ライフサイエンスコーパス: HMM

1 HMM analyses of movie sequences of living cells reveal t

2 HMMs are trained from the input genes and a group of ran

3 HMMs have been shown to exhibit exotic optical propertie

4 sizes of both M10(Full)LZ and BAP-M10(1-979)HMM are widely distributed on single actin filaments tha

5 artificial dimerization motif (BAP-M10(1-979)HMM).

6 oth surface and bulk plasmon polaritons in a HMM through a grating coupling technique of surface plas

7 controlled by the relative number of active HMM per total motors, rather than their absolute surface

8 In addition, HMM-ASE exploits ASE information to further improve geno

9 diffraction grating coupled with an Ag/Al2O3 HMM shows 18-fold spontaneous emission decay rate enhanc

10 complexity of the progressive all-versus-all HMM-HMM scoring and alignment.

11 y gene in the microarray is scored using all HMMs and significant matches with the input genes are re

12 by realizing an epitaxial superlattice as an HMM.

13 We describe an HMM-based approach using belief propagations (kmerHMM),

14 erence given a simple text description of an HMM.

15 osition-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or H

16 Furthermore, we now provide an HMM-based sequence search that places a user-provided pr

17 -mers are ranked and aligned for training an HMM as the underlying motif representation.

18 , including multiple sequence alignments and HMM profiles for each VOG.

19 two hidden Markov models (HMMs), HMM-ASE and HMM-NASE that consider or do not consider ASE, respectiv

20 improvement over conventional Psi-blast and HMM profile based methods in sequence matching.

21 tructure of metallic diffraction grating and HMM.

22 The manner in which LMMA (and HMM) DD-peptidases achieve substrate specificity, both i

23 rofile hidden Markov model (HMM) methods and HMM-banded CM alignment methods.

24 teins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively

25 Taxonomy and HMM databases can be downloaded from http://bioltfws1.yo

26 All datasets and HMMs are available at: http://bcb.cs.tufts.edu/pairwise/

27 We then applied HMM-DB to elucidate kinetics of regulated SNARE zipperin

28 tin at the same speed in a motility assay as HMM-2P.

29 nd to develop new inhibitors for HMM PBPs as HMM PBP targeted antibacterial agents.

30 ree approaches with the default or available HMMs.

31 d AUGUSTUS) using their respective available HMMs.

32 HMM method that satisfies detailed balance (HMM-DB) and optimizes model parameters by gradient searc

33 PDOS enhancement than gold- or silver-based HMMs.

34 We further extend the basic HMM to incorporate population linkage disequilibrium (LD

35 ser to select the target search space before HMM-based comparison steps and to easily organize the re

36 Both HMMs have the advantages of calling the genotypes of sev

37 more accurate and reliable model fitting by HMM-DB.

38 forms results obtained for the corresponding HMM profiles generated for each topology.

39 path persistence plateaued above a critical HMM surface density, and at high (micromolar) actin fila

40 amilies are still curated and used to define HMMs, but gene ontology functional annotations can now b

41 ithm, variable-stepsize integrating-detector HMM (VSI-HMM) better models the data-acquisition process

42 EBSeq-HMM may also be used for inference regarding isoform exp

43 Bayes mixture modeling approach called EBSeq-HMM.

44 In EBSeq-HMM, an auto-regressive hidden Markov model is implement

45 ithmic improvements for performing the exact HMM computation is introduced here, by exploiting the pa

46 MRHMMs supplements existing HMM software packages in two aspects.

47 Finally, we extended HMM-DB to correct the baseline drift in single-molecule

48 e presence of high-k modes in the fabricated HMM.

49 ecently proposed method based on a factorial HMM.

50 Finally, HMM-GRASPx was used to reconstruct comprehensive sets of

51 tes as required for the traditional (finite) HMM.

52 s] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary

53 is has impeded efforts to develop assays for HMM PBPs and to develop new inhibitors for HMM PBPs as H

54 ich per-allele categorical distributions for HMM transition probabilities and per-allele-per-position

55 r HMM PBPs and to develop new inhibitors for HMM PBPs as HMM PBP targeted antibacterial agents.

56 e the expectation-maximization algorithm for HMMs to adjust parameters for each data set.

57 ntly developed empirical Bayesian method for HMMs can be extended to enable a more automated and stat

58 ecular mass protease-resistant PrP fragment (HMM PrPres), distinct from L-BSE in QQ171 sheep.

59 nce protein sequences and thus, differs from HMM profiles and related methods.

60 However, results from HMM often violate detailed balance when applied to the t

61 grating coupled-hyperbolic metamaterials (GC-HMM) as multiband perfect absorber that can offer extrem

62 The fabricated GC-HMMs exhibit several highly desirable features for techn

63 gnment, thus providing a sort of generalized HMM.

64 high actin filament concentration, and high HMM surface densities might decrease alignment probabili

65 We propose two hidden Markov models (HMMs), HMM-ASE and HMM-NASE that consider or do not consider AS

66 We revealed how HMM-DB could be conveniently used to derive a simplified

67 s well as strong plasmon-exciton coupling in HMM via diffracting grating.

68 In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarize

69 hat were generated from healthy individuals, HMM-GRASPx accurately estimates the abundances of the an

70 method called hierarchically linked infinite HMM (hiHMM) to jointly infer chromatin state maps in mul

71 across the field of biophysics: the infinite HMM (iHMM).

72 We introduce HMMs, which are able to exploit LD and account for the A

73 C0 and another alternatively spliced isoform HMM II-C1, which contains 8 amino acids inserted into lo

74 ever, the computational demands of the joint HMM are substantial and the extent to which false positi

75 fold increase in speed relative to the joint HMM in a study of oral cleft trios.

76 ur algorithm compares favorably to the joint HMM with MinimumDistance being much faster.

77 false positive identifications in the joint HMM, despite a wave-correction implementation in PennCNV

78 gnition as compared with HMMER (a well-known HMM method) and a mean 33% (median 19%) improvement as c

79 experiments are summarized by a higher level HMM.

80 This method (henceforth denoted by m-HMM) is based on a hidden Markov model with emission pro

81 The new m-HMM method is a powerful and practical approach for iden

82 Furthermore, application of our m-HMM to DNA sequencing data from the two maize inbred lin

83 ation study demonstrates that our proposed m-HMM approach has greater power for detecting copy number

84 on of ZnO NWs in Human Monocyte Macrophages (HMMs).

85 ed into two groups, the high-molecular mass (HMM) and low-molecular mass (LMM) enzymes.

86 properties of an expressed heavy meromyosin (HMM) construct with only one of its RLCs phosphorylated

87 aculovirus-expressed mouse heavy meromyosin (HMM) II-C2 demonstrates no requirement for regulatory my

88 single- and double-headed heavy meromyosin (HMM) were each ~6 nm.

89 ed gliding over a skeletal heavy meromyosin (HMM)-coated surface.

90 , the flexural rigidity of heavy meromyosin (HMM)-propelled actin filaments is similar (without phall

91 he adsorbed motor protein (heavy meromyosin, HMM) using quartz crystal microbalance; and motor bioact

92 Hyperbolic metamaterial (HMM), a sub-wavelength periodic artificial structure wit

93 Hyperbolic metamaterials (HMMs) represent a novel class of fascinating anisotropic

94 devices including hyperbolic metamaterials (HMMs).

95 ch is the miRvestigator hidden Markov model (HMM) algorithm which systematically computes a similarit

96 divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patterns to determi

97 We present a novel hidden Markov model (HMM) approach to infer the IBD status in a pedigree with

98 Based on a Hidden Markov Model (HMM) approach, SCOPE++ accurately identifies specific ho

99 We propose a Hidden Markov Model (HMM) based algorithm to detect groups of genes functiona

100 ation with the pairwise Hidden Markov Model (HMM) based profile alignment method to improve profile-p

101 esentative sequences, a hidden Markov model (HMM) built from that alignment, cutoff scores that let a

102 utomatic selection of a hidden Markov model (HMM) filter and also a friendly user interface for selec

103 s article, we present a hidden Markov model (HMM) for ibd among a set of chromosomes and describe met

104 A hidden Markov model (HMM) formulation of the sequentially Markov CSD is devel

105 The profile hidden Markov model (HMM) framework enables the construction of very useful p

106 The hidden Markov model (HMM) has been a workhorse of single-molecule data analys

107 rent trios is the joint hidden Markov model (HMM) implemented in the PennCNV software.

108 on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alignment methods.

109 g sites using NR-HMM, a Hidden Markov Model (HMM) model.

110 of the donors, using a hidden Markov model (HMM) of identity-by-descent (IBD) states along the genom

111 rom state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations oc

112 copying states within a Hidden Markov Model (HMM) phasing algorithm.

113 A collection of hidden Markov model (HMM) profiles was used to identify putative hrp boxes in

114 The hidden Markov model (HMM) search tools now use HMMER3, dramatically reducing

115 od using an interleaved hidden Markov model (HMM) that can jointly estimate the aforementioned three

116 finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms

117 es using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence o

118 We developed a novel hidden Markov model (HMM) to computationally map the genomic locations of PMD

119 VIPR by incorporating a hidden Markov model (HMM) to detect recombinant genomes.

120 exome read depth and a hidden Markov model (HMM) to discover exon-resolution CNV and genotype variat

121 utant libraries using a Hidden Markov Model (HMM), along with formulas to adapt the parameters of the

122 A Hidden Markov Model (HMM), trained on CLIP-seq data, was used to score probab

123 ramming algorithm and a Hidden Markov Model (HMM), which is shown to be optimal.

124 Here we describe a hidden Markov model (HMM)-based algorithm mCarts to predict clustered functio

125 cing output and using a hidden Markov model (HMM)-based filter to exploit heretofore unappreciated in

126 combination of profile Hidden Markov Model (HMM)-based homology searches, network analysis and struc

127 cle, we introduce a new Hidden Markov Model (HMM)-based method that can take into account jumping rea

128 na-seq data (SEECER), a hidden Markov Model (HMM)-based method, which is the first to successfully ad

129 tion data, we develop a hidden Markov model (HMM)-based method.

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

131 to detect CNVs using a hidden Markov model (HMM).

132 alignment and a profile hidden Markov model (HMM).

133 novo using a two state hidden-Markov model (HMM).

134 Hidden Markov modeling (HMM) has revolutionized kinetic studies of macromolecule

135 We used multi-state hidden Markov models (HMM) to characterize states of diving behaviour and the

136 Existing hidden Markov models (HMM)-based imputation approaches require individual-leve

137 analysis applications-hidden Markov models (HMM).

138 ew algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using

139 This is based on hidden Markov models (HMMs) and is available together with a cognate database

140 anced method employing hidden Markov models (HMMs) and secondary structure (SS) prediction.

141 imaging data based on hidden Markov models (HMMs) and showed that it can determine the number and ev

142 Hidden Markov models (HMMs) and transition density plots (TDPs) are used to ch

143 Hidden Markov models (HMMs) are flexible and widely used in scientific studies

144 Hidden Markov models (HMMs) are probabilistic models that are well-suited to s

145 was accomplished using hidden Markov models (HMMs) generated from experimentally validated Pax6 bindi

146 odels and finite-state hidden Markov models (HMMs) have been used with some success in analyzing ChIP

147 Although hidden Markov models (HMMs) may be used to infer the conformational trajectori

148 st algorithms based on hidden Markov models (HMMs) of motor proteins.

149 mostly based on either hidden Markov models (HMMs) or transducer theories, both of which give the ind

150 Hidden Markov models (HMMs) provide an excellent analysis of recordings with v

151 lection of 68 TIGRFAMs hidden Markov models (HMMs) that define nonoverlapping and functionally distin

152 ogenetic networks with hidden Markov models (HMMs) to simultaneously capture the (potentially reticul

153 We propose two hidden Markov models (HMMs), HMM-ASE and HMM-NASE that consider or do not cons

154 DMR detection based on hidden Markov models (HMMs).

155 icroorganisms based on Hidden Markov Models (HMMs).

156 MCF7-T cells to train hidden Markov models (HMMs).

157 used CSDs are based on hidden Markov models (HMMs).

158 lated has been profile hidden Markov models (HMMs).

159 mic fragments based on hidden Markov models (HMMs).

160 in the SNP data using hidden Markov models (HMMs).

161 based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor sequences.

162 erefore, single-phosphorylated smooth muscle HMM molecules are active species, and the head associate

163 kinetics demonstrated that a dimer of Myo5c-HMM (double-headed heavy meromyosin 5c) has a 6-fold low

164 t-1), indicating that the two heads of Myo5c-HMM increase F-actin-binding affinity.

165 sion tracking analyses showed that two Myo5c-HMM dimers linked with each other via a DNA scaffold and

166 ase activity of fully phosphorylated myosin (HMM-2P) and to move actin at the same speed in a motilit

167 Actin-Tpm4.2 excluded both myoVa-HMM and full-length myoVa-Mlph from productive interacti

168 tter tracks compared to bare actin for myoVa-HMM based on event frequency, run length, and speed.

169 sensitive, N-ethylmaleimide-treated HMM (NEM-HMM; 25-30%).

170 In particular, the addition of NEM-HMM increased a non-Gaussian tail in the path curvature

171 We present a new HMM implementation for obtaining the chemical-kinetic mo

172 Here, to our knowledge, we developed a new HMM method that satisfies detailed balance (HMM-DB) and

173 ule level were observed in the case of NMIIB-HMM in optical tweezers or TIRF/in vitro motility experi

174 In contrast, noninserted HMM II-C0 and another alternatively spliced isoform HMM

175 ce annotation software package using a novel HMM "factorization" strategy.

176 NR-HMM and all results can be downloaded for free at the we

177 so provides predicted binding sites using NR-HMM, a Hidden Markov Model (HMM) model.

178 ry step size and attachment time to actin of HMM-1P is indistinguishable from that of HMM-2P.

179 Our study illustrates the applicability of HMM-based analysis of genome-wide high-throughput genomi

180 can generate approximately half the force of HMM-2P, which may relate to the observed duty ratio of H

181 efficient, versatile, and reliable method of HMM for kinetics studies of macromolecules under thermod

182 erial translates in a higher total number of HMM molecules per unit area, but also in a lower uptake

183 ies have validated the proof-of-principle of HMM for cellular imaging and provided direct evidence fo

184 eriments were performed over a wide range of HMM surface density and actin filament bulk concentratio

185 ich may relate to the observed duty ratio of HMM-1P being approximately half that of HMM-2P.

186 o of HMM-1P being approximately half that of HMM-2P.

187 of HMM-1P is indistinguishable from that of HMM-2P.

188 lightly lower (with phalloidin) than that of HMM-free filaments observed in solution without surface

189 show that we can improve the performance of HMMs in this domain by using a simple simulated model of

190 the extent to which the exotic properties of HMMs can be realized has been seriously limited by fabri

191 efficiently learns hundreds of thousands of HMMs and uses these to correct sequencing errors.

192 the variantS) that accommodates a variety of HMMs that can be flexibly applied to many biological stu

193 demonstrate that a scoring strategy based on HMM profiles can achieve good performance in identifying

194 functional prediction method HMMvar based on HMM profiles, which capture the conservation information

195 a position specific scoring model (a PSSM or HMM) that captures the pattern of sequence conservation

196 n Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection.

197 statistical models (hidden Markov models or HMMs).

198 al motif recognition as compared to ordinary HMMs.

199 cessible to the community by integrating our HMM algorithm with a proven algorithm for de novo discov

200 LD on the sensitivity and specificity of our HMM model in estimating segments of ibd among sets of fo

201 We trained our HMM on a set of non-recombinant parental viruses and app

202 ugmented with simulated evolution outperform HMMs trained only on real data.

203 mations that use finite-state machines (Pair HMMs and transducers) currently represent the most pract

204 tionary model compatible with symmetric pair HMMs that are the basis for Smith-Waterman pairwise alig

205 ct with only one of its RLCs phosphorylated (HMM-1P).

206 PhyloNet-HMM combines phylogenetic networks with hidden Markov mo

207 In this work, we report on PhyloNet-HMM-a new comparative genomic framework for detecting in

208 rm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment acc

209 novel sequences to OGs based on precomputed HMM profiles.

210 egrates banded Viterbi algorithm for profile HMM parsing with an iterative simultaneous alignment and

211 The algorithm searches a given profile HMM of a protein family against a database of fragmentar

212 ficient profile hidden Markov model (profile HMM) searches via the web.

213 ltiple protein sequence alignment or profile HMM against a target sequence database, and for searchin

214 vement in AUC over HHPred (a profile-profile HMM method), despite HHpred's use of extensive additiona

215 Seed alignments, profile HMMs, hit lists and other underlying data are available

216 ST and profile hidden Markov models (profile HMMs), are not based on any explicitly time-dependent ev

217 ins are predicted using a library of profile HMMs from 2738 CATH superfamilies.

218 ins are predicted using a library of profile HMMs representing 2737 CATH superfamilies.

219 ed using a library of representative profile HMMs derived from CATH superfamilies.

220 The resulting program, HMM-GRASPx, demonstrates superior performance in alignin

221 racy of the new CSD over previously proposed HMM-based CSDs increases substantially with the number o

222 -molecular-mass penicillin-binding proteins (HMM PBPs) are essential for bacterial cell wall biosynth

223 se alignments between the query model (PSSM, HMM) and the subject sequences in the library.

224 When purified, HMM PBPs give undetectable or weak enzyme activity.

225 However, even when purified, HMM PBPs retain their ability to bind beta-lactams.

226 ed partis, is built on a new general-purpose HMM compiler that can perform efficient inference given

227 erfaces which are essential for high-quality HMM devices.

228 om the common implementation of the relevant HMM techniques remain intractable for large genomic data

229 wed after stronger assignments of the repeat HMM.

230 nt of dye molecules with respect to the same HMM without grating.

231 verlaps was, thus, applied first to sequence/HMM alignments, then HMM-HMM alignments and then structu

232 These CAZyme family-specific HMMs are our key contribution and the foundation for the

233 seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studie

234 Seqping generates species-specific HMMs that are able to offer unbiased gene predictions.

235 short trajectories compared to the standard HMM based on an expectation-maximization algorithm, lead

236 MM was best, which was reduced to a 14-state HMM with a Bayesian information criterion score of 1.212

237 ation criterion to determine that a 20-state HMM was best, which was reduced to a 14-state HMM with a

238 We evaluate the performance of a 4-state HMM on a sequence dataset of M. tuberculosis transposon

239 A 4-state HMM provides an improved way of analyzing Tn-seq data an

240 kes use of recent advances in infinite-state HMMs by obtaining explicit formulas for posterior means

241 he basic algorithm, called variable-stepsize HMM (VS-HMM), was introduced in the previous article.

242 reaction cycle called the variable-stepsize HMM in which the quantized position variable is represen

243 y reveals that such a TiN-based superlattice HMM provides a higher PDOS enhancement than gold- or sil

244 rkers for multicomponent biological systems; HMMs defining their partner proteins also were construct

245 und decision-theoretic framework for testing HMM-dependent hypotheses is developed.

246 is present, the HMM-ASE had a lower FNR than HMM-NASE, while both can control the false discovery rat

247 detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison.

248 ted and measured trajectories, we found that HMM-DB significantly reduced overfitting of short trajec

249 Conclusion: We propose that HMM-based approach can be exploited in a wide avenue of

250 ty measurements on small ensembles show that HMM-1P can generate approximately half the force of HMM-

251 Our findings suggest that HMM-based approaches will enhance extraction of biologic

252 We show that HMMs trained with our pairwise model of simulated evolut

253 different remote protein homolog tasks, that HMMs whose training is augmented with simulated evolutio

254 The HMM can smooth variations in read abundance and thereby

255 The HMM identified a four-state model as the best descriptor

256 The HMM method represents the bond state by a hidden variabl

257 The iHMM goes beyond the HMM by self-consistently learning all parameters learned

258 The kinetic parameters estimated by the HMM are in excellent agreement with those by a descripti

259 ime and waiting time events estimated by the HMM are much more than those estimated by a descriptive

260 ently learning all parameters learned by the HMM in addition to learning the number of states without

261 Our method extends the HMM in Thunder and explicitly models jumping reads infor

262 Multiple motifs are then extracted from the HMM using belief propagations.

263 hidden Markov model (iHMM), generalizes the HMM that itself has become a workhorse in single molecul

264 ndencies have been partially captured in the HMM setting by simulated evolution in the training phase

265 uction to an important generalization of the HMM, which is poised to have a deep impact across the fi

266 rray in order to evaluate performance of the HMM.

267 y derive the transition probabilities of the HMM.

268 When ASE is present, the HMM-ASE had a lower FNR than HMM-NASE, while both can co

269 We also test the HMM on several synthetic datasets representing different

270 We show that the HMM produces results that are highly correlated with pre

271 human oral microbiome MT datasets, using the HMM-GRASPx estimated transcript abundances significantly

272 ed functions into multiple points within the HMM framework.

273 Simulation results indicate that the HMMs proposed demonstrate a very good prediction accurac

274 plied first to sequence/HMM alignments, then HMM-HMM alignments and then structure alignments, taking

275 This HMM uses both the density of sequence reads mapped to th

276 te better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) usi

277 es how computed evidence, including TIGRFAMs HMM results, should be used to judge whether an enzymati

278 that our method performs as well as TileMap HMM and BAC for the high-resolution data from Affymetrix

279 hree closely related methods, namely TileMap HMM, tileHMM and BAC.

280 r plate-based assay for inhibitor binding to HMM PBPs based on competition with biotin-ampicillin con

281 Compared to HMM that can only model very short-range residue correla

282 water, and a lower ratio of active per total HMM molecules per unit area.

283 C++ library that can implement a traditional HMM from a simple text file.

284 on pipeline, Seqping that uses self-training HMM models and transcriptomic data.

285 th ATP-insensitive, N-ethylmaleimide-treated HMM (NEM-HMM; 25-30%).

286 Nevertheless and unexpectedly, HMM PrPres was found in the spleen of ovine PrP transgen

287 By using HMM-HMM alignment as the sequence similarity metric, CMs

288 VB-HMM further revealed immature dynamic interactions betwe

289 Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of inter

290 In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized b

291 VIPR HMM correctly identified 95% of the 62 inter-species rec

292 VIPR HMM is freely available for academic use and can be down

293 algorithm, called variable-stepsize HMM (VS-HMM), was introduced in the previous article.

294 In this article, we extend the VS-HMM framework for better performance with experimental d

295 iable-stepsize integrating-detector HMM (VSI-HMM) better models the data-acquisition process, and acc

296 The fidelity of the VSI-HMM is tested with simulations and is applied to in vitr

297 When used as a blind step detector, the VSI-HMM outperforms conventional step detectors.

298 qualitatively different from that seen when HMM was mixed with ATP-insensitive, N-ethylmaleimide-tre

299 up of target KEGG Orthologs (KOs) from which HMMs were trained.

300 text] association statistics) compared with HMM-based imputation from individual-level genotypes at