ライフサイエンスコーパス: hidden Markov model

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

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

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

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

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

6 time-series physiologic measurements using a hidden Markov model.

7 autoregulation status was attempted using a hidden Markov model.

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

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

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

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

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

13 assification and contextual analysis through Hidden Markov Models.

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

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

16 l and adaptable bioinformatics tool based on hidden Markov models.

17 of the more standard classical ones, such as 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 ngitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns.

22 d metagenome databases through complementary hidden Markov model algorithms.

23 A data-driven approach based on Hidden Markov modeling allowed us to detect event bounda

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

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

26 erved transitions were reconstructed through hidden Markov model analysis.

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

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

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

30 subfamilies, which are strongly supported by hidden Markov model and maximum likelihood molecular phy

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

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

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

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

35 ur predictions using a set of virus-specific Hidden Markov Models and demonstrated that it improves o

36 ination of convolutional neural networks and hidden Markov models and evaluated this segmentation by

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

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

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

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

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

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

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

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

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

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

47 response modeling; event segmentation using hidden Markov models; and real-time fMRI.

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

49 Hidden Markov models are used to classify sequences by d

50 Hidden Markov models are widely used to model the spatia

51 messages required to perform inference using Hidden Markov Models as generative models.

52 It combines a local Bayesian model and a Hidden Markov Model at the genome-wide level and can wor

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

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

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

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

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

58 Using the BRAVE approach and hidden Markov model-based clustering, we present 25 synt

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

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

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

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

63 Hidden Markov model-based next-generation sequence analy

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

65 the localization of repeat boundaries and a hidden Markov model-based repeat counting mechanism.

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

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

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

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

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

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

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

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

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

75 We demonstrated that clustering using a hidden Markov model can reduce a complex set of physiolo

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

98 behavior is well-described by a Hierarchical Hidden Markov Model (HHMM).

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

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

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

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

105 5mC methylation states comparing to previous hidden Markov model (HMM) based methods.

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

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

108 This Hidden Markov Model (HMM) facilitated discovery of the d

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

128 Nase-seq data and PWMs within a multivariate hidden Markov model (HMM) to detect footprint-like regio

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

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

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

132 to active fishing nets within a multivariate hidden Markov model (HMM) to quantify changes in movemen

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

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

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

136 this work, we designed and implemented a new hidden Markov model (HMM)-based ab initio gene predictio

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

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

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

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

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

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

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

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

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

146 positives than the conventional approach of hidden Markov modeling (HMM) followed by hard thresholdi

147 Hidden Markov modeling (HMM) has revolutionized kinetic

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

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

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

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

152 dom-seeding algorithm of RRS is based on the hidden-Markov-model (HMM) to offer a robust enough way f

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

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

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

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

157 Hidden Markov models (HMMs) and transition density plots

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

159 Hidden Markov models (HMMs) are powerful tools for model

160 Hidden Markov models (HMMs) are probabilistic models tha

161 used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences ab

162 We use hidden Markov models (HMMs) fitted in a Bayesian framewo

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

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

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

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

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

168 Hidden Markov models (HMMs) provide an excellent analysi

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

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

171 are package designed to fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of

172 sequence alignments and statistical models (hidden Markov models (HMMs)).

173 Although GToTree can work with any custom hidden Markov Models (HMMs), also included are 13 newly

174 Hidden Markov models (HMMs), especially those with a Poi

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

176 It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple se

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

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

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

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

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

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

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

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

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

186 We accounted for their behaviour using a Hidden Markov Model, in which recent observations are in

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

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

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

190 The Hidden Markov Model is useful for modelling transitions

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

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

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

194 The hidden Markov model library that provides sequence homol

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

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

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

198 predictive power using our model compared to Hidden Markov Model, Multi-Layer Perceptron Neural Netwo

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

200 First, by fitting a hidden Markov model of EMT with experimental data, we pr

201 nctional brain dynamics were disclosed using hidden Markov modeling of power envelope activity.

202 Using Hidden Markov modeling of two acoustic and four movement

203 This tool combines profile hidden Markov models of each smORF family and deep learn

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

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

206 Using hidden Markov models on insertion, deletion, nucleotide

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

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

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

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

211 Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly a

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

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

214 CR sequences that is based on a phylogenetic hidden Markov model (phylo-HMM).

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

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

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

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

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

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

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

222 The hidden Markov model scFv library generated multiple bind

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

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

225 1000 RNA-binding proteins are identified by hidden Markov model searches, of which mRBPs encompass a

226 ved gene discovery method based on iterative hidden Markov model searching and phylogenetic inference

227 erived from the convolutional neural network-hidden Markov model segmentation agreed with clinical es

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

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

230 We developed the Continuous-State Hidden Markov Models TF (CSHMM-TF) method which integrat

231 We also develop a Hidden Markov Model that allows visualization of distinc

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

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

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

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

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

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

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

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

240 to estimate the parameters of a hierarchical hidden Markov model, thereby enabling robust identificat

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

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

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

244 nt predictions, and furthermore integrated a hidden Markov model to constrain state dynamics based up

245 We applied a Continuous Time Hidden Markov Model to describe the probability of trans

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

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

248 AlleleHMM uses a hidden Markov model to divide the genome into three hidd

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

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

251 e repeatability of foraging trips and used a hidden Markov model to identify locations of foraging si

252 We develop a Continuous State Hidden Markov model to identify the timing and type of s

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

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

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

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

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

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

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

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

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

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

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

264 We used hidden Markov models to characterize disability states a

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

266 Rs, the first application of self-supervised hidden Markov models to discovering microsatellites.

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

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

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

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

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

272 The hidden Markov models traditionally used for haplotypes a

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

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

275 Dynamic analysis suggested that the hidden Markov model was stable over short periods of tim

276 The proposed hidden Markov model was trained and applied on a large d

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

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

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

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

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

282 Defining hidden Markov models were constructed for all.

283 Hidden Markov models were used to characterize behaviour

284 Our approach is built on a hidden Markov model where the underlying process is a tw

285 Our approach is based on a hidden Markov model where the underlying process is a Wr

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

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

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

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

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

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

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

293 tion by Genetic Algorithm (GOOGA), couples a Hidden Markov Model with a Genetic Algorithm to analyze

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

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

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

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

298 1-30 kilobases (kb), making use of the eXome Hidden Markov Model (XHMM) program.

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

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