectiveness ratios l

203 We then present probabilistic models showing how such large probabilitie

204 We then construct probabilistic models-specifically, marked renewal proces

205 Probabilistic models such as Bliss independence have bee

206 ergistic efficacy in drug combinations using probabilistic models such as Bliss independence.

207 , a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic bio

208 ral vision, together with the development of probabilistic modeling techniques, have provided insight

209 We use probabilistic modelling techniques to quantify pseudotim

210 A central piece of our approach is a probabilistic model that accommodates node, link and hyb

211 of the underlying variability, an objective probabilistic model that accounted for all of the data i

212 ian process regression (GPR) combined with a probabilistic model that accounts for uncertainty about

213 dPeak implements a probabilistic model that accurately describes ChIP-Seq d

214 ultiple miRNAs to a ceRNA interaction with a probabilistic model that addresses previously neglected

215 We have developed a general probabilistic model that clusters genes and experiments

216 e resulting from structural variants using a probabilistic model that combines multiple signals in ba

217 PIPmiR uses a probabilistic model that combines RNA structure and expr

218 Here, we describe a probabilistic model that combines the effects of mutatio

219 We present a probabilistic model that deals with heterogeneity among

220 Therefore, we formulate a probabilistic model that divides the genes into two sets

221 Here, we introduce scPDA, a probabilistic model that employs a variational autoencod

222 indings suggest the existence of an internal probabilistic model that facilitates behavioral adaptati

223 The basis of our approach is a new probabilistic model that finds the most likely haplotype

224 A probabilistic model that includes BI-RADS descriptors fo

225 e incorporate distinguishing features into a probabilistic model that infers the number of cells to s

226 on of data SOurces using Networks), a formal probabilistic model that integrates background biologica

227 We developed Watershed, a probabilistic model that integrates multiple genomic and

228 Mixture model on graphs (MMG) is a probabilistic model that integrates network topology wit

229 across a landscape of single cells, using a probabilistic model that is robust against the data limi

230 rcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of ce

231 ts for the next 20 years.Methods: We built a probabilistic model that linked state-specific estimates

232 e we extend these approaches and construct a probabilistic model that not only compensates for motor

233 First, we devise a probabilistic model that predicts nucleotide-specific el

234 previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data t

235 Here, we develop a novel generative, probabilistic model that simultaneously captures local s

236 d validated the IMPACT-Better Ageing Model-a probabilistic model that tracks the population aged 35-1

237 These results demonstrate the importance of probabilistic modeling that delves deeper into molecular

238 Hidden Markov models (HMMs) are probabilistic models that are well-suited to solve many

239 HILARy combines probabilistic models that capture the receptor generatio

240 modeling and flexible fitting; and 3) build probabilistic models that combine high-accuracy priors (

241 Bayesian networks are probabilistic models that represent complex distribution

242 ngs, we propose the use of a single coherent probabilistic model, that encompasses much of the rich s

243 In the wider field of probabilistic modelling, the stochastic EM (sEM) algorit

244 Ultimately, our results argue that for the probabilistic model there is indeed a statistical effect

245 model incorporates the read information in a probabilistic model through base quality scores within e

246 abilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors w

247 work to add context for homology transfer, a probabilistic model to account for the uncertainty in la

248 evised an efficient sampling method within a probabilistic model to achieve superior performance than

249 Here we present MultiVI, a probabilistic model to analyze such multiomic data and l

250 We present a probabilistic model to assess these risk trade-offs.

251 rom microarray/qRT-PCR platforms and a local probabilistic model to assign mapping results to the mos

252 Here, we developed a probabilistic model to cluster genomic sequences based o

253 Here, we present adaPop, a probabilistic model to estimate past population dynamics

254 We present a probabilistic model to estimate the number of turbines t

255 oduce Mixture Model on Graphs (MMG), a novel probabilistic model to identify differentially expressed

256 We used a probabilistic model to infer 8349 (M, NF-kappaB/RelA, TG

257 c Map Interpreter), an algorithm that uses a probabilistic model to infer an accurate protein backbon

258 We introduce a novel probabilistic model to infer transcription factor activi

259 small molecule identification by learning a probabilistic model to match small molecules with their

260 A comprehensive probabilistic model to obtain a definitive reconstructio

261 We developed a probabilistic model to predict target-by-target harvesti

262 further smoothed and post-processed using a probabilistic model to predict the most likely transitio

263 To this end, we have designed a Bayesian probabilistic model to predict the probability of dichot

264 ministic screening algorithm combined with a probabilistic model to score gene candidates.

265 We apply a probabilistic model to the progeny of a single cross and

266 onth outcomes were combined with a long-term probabilistic model to yield quality-adjusted life years

267 fier, and (ii) deploy a de-noising diffusion probabilistic model to yield reliable in-focus images.

268 BATCHIE uses information theory and probabilistic modeling to design each batch to be maxima

269 This paper applies probabilistic modeling to evaluate the effectiveness of

270 Here, we propose the use of probabilistic models to analyze the structure of the Ind

271 We have developed probabilistic models to identify sequence motifs of resi

272 HIV-1- or HCV-infected individuals and learn probabilistic models to predict the likelihood of bNAb d

273 We have developed probabilistic models to quantify propensities of residue

274 and wave simulations are combined with novel probabilistic models to quantify the likelihood of rogue

275 Of interest in this article, is the use of probabilistic modelling tools with which parameters and

276 ctive motifs with a positional preference, a probabilistic model (used reasonably) generally provides

277 Here, we introduce a probabilistic model useful for predicting protein intera

278 We used a probabilistic model using methods that simultaneously es

279 Probabilistic modeling using binomial distribution funct

280 efforts bring together mathematical models, probabilistic models, visual representations, and data t

281 A computer-based probabilistic model was created in which fibrosis of CHC

282 A probabilistic model was developed for the evaluation of

283 A "bottom-up" probabilistic model was developed using engineering firs

284 s and maximum likelihood estimation of three probabilistic models was used to automatically construct

285 Using an atom-based probabilistic model, we estimate the membrane helical in

286 Using a probabilistic model, we found that 1,922 genetic interac

287 Using a simple probabilistic model, we generate a set of predictions on

288 By examining the underlying high-dimensional probabilistic models, we reveal that the training proces

289 th paired read and read depth signals into a probabilistic model which can analyze multiple alignment

290 We introduce treeHFM, a probabilistic model which generalizes the theory of hidd

291 ome these problems we developed a generative probabilistic model which identifies a (small) subset of

292 Here we use this principle to construct probabilistic models which describe the correlated spiki

293 g-linear models (CLLMs), a flexible class of probabilistic models which generalize upon SCFGs by usin

294 Our approach is based on a unified probabilistic model, which is learned from the data usin

295 We present a principled probabilistic model with a Bayesian inference scheme to

296 Furthermore, by providing an explicit probabilistic model with a relatively simple nonlinear s

297 omes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain

298 us, our formulation will provide other indel probabilistic models with a sound reference point.

299 he presence of variable information requires probabilistic models, yet it is unclear whether animals

300 lly from single-cell swimming behavior using probabilistic models, yet the mechanistic foundations of