ライフサイエンスコーパス: Markov model

1 ries physiologic measurements using a hidden Markov model.

2 gulation status was attempted using a hidden Markov model.

3 Patient-level Monte Carlo-based Markov model.

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

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

6 effects were projected by using a validated Markov model.

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

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

9 counts for any branch under a nonstationary Markov model.

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

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

12 ases on outbreaks using a two-state discrete Markov model.

13 HPV type using a time-homogenous multi-state Markov model.

14 d: a multilevel model (MLM) and a continuous Markov model.

15 cases on outbreaks using a 2-state discrete Markov model.

16 nitiation using a continuous-time multistate Markov model.

17 ernative resource allocations in a validated Markov model.

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

19 iple sequence alignment tool based on hidden Markov models.

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

21 daptable bioinformatics tool based on hidden Markov models.

22 zed using logistic regression and multistate Markov modeling.

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

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

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

26 nal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns.

27 bserve and Measure Phenotypic Plasticity), a Markov modeling algorithm that uses optimization and res

28 enome databases through complementary hidden Markov model algorithms.

29 A data-driven approach based on Hidden Markov modeling allowed us to detect event boundaries as

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

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

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

33 In a Markov model analysis, we found that starting CRC screen

34 ransitions were reconstructed through hidden Markov model analysis.

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

36 lies, which are strongly supported by hidden Markov model and maximum likelihood molecular phylogenet

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

38 We, therefore, revisited higher-order Markov models and assessed their performance in classify

39 ictions using a set of virus-specific Hidden Markov Models and demonstrated that it improves on the s

40 of convolutional neural networks and hidden Markov models and evaluated this segmentation by compari

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

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

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

44 se modeling; event segmentation using hidden Markov models; and real-time fMRI.

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

46 Hidden Markov models are used to classify sequences by determin

47 s required to perform inference using Hidden Markov Models as generative models.

48 combines a local Bayesian model and a Hidden Markov Model at the genome-wide level and can work both

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

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

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

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

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

54 Using the BRAVE approach and hidden Markov model-based clustering, we present 25 synthetic c

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

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

57 calization of repeat boundaries and a hidden Markov model-based repeat counting mechanism.

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

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

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

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

62 demonstrated that clustering using a hidden Markov model can reduce a complex set of physiologic var

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

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

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

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

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

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

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

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

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

72 A Markov model evaluated the costs (in US dollars) and eff

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

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

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

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

77 We constructed lifetime Markov models for all stages of NASH and a separate mode

78 We developed decision-analytic Markov models for separate and combined screening for PA

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

80 gh the mu-OR (MOR), we used a multi-ensemble Markov model framework combining equilibrium and non-equ

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

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

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

84 Based on the trained Markov models from both miRNA-specific and general datas

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

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

87 existing mitochondrial genomes using hidden Markov model gene profiles.

88 Higher-order Markov models have been used with caution, perhaps spari

89 Markov models have emerged as a powerful way to approxim

90 In particular, the variants of Markov models have previously been used extensively.

91 r is well-described by a Hierarchical Hidden Markov Model (HHMM).

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

93 hylation states comparing to previous hidden Markov model (HMM) based methods.

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

95 This Hidden Markov Model (HMM) facilitated discovery of the dynamic

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

97 We develop a Hidden Markov Model (HMM) framework for estimating the admixtur

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

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

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

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

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

103 predictions are superior to those of hidden Markov model (HMM) sub-compartment predictions.

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

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

106 q data and PWMs within a multivariate hidden Markov model (HMM) to detect footprint-like regions with

107 We then used a three-state hidden Markov model (HMM) to detect musth behaviour in a subset

108 ve fishing nets within a multivariate hidden Markov model (HMM) to quantify changes in movement behav

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

110 rk, we designed and implemented a new hidden Markov model (HMM)-based ab initio gene prediction tool,

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

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

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

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

115 ves than the conventional approach of hidden Markov modeling (HMM) followed by hard thresholding.

116 Hidden Markov modeling (HMM) has revolutionized kinetic studies

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

118 ding algorithm of RRS is based on the hidden-Markov-model (HMM) to offer a robust enough way for trac

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

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

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

122 Hidden Markov models (HMMs) are powerful tools for modeling pro

123 o describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about com

124 We use hidden Markov models (HMMs) fitted in a Bayesian framework and

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

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

127 kage designed to fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of symbols

128 ce alignments and statistical models (hidden Markov models (HMMs)).

129 ough GToTree can work with any custom hidden Markov Models (HMMs), also included are 13 newly generat

130 Hidden Markov models (HMMs), especially those with a Poisson de

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

132 ased on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence

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

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

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

136 accounted for their behaviour using a Hidden Markov Model, in which recent observations are integrate

137 Using a Markov model informed by in-trial costs, utilities, and

138 We used a decision analytic Markov model informed by recent multicenter, single-arm

139 HOMs, 9th order or higher) with interpolated Markov model, interpolated context model and lower-order

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

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

142 The Hidden Markov Model is useful for modelling transitions across

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

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

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

146 The hidden Markov model library that provides sequence homology to

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

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

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

150 ive power using our model compared to Hidden Markov Model, Multi-Layer Perceptron Neural Network, Sup

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

152 First, by fitting a hidden Markov model of EMT with experimental data, we propose a

153 ion tree screening model with results from a Markov model of HCV treatment outcomes.

154 We tested a simple Markov model of migraine attacks on headache diary data

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

156 We developed a Markov model of patients with wild-type or variant trans

157 Methods: A Markov model of prostate cancer onset and progression wa

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

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

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

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

162 l brain dynamics were disclosed using hidden Markov modeling of power envelope activity.

163 Markov modeling of the binary response for eGFR (greater

164 Using Hidden Markov modeling of two acoustic and four movement variab

165 This tool combines profile hidden Markov models of each smORF family and deep learning mod

166 In this modelling study, we developed Markov models of HIV and syphilis in pregnant women to e

167 r performance of higher order models such as Markov models of order one, also called adjacent dinucle

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

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

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

171 o (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignmen

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

173 ences that is based on a phylogenetic hidden Markov model (phylo-HMM).

174 The Markov model predicted no interference, whereas the quan

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 Markov modeling revealed that the cell state transitions

178 Markov modeling reveals a novel "pinched" SF configurati

179 a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for mi

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

181 e discovery method based on iterative hidden Markov model searching and phylogenetic inference.

182 from the convolutional neural network-hidden Markov model segmentation agreed with clinical estimates

183 The Markov model showed that Zollinger-Ellison-related tumor

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

185 Markov model simulated long-term outcomes among HCV-nega

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

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

188 s in passive intrinsic properties, different Markov model structures based on the properties of the t

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

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

191 We developed the Continuous-State Hidden Markov Models TF (CSHMM-TF) method which integrates prob

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

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

194 We also develop a Hidden Markov Model that allows visualization of distinct sleep

195 screening in the first year after KT using a Markov model that compared no screening with screening u

196 by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n-grams from t

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

198 Simulations were conducted using Markov models that integrated data from contemporary pop

199 We then develop a series of data-driven Markov models that isolate and identify the behavioral f

200 mate the parameters of a hierarchical hidden Markov model, thereby enabling robust identification and

201 In this study, we created a Markov model to assess the cost-effectiveness of ibrutin

202 We developed a Markov model to characterize position-wise pairing patte

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

204 to identify peak regions (P<0.01), applied a Markov model to classify regulatory elements, and annota

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

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

207 We created a Markov model to compare the overall outcomes of acceptin

208 ictions, and furthermore integrated a hidden Markov model to constrain state dynamics based upon know

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

210 We applied a Continuous Time Hidden Markov Model to describe the probability of transition f

211 from multichannel patches and used a coupled Markov model to determine the extent of signal coupling

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

213 AlleleHMM uses a hidden Markov model to divide the genome into three hidden stat

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

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

216 We constructed a decision-analytic Markov model to estimate the costs and benefits of cathe

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

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

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

220 tability of foraging trips and used a hidden Markov model to identify locations of foraging sites in

221 We develop a Continuous State Hidden Markov model to identify the timing and type of signals,

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

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

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

225 of the aorta, we developed and calibrated a Markov model to match published IA prevalence estimates.

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

227 We used a Markov model to simulate a cohort of patients presenting

228 We used a Markov Model to simulate HIV and TB coinfected patient c

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

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

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

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

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

234 We used multistate Markov modelling to estimate optimum screening intervals

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

236 first application of self-supervised hidden Markov models to discovering microsatellites.

237 We used continuous-time multistate Markov models to estimate transitions between CD4 strata

238 We used linked decision trees and Markov models to evaluate outcomes short term (cost-per-

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

240 The algorithm employs nested hidden Markov models to obtain local ancestry estimation along

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

242 Here, we used hidden Markov models to test how wild dog movements were affect

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

244 model which generalizes the theory of hidden Markov models to tree structured data.

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

246 The hidden Markov models traditionally used for haplotypes are hind

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

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

249 A Markov model was applied to estimate differences in tota

250 A Markov model was built to simulate outcomes of individua

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

252 A Markov model was constructed that followed each implant

253 A Markov model was constructed that simulated patients who

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

255 A Markov model was constructed to simulate a hypothetical

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

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

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

259 A Markov model was developed to compare the costs and effe

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

261 A Markov model was developed to estimate costs and quality

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

263 A Markov model was developed to examine the costs and heal

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

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

266 Dynamic analysis suggested that the hidden Markov model was stable over short periods of time consi

267 The proposed hidden Markov model was trained and applied on a large dataset

268 A Markov model was used to determine the incremental cost

269 A Markov model was used to estimate the potential effect o

270 An HCV natural history Markov model was used to evaluate the cost-effectiveness

271 A Markov model was used to evaluate the costs (in US dolla

272 A Markov model was used to extrapolate the effects of trea

273 A Markov model was used to forecast NAFLD disease progress

274 Markov modeling was used to determine the likelihood of

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

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

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

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

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

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

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

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

283 Hidden Markov models were used to characterize behavioural stat

284 Our approach is built on a hidden Markov model where the underlying process is a two-locus

285 Our approach is based on a hidden Markov model where the underlying process is a Wright-Fi

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

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

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

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

290 data, and progress to a discussion of Hidden Markov Models, which are of particular value in analyzin

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

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

293 We found that a Hidden Markov Model with 15 hidden states provide a good model

294 Genetic Algorithm (GOOGA), couples a Hidden Markov Model with a Genetic Algorithm to analyze data fr

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

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

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

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

299 rojected over a 15-year time horizon using a Markov model with Hodapp-Parrish-Anderson glaucoma stage

300 Unsupervised training of the hidden Markov model yielded states characterized by intracrania