ライフサイエンスコーパス: HMM

1 HMMs have been shown to exhibit exotic optical propertie

2 y identified using one of two major ways: 1) HMM-profile based searches using models built on Arabido

3 ile HMM library containing a total of 27 623 HMMs.

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 by realizing an epitaxial superlattice as an HMM.

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

12 erence given a simple text description of an HMM.

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

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

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

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

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

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

19 tructure of metallic diffraction grating and HMM.

20 As a result, vHMM-HA and HMM-HA induce opposing effects on the expression of CD44

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

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

23 filament severing pattern of stationary and HMM propelled filaments.

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

25 ree approaches with the default or available HMMs.

26 d AUGUSTUS) using their respective available HMMs.

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

28 atrix in the likelihood of the Poisson based HMM is replaced by the observed transition probabilities

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

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

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

32 y enhanced actin filament severing caused by HMM-induced forces at 1 mM MgATP, an effect that was inc

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

34 s much lower coverage than those required by HMM and statistics based methods.

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

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

37 (WGS) data using NCBI's AMRFinder and custom HMM search.

38 w for easy building and evaluation of custom HMMs, which could be a useful resource for the research

39 se a simple conceptual template to customise HMMs for their specific systems of interest, revealing m

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

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

42 Bayes mixture modeling approach called EBSeq-HMM.

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

44 se approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.

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 Inference in these so-called Factorial HMMs has a naive running time that scales as the square

50 ackage for fast exact inference in Factorial HMMs.

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

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

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

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

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

56 large collection of standard algorithms for HMMs as well as a number of extensions and evaluate the

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 In a standard genomic HMM, observations are drawn, at each genomic position, f

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

66 gATP, an effect that was increased at higher HMM motor density.

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

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

69 However, HMMs have only recently begun to gain traction within th

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

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

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

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

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

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

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

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

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

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

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 d not show increases in high molecular mass (HMM) species after treatment with DPTA or bortezomib + D

86 orter myosin subfragments, heavy meromyosin (HMM) and myosin subfragment 1 (S1).

87 pelled by surface-adsorbed heavy meromyosin (HMM) motor fragments.

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

98 s comparing to previous hidden Markov model (HMM) based methods.

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

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

101 This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography b

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

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

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

105 We develop a Hidden Markov Model (HMM) framework for estimating the admixture proportions

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

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 s within a multivariate hidden Markov model (HMM) to detect footprint-like regions with matching moti

120 then used a three-state hidden Markov model (HMM) to detect musth behaviour in a subset of sequential

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

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

123 s within a multivariate hidden Markov model (HMM) to quantify changes in movement behaviour of grey s

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

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

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

127 d and implemented a new hidden Markov model (HMM)-based ab initio gene prediction tool, which is opti

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

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

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

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

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

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

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

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

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

137 of RRS is based on the hidden-Markov-model (HMM) to offer a robust enough way for tracing arbitrary

138 entional approach of hidden Markov modeling (HMM) followed by hard thresholding.

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

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

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

142 analysis applications-hidden Markov models (HMM).

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

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

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

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

147 Hidden Markov models (HMMs) are powerful tools for modeling processes along th

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

149 ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system sta

150 We use hidden Markov models (HMMs) fitted in a Bayesian framework and hourly Global P

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

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

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

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

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

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

157 o fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of symbols.

158 nd statistical models (hidden Markov models (HMMs)).

159 n work with any custom hidden Markov Models (HMMs), also included are 13 newly generated SCG-set HMMs

160 Hidden Markov models (HMMs), especially those with a Poisson density governing

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

162 e alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of h

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

164 icroorganisms based on Hidden Markov Models (HMMs).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

182 Description of HMM module-obtained macula images.

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

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

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

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

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

188 mon terminology, review the immense scope of HMMs for applied ecological research and provide a tutor

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

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

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

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

193 d automatic Pfam domain assignments based on HMM profiles.

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

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

196 myosin SRX population in STFs than in S1 or HMM but also in increasing myosin SRX population equally

197 statistical models (hidden Markov models or HMMs).

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

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

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

201 Our HMMs separate elephant movements into two states: explor

202 t SCI sub-compartment prediction outperforms HMM.

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 on a phylogenetic hidden Markov model (phylo-HMM).

206 associated sequence alignments, phylogenies, HMM models and functional descriptors.

207 PhyloNet-HMM combines phylogenetic networks with hidden Markov mo

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

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

210 novel sequences to OGs based on precomputed HMM profiles.

211 the addition of new SUPERFAMILY 2.0 profile HMM library containing a total of 27 623 HMMs.

212 ntation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups.

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

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

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

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

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

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

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

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

221 and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster ser

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

223 feature is the integration of unique profile HMMs to link complex chemosensory systems with correspon

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

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

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

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

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

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

230 wed after stronger assignments of the repeat HMM.

231 idated samples compared to 15 found by CGI's HMM-based CNA model.

232 more, WT myosin containing STFs, but not S1, HMM, or STFs-containing R403Q myosin, recapitulated the

233 han current tools while maintaining the same HMM hit accuracy.

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

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

236 , we investigate the potential of sequential HMM-profile identification for the rapid and precise ide

237 also included are 13 newly generated SCG-set HMMs for different lineages and levels of resolution, bu

238 rotective properties compared to the shorter HMM-HA.

239 pression of 2B6 enzyme activity, significant HMM species generation, and ubiquitination of CYP2B6 pro

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

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

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

243 Images were acquired using the Spectralis HMM by a single operator during 2 separate imaging sessi

244 e-shaped dependency of samples over standard HMM modeling.

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

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

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

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

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

250 th periods over age 35 meant the three-state HMM could automatically detect musth movement with high

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

252 show that TreeGrafter outperforms subfamily HMM scoring for correctly assigning subfamily membership

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

254 coverage of reads, which is much better than HMM based methods.

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

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

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

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

259 The HMM can smooth variations in read abundance and thereby

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

261 The HMM images were classified qualitatively based on struct

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

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

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

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

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

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

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

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

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

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

272 lso observed reduced sliding velocity of the HMM-propelled filaments in the presence of gelsolin, pro

273 rray in order to evaluate performance of the HMM.

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

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

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

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

278 Images obtained with the HMM allow for photoreceptor mosaic visualization in the

279 ed functions into multiple points within the HMM framework.

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

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

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

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

284 We provide a gentle introduction to HMMs, establish some common terminology, review the imme

285 e inference of all key quantities related to HMMs: (i) the (Viterbi) sequence of states with the high

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

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

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

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

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

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

292 VB-HMM further revealed immature dynamic interactions betwe

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

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

295 ae were imaged in vivo using still and video HMM lens modes, with fixation and contrast adjustments t

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

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

298 s CD44 protein-protein interactions, whereas HMM-HA promotes them.

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