ライフサイエンスコーパス: Markov model

1 overview of the components of a decision and Markov model.

2 counts for any branch under a nonstationary Markov model.

3 s (CNV) was performed using the eXome-Hidden Markov Model.

4 se were evaluated over a lifetime horizon by Markov model.

5 Patient-level Monte Carlo-based Markov model.

6 The method is based on a two-state Hidden Markov Model.

7 ere assessed using a longitudinal multistate Markov model.

8 rary by sampling from an Input-Output Hidden Markov Model.

9 (PD) and death) were analyzed in the current Markov model.

10 effects were projected by using a validated Markov model.

11 ta, in particular exome data, using a hidden Markov model.

12 ata to a reference through the use of hidden Markov models.

13 e states were assessed using continuous-time Markov models.

14 the effect of genetic variation using hidden Markov models.

15 both sequence alignments and profile hidden Markov models.

16 ation and contextual analysis through Hidden Markov Models.

17 more standard classical ones, such as hidden Markov models.

18 iple sequence alignment tool based on hidden Markov models.

19 zed using logistic regression and multistate Markov modeling.

20 ios were consistent with cost-effectiveness (Markov model, 58.5% [95% CI 54.2-62.8]; multilevel model

21 ssion than predicted for untreated controls (Markov model, 75.8% [95% CI 71.4-80.2]; multilevel model

22 MR detection based on non-homogeneous hidden Markov model), a novel Bayesian framework for detecting

23 ed the Hierarchical Dirichlet Process Hidden Markov Model, a non-parametric extension of the traditio

24 through the graph using an efficient hidden Markov model, allowing for recombination between differe

25 Combining JPGM with hidden Markov model allows genome-wide inference of RNA structu

26 The Markov model allows patients to see, for their phenotype

27 mple, capturing dynamics with a second-order Markov model allows us to reveal actual travel patterns

28 To do so, we propose augmented Markov models (AMMs), an approach that combines concepts

29 ransitions were reconstructed through hidden Markov model analysis.

30 By combining NMR-detected H/D exchange with Markov modelling analysis of an aggregate of 275 microse

31 The system is based on a hidden Markov model and a general linear model.

32 We developed a tiered hidden Markov model and applied it to analyze a ChIP-seq datase

33 strategies (support vector machines, hidden Markov model and decision tree) and developed an algorit

34 h-based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework

35 We have developed a hidden Markov model and optimization procedure for photon-based

36 stimate the attraction radii, we developed a Markov model and related it to the acquired data.

37 classification tool based on profile hidden Markov models and graph algorithms.

38 We concluded that hidden Markov models and random forest imputation are more suit

39 alled MRHMMs (Multivariate Regression Hidden Markov Models and the variantS) that accommodates a vari

40 nderstanding time-series data such as hidden Markov models and time-structure based independent compo

41 ific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM compa

42 tion by marrying generative learning (hidden Markov models) and discriminative learning (support vect

43 py numbers for individuals based on a hidden Markov model, and identified cases and controls that had

44 we outline a nonparametric multilevel latent Markov modeling approach and apply it to 2 longitudinal

45 advantages and limitations of research with Markov models are described, and new modeling techniques

46 We present a hidden Markov model based approach we call delta-bitscore (DBS)

47 We develop a hidden Markov model based maximum-likelihood approach for estim

48 We constructed a Markov model based on anal histology in HIV-positive MSM

49 Previously, we developed a Markov model based on the presence of one IVM binding si

50 Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions thro

51 Hidden Markov model, based on Li and Stephens model that takes

52 We show that our approach extends a previous Markov model-based approach to additionally score all pa

53 elop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to

54 Profile Hidden Markov Model-based homology search has been widely used

55 ortholog identification using subtree hidden Markov model-based placement of protein sequences to phy

56 Information from hidden Markov model-based sequence profiles and domain architec

57 ical simulations and with a Bayesian (hidden Markov model-based) analysis of single particle tracking

58 R elements is calculated using a pair-Hidden Markov Models-based method.

59 passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis

60 fam entry is represented by a profile hidden Markov model, built from alignments generated using Repe

61 ned in sequence alignments or profile hidden Markov models by drawing a stack of letters for each pos

62 Materials and Methods We developed Markov models by using a US-payer perspective and lifeti

63 with HF with reduced ejection fraction, the Markov model calculated that sacubitril/valsartan would

64 d Durbin developed a coalescent-based hidden Markov model, called the pairwise sequentially Markovian

65 We develop an extended hidden Markov model capable of accurately describing the region

66 Markov model compared the following surveillance EMB str

67 ve software pipeline based on profile hidden Markov models constructed from manually curated IS eleme

68 Methods Using a Markov model, costs per SRE avoided were calculated for

69 We propose a dynamic Bayesian Markov model (DBM) for simultaneous genotype calling and

70 A Markov model described the history of the disease and tr

71 e alignments to evaluating matches to hidden Markov models describing protein domain families.

72 limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate chromati

73 etical framework of so-called discrete-state Markov models (DMMs), whereby activation is conceptualiz

74 OURIER PCSK9i trial population and created a Markov model during the time horizon of a full lifetime.

75 n remains unknown; therefore, we developed a Markov model estimating the incremental cost-utility rat

76 loped the Functional Analysis through Hidden Markov Models (FATHMM) software and, using a model weigh

77 gorithms are employed to optimize the Hidden Markov Models, feature selection and heterogeneous ensem

78 h utilizes hierarchical clustering of hidden Markov modeling-fitted single-molecule fluorescence reso

79 PD mass spectra, passing results to a hidden Markov model for de novo sequence prediction and scoring

80 but they are often interpreted directly as a Markov model for stage transitions.

81 ampling algorithm coupled with a first-order Markov model for the background nucleotide sequences to

82 restricting to the simplest possible hidden Markov model for the underlying channel current, in whic

83 tions were assigned using families of hidden Markov models for c. 11% of open reading frames; M. pyri

84 Here, we used hidden Markov models for fitting the DNA sequence dynamics, and

85 st bioinformatics pipeline exploiting Hidden Markov Models for the identification of nuclease bacteri

86 xploit the recently developed multi-ensemble Markov model framework to compute full protein-peptide k

87 effectiveness analysis was conducted using a Markov model from the Canadian Public health (Ontario) p

88 A cost-effectiveness analysis using a Markov model from the hospital perspective was conducted

89 BOA generates profile Hidden Markov Models from the clusters of bacteriocin context g

90 an filter and graphical models (i.e., Hidden Markov Model, Fully Connected Chain Model) to specifical

91 existing mitochondrial genomes using hidden Markov model gene profiles.

92 Markov models have emerged as a powerful way to approxim

93 ribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequence co

94 s of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patte

95 Based on a Hidden Markov Model (HMM) approach, SCOPE++ accurately identifi

96 oupling information with the pairwise Hidden Markov Model (HMM) based profile alignment method to imp

97 f trusted representative sequences, a hidden Markov model (HMM) built from that alignment, cutoff sco

98 In this article, we present a hidden Markov model (HMM) for ibd among a set of chromosomes an

99 The profile hidden Markov model (HMM) framework enables the construction of

100 The hidden Markov model (HMM) has been a workhorse of single-molecu

101 y search based on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alignment m

102 edicted binding sites using NR-HMM, a Hidden Markov Model (HMM) model.

103 t are siblings of the donors, using a hidden Markov model (HMM) of identity-by-descent (IBD) states a

104 transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed tha

105 y informative copying states within a Hidden Markov Model (HMM) phasing algorithm.

106 The hidden Markov model (HMM) search tools now use HMMER3, dramatic

107 vailable gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for

108 state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinator

109 ementation of VIPR by incorporating a hidden Markov model (HMM) to detect recombinant genomes.

110 m transposon mutant libraries using a Hidden Markov Model (HMM), along with formulas to adapt the par

111 A Hidden Markov Model (HMM), trained on CLIP-seq data, was used t

112 Here we describe a hidden Markov model (HMM)-based algorithm mCarts to predict clu

113 tion on sequencing output and using a hidden Markov model (HMM)-based filter to exploit heretofore un

114 Using a combination of profile Hidden Markov Model (HMM)-based homology searches, network anal

115 In this article, we introduce a new Hidden Markov Model (HMM)-based method that can take into accou

116 orrEction in Rna-seq data (SEECER), a hidden Markov Model (HMM)-based method, which is the first to s

117 hermal fluctuation data, we develop a hidden Markov model (HMM)-based method.

118 A number of hidden Markov model (HMM)-based methods have been developed to

119 ption units de novo using a two state hidden-Markov model (HMM).

120 iple sequence alignment and a profile hidden Markov model (HMM).

121 Hidden Markov modeling (HMM) has revolutionized kinetic studies

122 We used multi-state hidden Markov models (HMM) to characterize states of diving beh

123 Existing hidden Markov models (HMM)-based imputation approaches require

124 ortant sequence analysis applications-hidden Markov models (HMM).

125 we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimod

126 This is based on hidden Markov models (HMMs) and is available together with a co

127 Hidden Markov models (HMMs) and transition density plots (TDPs)

128 Hidden Markov models (HMMs) are flexible and widely used in sci

129 Hidden Markov models (HMMs) are probabilistic models that are w

130 Although hidden Markov models (HMMs) may be used to infer the conformati

131 tic models are mostly based on either hidden Markov models (HMMs) or transducer theories, both of whi

132 M combines phylogenetic networks with hidden Markov models (HMMs) to simultaneously capture the (pote

133 We propose two hidden Markov models (HMMs), HMM-ASE and HMM-NASE that consider

134 framework for DMR detection based on hidden Markov models (HMMs).

135 environmental microorganisms based on Hidden Markov Models (HMMs).

136 ion in MCF7 and MCF7-T cells to train hidden Markov models (HMMs).

137 Most well-used CSDs are based on hidden Markov models (HMMs).

138 We applied hidden semi-Markov models (HSMM) to hourly geographic positioning sy

139 e such promising method, the infinite hidden Markov model (iHMM), generalizes the HMM that itself has

140 This Markov model illustrates how patients, families, and pro

141 A hidden Markov model in combination with a position-specific sco

142 edictability, and describe the accuracy of a Markov model in predicting a person's next location.

143 called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and

144 raction (DDI), an interaction profile hidden Markov model (ipHMM) is first built for the domain famil

145 In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate dependence in

146 a brief explanation of decision analysis and Markov models is presented in simple steps, followed by

147 The deployment of multiple hidden Markov models is proposed to computationally classify mu

148 The repertoire of profile hidden Markov model libraries, which are used for annotation of

149 The hidden Markov model library that provides sequence homology to

150 t were provided, was analyzed through Latent Markov Models (LMMs).

151 as follows: multivariate mixed model, hidden Markov model, logical algorithm, k-nearest neighbor, sin

152 TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic

153 e, we test the ability of the profile hidden Markov model method HMMER3 to correctly assign homologou

154 nal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor s

155 s, we conducted database searching by hidden Markov models, multiple sequence alignment, and phylogen

156 A Markov model of asthma was created to simulate the effec

157 We used a Markov model of average-risk CRC screening to compare th

158 The authors developed a Markov model of pancreatic ductal adenocarcinoma (PDAC).

159 more specifically, a general continuous-time Markov model of the evolution of an entire sequence via

160 A Markov model of the natural history of HCV was developed

161 nfirming the predictions of a nonequilibrium Markov model of translation.

162 The 2-state Markov model of US adult patients (mean age, 63.8 years)

163 probabilities between categories of BP using Markov modeling of cross-sectional data from the Nationa

164 Markov modeling of the binary response for eGFR (greater

165 or Next-Generation Sequencing data with the Markov models of the background genomes.

166 We constructed decision-analytic Markov models of the natural disease progression of HCV

167 ned their behavioural phenotype using hidden Markov models of their movement and body temperature.

168 Together, these hidden Markov models offer a powerful approach for dealing with

169 Using hidden Markov models on insertion, deletion, nucleotide substit

170 Our model consists of a pair of hidden Markov models--one for the germline and one for the tumo

171 ce alignments and statistical models (hidden Markov models or HMMs).

172 We used a Poisson hidden Markov model (PHMM) of RNA-Seq data to identify potentia

173 s the inserted sequence using profile hidden Markov model (PHMM)-based guided assembly.

174 lies are often represented by profile hidden Markov models (pHMMs).

175 lignment, including BLAST and profile hidden Markov models (profile HMMs), are not based on any expli

176 didate and living donor characteristics, the Markov model provides the expected patient survival over

177 n, based on probabilistic matching to hidden Markov model representations of the reference data, whic

178 Photon distribution and hidden Markov modeling revealed fast dynamic and slow conformat

179 Markov modeling revealed that the cell state transitions

180 Markov modeling reveals a novel "pinched" SF configurati

181 holog identification based on subtree hidden Markov model scoring.

182 When used in conjunction with the hidden Markov model search tool nhmmer, Dfam produces a 2.9% in

183 NA-binding proteins are identified by hidden Markov model searches, of which mRBPs encompass a relati

184 M-HF trial were used as inputs for a 2-state Markov model simulated HF.

185 A Markov model simulated progression from HCC diagnosis to

186 Materials and Methods We developed a Markov model simulating 10-year outcomes for 60-year-old

187 We created a Markov model simulating 2 cohorts of 21-year-old patient

188 We developed a Markov model simulating a hypothetical cohort of commerc

189 ansition probabilities were calculated for a Markov model simulating the natural history of patients

190 Our Markov model structure had a 1 year cycle length and con

191 Simulations of the Markov models suggest that in men with diabetes aged 50

192 owed by analysis based on a two-state hidden Markov model, taking advantage of the availability of mu

193 A hidden-Markov model test for selection also found widespread ev

194 We used a microsimulation Markov model that accounts for major sources of variabil

195 Using a 2-step piecewise homogenous Markov model that accounts for the distinction between p

196 "delirium," was analyzed using a first-order Markov model that adjusted for eight covariables.

197 Specifically, we extend a hidden Markov model that is widely used to describe haplotype s

198 ference using our previously proposed hidden Markov model that models homopolymer errors and then mer

199 al knowledge with the non-homogeneous hidden Markov model that models spatial correlation.

200 a novel Bayesian approach based on a hidden Markov model that predicts how the C4 phenotype evolved.

201 We used a Markov model to assess the cost-effectiveness of treatme

202 this research, we implemented a hidden semi-Markov model to characterize the amino acid composition

203 iables to capture the variances and a Hidden Markov model to characterize the reads dependency across

204 We developed a Markov model to compare the cost and effectiveness of re

205 he value of these treatments, we developed a Markov model to compare the cost-effectiveness of differ

206 n and differentiation with a branching state Markov model to describe the cell population dynamics.

207 We used the hidden Markov model to describe the spatiotemporal pattern of a

208 thylated DNA, we were able to train a hidden Markov model to distinguish 5-mC from unmethylated cytos

209 We used a semi-Markov model to do the cost-utility analysis from a heal

210 e imaging results, to construct a multistate Markov model to estimate four different age-specific bio

211 We used a decision analytic Markov model to estimate lifetime costs and benefits in

212 We used a decision tree and Markov model to estimate the cost-effectiveness of the c

213 ting the two birth cohorts into a five-state Markov model to estimate the number of years of disabled

214 l course of illness, we developed a lifetime Markov model to estimate the primary economic outcome of

215 We developed a decision-analytic Markov model to evaluate the cost effectiveness of docet

216 Further, we apply a hidden Markov model to identify copy-neutral LOH (loss of heter

217 our previous work, we developed a new hidden Markov model to incorporate a two-site joint emission te

218 are application, AD-LIBS, that uses a hidden Markov model to infer ancestry across hybrid genomes wit

219 Finally, our method uses a hidden Markov model to integrate multiple sources of informatio

220 er highly diffusive motion by using a hidden Markov model to jointly analyze multiple stochastic read

221 ed the family relationship and used a hidden Markov model to jointly infer CNVs for three samples of

222 Using a hidden Markov model to map placental PMDs genome-wide and compa

223 d further utilizes a infinite-state Bayesian Markov model to perform de novo stratification and admix

224 We constructed a dynamic Markov model to represent India's tuberculosis epidemic,

225 We developed a decision analytical Markov model to simulate opioid overdose, HIV incidence,

226 Methods We constructed a Markov model to simulate primary, adjuvant, and salvage

227 We developed a hybrid decision tree/Markov model to simulate the costs, effectiveness, and i

228 The tool uses a two-state Markov model to simulate the evolution of methane leakag

229 We used multi-state Markov modeling to examine transitions among HFREF, HFPE

230 e applied Bayesian model selection to hidden Markov modeling to infer transient transport states from

231 We developed two Markov models to compare the cost and effectiveness of f

232 , we used hydrophobicity profiles and hidden Markov models to define a structural repeat common to al

233 more, for these mediated transitions, we use Markov models to determine whether the native state acts

234 s) channel required modification of existing Markov models to include these features of channel behav

235 y addresses the use of decision analysis and Markov models to make contemplated decisions for surgica

236 odeling method that uses input-output hidden Markov models to reconstruct dynamic regulatory networks

237 We used Markov models to simulate disease progression, quality o

238 dress potential survivorship bias, we fitted Markov models to the distribution of discrete post-trans

239 babilistic inference methods based on hidden Markov models to the problem of homology search.

240 We developed a state-transition (or Markov) model to calculate costs incurred and quality-ad

241 The hidden Markov models traditionally used for haplotypes are hind

242 omic sequences to binary strings, homogenous Markov models trained on the binary sequences are used t

243 We developed a Markov model using data from multiple sources, including

244 develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal pr

245 A Markov model was applied to estimate differences in tota

246 A Markov model was built to evaluate quality-adjusted life

247 A Markov model was built to simulate outcomes of individua

248 An analysis of all trajectories by a hidden Markov model was consistent with two diffusion states wh

249 A Markov model was constructed that followed each implant

250 A decision-analytic Markov model was constructed to evaluate the cost-effect

251 A decision-analytic Markov model was constructed to evaluate the cost-effect

252 A Markov model was constructed to simulate a hypothetical

253 A cost-benefit decision model with a Markov model was constructed.

254 Materials and Methods A decision-analytic Markov model was developed for patients with inoperable,

255 A Markov model was developed in TreeAge Pro 2009 (TreeAge

256 A Markov model was developed to assess the cost-effectiven

257 A multi -state transition Markov model was developed to determine the cost-effecti

258 A Markov model was developed to estimate costs and quality

259 A decision-analytic Markov model was developed to evaluate the cost-effectiv

260 A Markov model was developed to project outcomes in patien

261 A Markov model was developed to simulate gating and the ef

262 A decision analysis using a Markov model was implemented to compare 2 different vasc

263 A 4-state continuous-time Markov model was implemented to identify determinants of

264 A Markov model was used to evaluate survival benefits and

265 A Markov model was used to forecast NAFLD disease progress

266 Markov modeling was used to determine the likelihood of

267 Using a Markov model we simulated patients' progression through

268 d on our weighted alignment graph and hidden Markov model, we develop a method called PyroHMMvar, whi

269 With the Markov model, we estimated that a corner light was 2.77

270 Using a Markov model, we estimated the number of life-years save

271 In a Markov model, we found FIT and colonoscopy to be more ef

272 Using a hidden Markov model, we infer the stage of the meiotic error (I

273 Using a Markov model, we simulated HCV disease in chronically in

274 g Bayesian model selection applied to hidden Markov modeling, we found that SVs oscillated between di

275 By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running ti

276 single-trial ensemble activity using hidden Markov models, we show these decision-related cortical r

277 Markov models were constructed to replicate Protocol I's

278 Three probabilistic Markov models were developed to compare bead-based assay

279 represents DNA binding sequences as a hidden Markov model which captures both position specific infor

280 ies were synthesized using a continuous-time Markov model which takes into account the competing risk

281 gnment probabilities under a continuous-time Markov model, which describes the stochastic evolution o

282 rametric extension of the traditional Hidden Markov Model, which does not require us to fix the numbe

283 A liver disease progression Markov model, which used a lifetime horizon and health c

284 otype refinement methods are based on hidden Markov models, which are accurate but computationally ex

285 e learning technique, the ensemble of hidden Markov models, which we propose here.

286 We developed a Markov model with 1-year cycle length for a cohort of 60

287 was chosen following acceptance by NICE of a Markov model with 10-letter health states in the assessm

288 By using a Markov model with a 25-year time horizon, we compared th

289 A Markov model with a latent period of 20 years and a time

290 Design, Setting, and Participants: A Markov model with a latent period of 20 years and a time

291 Analysis uses a Markov model with a lifetime horizon and a societal pers

292 The MCAST algorithm uses a hidden Markov model with a P-value-based scoring scheme to iden

293 We used a Markov model with an eight-cycle chemotherapy time horiz

294 forth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are govern

295 The decision tree included a Markov model with five states, related to the chronic st

296 A Markov model with lifetime horizon and two states, dead

297 Using multivariable Markov models with robust variance estimates, the follow

298 as transition probabilities of a stochastic Markov model, with the assumption that the amount of IL-

299 aches for haplotype inference rely on Hidden Markov Models, with the underlying assumption that the h

300 lobases (kb), making use of the eXome Hidden Markov Model (XHMM) program.