ライフサイエンスコーパス: probabilistic model

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

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

3 et of pairing probabilities with a posterior probabilistic model.

4 nted using statistical inference in a single probabilistic model.

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

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

7 transcription factor binding sites based on probabilistic models.

8 n factors are most commonly represented with probabilistic models.

9 olecular genetics, stochastic simulation and probabilistic modelling.

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

11 Probabilistic model analyses were performed to predict t

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

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

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

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

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

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

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

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

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

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

22 A probabilistic modeling approach was used to assess the r

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

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

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

26 Generally probabilistic models are reasonably good approximations,

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

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

29 A probabilistic model based on pseudo free energies obtain

30 The probabilistic model-based approach proposed here provide

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

32 The proposed probabilistic model-based synapse detector accepts molec

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

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

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

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

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

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

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

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

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

42 The use of probabilistic models can overcome this limitation.

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

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

45 Phylo-grammars, probabilistic models combining Markov chain substitution

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

67 Historically, probabilistic models for decision support have focused o

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

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

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

71 Probabilistic models for these subcomponents provide new

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

73 A probabilistic model found reduced integration efficiency

74 Our probabilistic modeling framework combines a previously p

75 By adopting a probabilistic modeling framework compatible with the fam

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

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

78 A probabilistic model has been developed by considering th

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

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

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

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

83 We used probabilistic modelling in conjunction with protein stru

84 The apparent limitations of probabilistic models in representing complex nucleotide

85 Its probabilistic model includes site-specific parameters co

86 The probabilistic model is a dynamic Bayes network whose par

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

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

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

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

91 This probabilistic model leads to near-perfect classification

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

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

94 Probabilistic modeling methods akin to those used in spe

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

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

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

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

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

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

101 A probabilistic model of each dichotomous outcome, derived

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

103 We describe a probabilistic model of expression-magnitude distortions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

125 Integrated probabilistic modeling of gametogenesis developed in res

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

127 Probabilistic modeling of the transcriptomic data identi

128 Probabilistic modeling of these lag times revealed that

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

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

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

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

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

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

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

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

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

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

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

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

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

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

143 We use probabilistic models of trait evolution to investigate t

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

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

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

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

148 The probabilistic model predicted more trials correctly than

149 The probabilistic model predictions are weighted based on po

150 Our probabilistic model predicts the generation probability

151 Formal probabilistic models provide significantly greater accur

152 This probabilistic model provides a new global tool for scree

153 Probabilistic modelling provides a framework for underst

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

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

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

157 We then present probabilistic models showing how such large probabilitie

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

159 We use probabilistic modelling techniques to quantify pseudotim

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

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

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

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

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

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

166 We present a probabilistic model that deals with heterogeneity among

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

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

169 A probabilistic model that includes BI-RADS descriptors fo

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

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

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

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

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

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

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

177 Bayesian networks are probabilistic models that represent complex distribution

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

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

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

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

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

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

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

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

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

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

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

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

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

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

192 A comprehensive probabilistic model to obtain a definitive reconstructio

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

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

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

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

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

198 This paper applies probabilistic modeling to evaluate the effectiveness of

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

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

201 We have developed probabilistic models to quantify propensities of residue

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

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

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

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

206 We used a probabilistic model using methods that simultaneously es

207 Probabilistic modeling using binomial distribution funct

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

209 A probabilistic model was developed for the evaluation of

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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