ライフサイエンスコーパス: hidden Markov model

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

2 cal measurements into a DNA sequence using a Hidden Markov model.

3 mic model and alignment probabilities from a hidden Markov model.

4 experiments in a discrete-valued, bivariate hidden Markov model.

5 ent library by sampling from an Input-Output Hidden Markov Model.

6 cing data, in particular exome data, using a hidden Markov model.

7 analysis (CNV) was performed using the eXome-Hidden Markov Model.

8 assification and contextual analysis through Hidden Markov Models.

9 sal state models, equivalent in structure to hidden Markov models.

10 of the more standard classical ones, such as hidden Markov models.

11 are evolutionarily related, has been profile hidden Markov models.

12 multiple sequence alignments and as profile hidden Markov models.

13 rated using an expert curated set of profile hidden Markov models.

14 in multiple sequence alignment tool based on hidden Markov models.

15 opore data to a reference through the use of hidden Markov models.

16 redict the effect of genetic variation using hidden Markov models.

17 gos for both sequence alignments and profile hidden Markov models.

18 Mark (DMR detection based on non-homogeneous hidden Markov model), a novel Bayesian framework for det

19 e applied the Hierarchical Dirichlet Process Hidden Markov Model, a non-parametric extension of the t

20 Using Hidden Markov modeling, a method of analysis that can ma

21 s paths through the graph using an efficient hidden Markov model, allowing for recombination between

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

23 erved transitions were reconstructed through hidden Markov model analysis.

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

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

26 earning strategies (support vector machines, hidden Markov model and decision tree) and developed an

27 ad-depth-based method, GENSENG, which uses a hidden Markov model and negative binomial regression fra

28 We have developed a hidden Markov model and optimization procedure for photo

29 We have developed SoDA2, which is based on a Hidden Markov Model and used to compute the posterior pr

30 VARiD is based on a hidden Markov model and uses the forward-backward algori

31 Hidden Markov modeling and Monte Carlo simulation are po

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

33 sition-specific weight matrices, first-order Hidden Markov Models and joint probability models.

34 We concluded that hidden Markov models and random forest imputation are mo

35 ckage called MRHMMs (Multivariate Regression Hidden Markov Models and the variantS) that accommodates

36 s for understanding time-series data such as hidden Markov models and time-structure based independen

37 on-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HM

38 prediction by marrying generative learning (hidden Markov models) and discriminative learning (suppo

39 ging copy numbers for individuals based on a hidden Markov model, and identified cases and controls t

40 e90) were found by diverse gene predictions, hidden Markov models, and database search tools inferrin

41 ality metrics, multiple sequence alignments, hidden Markov models, and phylogenetic trees.

42 application of Kabat column-labeled profile Hidden Markov Models, and translated complementarity det

43 ing reading frames on raw reads, we find the hidden Markov model approach in FragGeneScan is more sen

44 Hidden Markov models are widely applied within computati

45 Hidden Markov models are widely used to model the spatia

46 We present a hidden Markov model based approach we call delta-bitscor

47 We present Pairagon, a pair hidden Markov model based cDNA-to-genome alignment progr

48 We develop a hidden Markov model based maximum-likelihood approach fo

49 Hidden Markov models based on Structural Classification

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

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

52 nown binding motifs for vertebrate TFs and a hidden Markov model-based approach to detect HCTs in the

53 we develop a traveling salesman problem and hidden Markov model-based computational method named reC

54 the CATH database and a corresponding set of Hidden Markov Model-based domain predictions.

55 Profile Hidden Markov Model-based homology search has been widel

56 tly reported a highly sensitive and accurate hidden Markov model-based method for the automatic detec

57 We, therefore, present a novel hidden Markov model-based method-Haplotype Amplification

58 Hidden Markov model-based next-generation sequence analy

59 ach to ortholog identification using subtree hidden Markov model-based placement of protein sequences

60 atabase of conserved protein domains using a Hidden Markov Model-based sequence alignment tool (HMMer

61 Information from hidden Markov model-based sequence profiles and domain a

62 Here we use sensitive Hidden Markov Model-based techniques to show that the Dn

63 mutagenesis to set constraints in HMMTOP, a hidden Markov model-based transmembrane topology predict

64 h numerical simulations and with a Bayesian (hidden Markov model-based) analysis of single particle t

65 er of TR elements is calculated using a pair-Hidden Markov Models-based method.

66 making the application of a custom-designed Hidden-Markov-Model-based motion correction algorithm us

67 By utilizing a heterogeneous hidden Markov model, BioHMM incorporates relevant biolog

68 Each Dfam entry is represented by a profile hidden Markov model, built from alignments generated usi

69 contained in sequence alignments or profile hidden Markov models by drawing a stack of letters for e

70 , Li and Durbin developed a coalescent-based hidden Markov model, called the pairwise sequentially Ma

71 l data, we show that the use of phylogenetic hidden Markov model can lead to an increase in accuracy

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

73 Hidden Markov models constructed from alignments of sequ

74 sensitive software pipeline based on profile hidden Markov models constructed from manually curated I

75 sequence alignments to evaluating matches to hidden Markov models describing protein domain families.

76 emory implementation on an extended Duration Hidden Markov Model (DHMM) and on an HMM with a spike de

77 e this limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate c

78 we developed the Functional Analysis through Hidden Markov Models (FATHMM) software and, using a mode

79 nary algorithms are employed to optimize the Hidden Markov Models, feature selection and heterogeneou

80 ), which utilizes hierarchical clustering of hidden Markov modeling-fitted single-molecule fluorescen

81 pret UVPD mass spectra, passing results to a hidden Markov model for de novo sequence prediction and

82 ases directly from sequence alignments and a hidden Markov model for detecting constrained elements c

83 The development of a hidden Markov model for genotype reconstruction in such

84 Utilization of a hidden Markov model for predictive modeling of nuclear h

85 t of as restricting to the simplest possible hidden Markov model for the underlying channel current,

86 Annotations were assigned using families of hidden Markov models for c. 11% of open reading frames;

87 Here, we used hidden Markov models for fitting the DNA sequence dynami

88 a robust bioinformatics pipeline exploiting Hidden Markov Models for the identification of nuclease

89 dress this, we coupled wavelet analysis with hidden Markov models for unbiased discovery of "domain-l

90 their scores are evaluated by using profile hidden Markov models (for protein domains) and Gaussian

91 We present a new method for inferring hidden Markov models from noisy time sequences without t

92 BOA generates profile Hidden Markov Models from the clusters of bacteriocin co

93 Gaussian filter and graphical models (i.e., Hidden Markov Model, Fully Connected Chain Model) to spe

94 notated existing mitochondrial genomes using hidden Markov model gene profiles.

95 utational approach inspired by a generalized hidden Markov model (GHMM).

96 e predictors, which are based on generalized hidden Markov models (GHMMs) and trained by maximum like

97 not known, so approximations, formulated as hidden Markov models, have been proposed in the past.

98 In this work, hierarchical hidden Markov model (HHMM) is proposed for combining dat

99 is distribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequ

100 novelty of our approach is the miRvestigator hidden Markov model (HMM) algorithm which systematically

101 behavior is introduced and analyzed through hidden Markov model (HMM) analysis of the time series of

102 It employs a hidden Markov model (HMM) and a sampling algorithm to in

103 profiles of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expressio

104 We present a novel hidden Markov model (HMM) approach to infer the IBD stat

105 Based on a Hidden Markov Model (HMM) approach, SCOPE++ accurately i

106 We propose a Hidden Markov Model (HMM) based algorithm to detect grou

107 Here we present PennCNV, a hidden Markov model (HMM) based approach, for kilobase-r

108 sidue coupling information with the pairwise Hidden Markov Model (HMM) based profile alignment method

109 nment of trusted representative sequences, a hidden Markov model (HMM) built from that alignment, cut

110 wser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs,

111 ing the function of automatic selection of a hidden Markov model (HMM) filter and also a friendly use

112 In this article, we present a hidden Markov model (HMM) for ibd among a set of chromos

113 A hidden Markov model (HMM) formulation of the sequentiall

114 The profile hidden Markov model (HMM) framework enables the construc

115 egions are incorporated into a probabilistic hidden Markov model (HMM) framework which is used to ann

116 Methodology: In particular, we use a hidden Markov model (HMM) framework, which is capable of

117 The hidden Markov model (HMM) has been a workhorse of single

118 novo CNVs in case-parent trios is the joint hidden Markov model (HMM) implemented in the PennCNV sof

119 Profile Hidden Markov Model (HMM) is a powerful statistical mode

120 homology search based on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alig

121 ides predicted binding sites using NR-HMM, a Hidden Markov Model (HMM) model.

122 or that are siblings of the donors, using a hidden Markov model (HMM) of identity-by-descent (IBD) s

123 o model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assu

124 f highly informative copying states within a Hidden Markov Model (HMM) phasing algorithm.

125 A collection of hidden Markov model (HMM) profiles was used to identify

126 The hidden Markov model (HMM) search tools now use HMMER3, d

127 likelihood-based method using an interleaved hidden Markov model (HMM) that can jointly estimate the

128 ently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene predicti

129 omatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the com

130 applied a machine-learning approach using a hidden Markov model (HMM) to capture the complex and oft

131 We employ a Hidden Markov Model (HMM) to cluster the haplotypes into

132 We developed a novel hidden Markov model (HMM) to computationally map the gen

133 us implementation of VIPR by incorporating a hidden Markov model (HMM) to detect recombinant genomes.

134 is (PCA) to normalize exome read depth and a hidden Markov model (HMM) to discover exon-resolution CN

135 Here, we generalize a hidden Markov model (HMM) to infer changes in phylogeny

136 phylogenetic footprinting techniques with an hidden Markov model (HMM) transducer-based multiple alig

137 ata from transposon mutant libraries using a Hidden Markov Model (HMM), along with formulas to adapt

138 enotypes of SNPs, identifies rare CNVs via a hidden Markov model (HMM), and generates an integrated s

139 a sequence motif, which is represented by a hidden Markov model (HMM), from the cluster of peptides

140 A hidden Markov model (HMM), however, was more effective a

141 A Hidden Markov Model (HMM), trained on CLIP-seq data, was

142 Each family is based on a hidden Markov model (HMM), where both cutoff scores and

143 rid of a dynamic programming algorithm and a Hidden Markov Model (HMM), which is shown to be optimal.

144 Here we describe a hidden Markov model (HMM)-based algorithm mCarts to pred

145 replication on sequencing output and using a hidden Markov model (HMM)-based filter to exploit hereto

146 Conventional hidden Markov model (HMM)-based gene-finding algorithms

147 Using a combination of profile Hidden Markov Model (HMM)-based homology searches, netwo

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

149 Error CorrEction in Rna-seq data (SEECER), a hidden Markov Model (HMM)-based method, which is the fir

150 from thermal fluctuation data, we develop a hidden Markov model (HMM)-based method.

151 A number of hidden Markov model (HMM)-based methods have been develo

152 osition in the genome to detect CNVs using a hidden Markov model (HMM).

153 set to estimate parameters of the underlying hidden Markov model (HMM).

154 rforms the conventional methods based on the hidden Markov model (HMM).

155 a multiple sequence alignment and a profile hidden Markov model (HMM).

156 Hidden Markov modeling (HMM) has revolutionized kinetic

157 We have previously used Hidden Markov Models (HMM) to analyze SNP array data for

158 We used multi-state hidden Markov models (HMM) to characterize states of div

159 Existing hidden Markov models (HMM)-based imputation approaches r

160 ost important sequence analysis applications-hidden Markov models (HMM).

161 ranscription units de novo using a two state hidden-Markov model (HMM).

162 , CYPED; cytochrome P450 monooxygenase, CYP; Hidden Markov Model, HMM.

163 We develop a visual editor for profile Hidden Markov Models (HMMEditor).

164 Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and m

165 Hidden Markov models (HMMs) and generalized HMMs been su

166 This is based on hidden Markov models (HMMs) and is available together wi

167 d approaches to admixture have been based on hidden Markov models (HMMs) and Markov hidden Markov mod

168 to a more complex advanced method employing hidden Markov models (HMMs) and secondary structure (SS)

169 behavior in live cell imaging data based on hidden Markov models (HMMs) and showed that it can deter

170 Hidden Markov models (HMMs) and transition density plots

171 of the InterPro Consortium, and the PANTHER hidden markov Models (HMMs) are distributed as part of I

172 Hidden Markov models (HMMs) are flexible and widely used

173 Hidden Markov models (HMMs) are probabilistic models tha

174 While profile hidden Markov models (HMMs) are successful and powerful

175 We present a method that utilizes hidden Markov models (HMMs) for the classification task.

176 This was accomplished using hidden Markov models (HMMs) generated from experimentall

177 ows-based, heuristic models and finite-state hidden Markov models (HMMs) have been used with some suc

178 The flexibility in gap cost enjoyed by hidden Markov models (HMMs) is expected to afford them b

179 Although hidden Markov models (HMMs) may be used to infer the con

180 we have developed robust algorithms based on hidden Markov models (HMMs) of motor proteins.

181 one program for scoring and aligning profile hidden Markov models (HMMs) of protein families.

182 We trained Hidden Markov models (HMMs) on the histone modification

183 babilistic models are mostly based on either hidden Markov models (HMMs) or transducer theories, both

184 Hidden Markov models (HMMs) provide an alternative theor

185 Hidden Markov models (HMMs) provide an excellent analysi

186 The Baum-Welch learning procedure for Hidden Markov Models (HMMs) provides a powerful tool for

187 ided in building a collection of 68 TIGRFAMs hidden Markov models (HMMs) that define nonoverlapping a

188 oNet-HMM combines phylogenetic networks with hidden Markov models (HMMs) to simultaneously capture th

189 lignment quality by using pairwise alignment hidden Markov models (HMMs) with multiple match states t

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

191 equence analysis and modeling tools, such as hidden Markov models (HMMs), which can be used to search

192 ant to environmental microorganisms based on Hidden Markov Models (HMMs).

193 ethylation in MCF7 and MCF7-T cells to train hidden Markov models (HMMs).

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

195 are evolutionarily related has been profile hidden Markov models (HMMs).

196 NA genes from metagenomic fragments based on hidden Markov models (HMMs).

197 inear block dependency in the SNP data using hidden Markov models (HMMs).

198 egmentation of continuous genomic data using hidden Markov models (HMMs).

199 a novel framework for DMR detection based on hidden Markov models (HMMs).

200 One such promising method, the infinite hidden Markov model (iHMM), generalizes the HMM that its

201 A hidden Markov model in combination with a position-speci

202 I propose a new application of profile Hidden Markov Models in the area of SNP discovery from r

203 rithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature spa

204 in interaction (DDI), an interaction profile hidden Markov model (ipHMM) is first built for the domai

205 In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate depend

206 A hidden Markov model is then employed to estimate the num

207 The deployment of multiple hidden Markov models is proposed to computationally clas

208 The repertoire of profile hidden Markov model libraries, which are used for annota

209 A new hidden Markov model library based on SCOP 1.75 has been

210 The hidden Markov model library that provides sequence homol

211 A hidden Markov model library, constructed from the manual

212 d were as follows: multivariate mixed model, hidden Markov model, logical algorithm, k-nearest neighb

213 Here, we test the ability of the profile hidden Markov model method HMMER3 to correctly assign ho

214 e distributed implementation of a Mixture of Hidden Markov Models (MHMMs) to the problem of channel c

215 ed on hidden Markov models (HMMs) and Markov hidden Markov models (MHMMs).

216 for clonal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell rec

217 genomes, we conducted database searching by hidden Markov models, multiple sequence alignment, and p

218 SNP genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM).

219 nd defined their behavioural phenotype using hidden Markov models of their movement and body temperat

220 Together, these hidden Markov models offer a powerful approach for deali

221 Using hidden Markov models on insertion, deletion, nucleotide

222 Our model consists of a pair of hidden Markov models--one for the germline and one for t

223 e haplotypes and collected genotypes using a Hidden Markov Model or assemble haplotypes by overlappin

224 sequence alignments and statistical models (hidden Markov models or HMMs).

225 oes not use machine learning methods such as hidden Markov models or neural networks; instead, transF

226 3, the latest version of the popular profile hidden Markov model package.

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

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

229 in families are often represented by profile hidden Markov models (pHMMs).

230 Hidden Markov model profile-to-profile searches in prote

231 It is now feasible to make efficient profile hidden Markov model (profile HMM) searches via the web.

232 uence alignment, including BLAST and profile hidden Markov models (profile HMMs), are not based on an

233 putation, based on probabilistic matching to hidden Markov model representations of the reference dat

234 ed TFs must contain a significant match to a hidden Markov model representing a sequence-specific DNA

235 t fall into known superfamilies: hundreds of hidden Markov models representing coiled-coil-containing

236 Photon distribution and hidden Markov modeling revealed fast dynamic and slow co

237 aneous alignment and tree construction using hidden Markov models (SATCHMO-JS) web server for simulta

238 The hidden Markov model scFv library generated multiple bind

239 of ortholog identification based on subtree hidden Markov model scoring.

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

241 1000 RNA-binding proteins are identified by hidden Markov model searches, of which mRBPs encompass a

242 s, followed by analysis based on a two-state hidden Markov model, taking advantage of the availabilit

243 A hidden-Markov model test for selection also found widesp

244 Our method builds a hidden Markov model that derives the ancestry probabilit

245 , such as gating/desensitization, by using a hidden Markov model that describes the complete channel

246 Specifically, we extend a hidden Markov model that is widely used to describe hapl

247 the reference using our previously proposed hidden Markov model that models homopolymer errors and t

248 iological knowledge with the non-homogeneous hidden Markov model that models spatial correlation.

249 erified a novel Bayesian approach based on a hidden Markov model that predicts how the C4 phenotype e

250 rate secondary structure prediction; (iii) a hidden Markov model that uses a novel combined scoring o

251 optimizes for protein binding by utilizing a hidden Markov model that was trained on all antibody-ant

252 ansducers are probabilistic tools similar to Hidden Markov Models that can be systematically extended

253 Finally, we present a set of hidden Markov models that can reliably place most new ki

254 m by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal

255 population haplotypes, we employ an infinite hidden Markov model to characterize each ancestral popul

256 eta variables to capture the variances and a Hidden Markov model to characterize the reads dependency

257 Using a time-dependent hidden Markov model to combine evidence of copy number v

258 We used the hidden Markov model to describe the spatiotemporal patte

259 ally methylated DNA, we were able to train a hidden Markov model to distinguish 5-mC from unmethylate

260 We develop a Hidden Markov Model to estimate ancestry at all genomic

261 Further, we apply a hidden Markov model to identify copy-neutral LOH (loss o

262 equencing error models and codon usages in a hidden Markov model to improve the prediction of protein

263 In our previous work, we developed a new hidden Markov model to incorporate a two-site joint emis

264 a software application, AD-LIBS, that uses a hidden Markov model to infer ancestry across hybrid geno

265 Finally, our method uses a hidden Markov model to integrate multiple sources of inf

266 ven under highly diffusive motion by using a hidden Markov model to jointly analyze multiple stochast

267 orporated the family relationship and used a hidden Markov model to jointly infer CNVs for three samp

268 Using a hidden Markov model to map placental PMDs genome-wide an

269 ast to prior approaches, we have developed a hidden Markov model to narrowly define the mutation area

270 We have also constructed a hidden Markov model to represent the signature domain of

271 Here, we use a multivariate Hidden Markov Model to reveal 'chromatin states' in huma

272 We applied Bayesian model selection to hidden Markov modeling to infer transient transport stat

273 We used hidden Markov models to characterize disability states a

274 Here, we used hydrophobicity profiles and hidden Markov models to define a structural repeat commo

275 CEGMA includes the use of profile-hidden Markov models to ensure the reliability of the ge

276 istic modeling method that uses input-output hidden Markov models to reconstruct dynamic regulatory n

277 ies probabilistic inference methods based on hidden Markov models to the problem of homology search.

278 The hidden Markov models traditionally used for haplotypes a

279 Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temp

280 An analysis of all trajectories by a hidden Markov model was consistent with two diffusion st

281 Based on our weighted alignment graph and hidden Markov model, we develop a method called PyroHMMv

282 Using a structurally constrained hidden Markov model, we discovered an orthopoxvirus prot

283 Using a hidden Markov model, we infer the stage of the meiotic e

284 Using Bayesian model selection applied to hidden Markov modeling, we found that SVs oscillated bet

285 sing single-molecule fluorescence assays and hidden Markov modeling, we show the most direct evidence

286 By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced run

287 alyzing single-trial ensemble activity using hidden Markov models, we show these decision-related cor

288 Defining hidden Markov models were constructed for all.

289 Hidden Markov models were used to search the genome data

290 method represents DNA binding sequences as a hidden Markov model which captures both position specifi

291 non-parametric extension of the traditional Hidden Markov Model, which does not require us to fix th

292 in the software polyHap v2.0, is based on a hidden Markov model, which models the joint haplotype st

293 ost genotype refinement methods are based on hidden Markov models, which are accurate but computation

294 machine learning technique, the ensemble of hidden Markov models, which we propose here.

295 The method implements a factorial hidden Markov model with a non-linear likelihood to repr

296 The MCAST algorithm uses a hidden Markov model with a P-value-based scoring scheme

297 (henceforth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are

298 g approaches for haplotype inference rely on Hidden Markov Models, with the underlying assumption tha

299 1-30 kilobases (kb), making use of the eXome Hidden Markov Model (XHMM) program.

300 We present a statistical tool (exome hidden Markov model [XHMM]) that uses principal-componen