ライフサイエンスコーパス: maximization

1 arent way under the principle of growth-rate maximization.

2 flight list-mode ordered-subsets expectation maximization.

3 fit from phase stabilization through entropy maximization.

4 een put forward through collective influence maximization.

5 ments were in directions predicted by reward maximization.

6 nference, effectively performing expectation maximization.

7 by surprise minimization compared to utility maximization.

8 an economic models based on endpoint utility maximization.

9 sing pixel-based ordered-subsets expectation maximization.

10 nterface, and options for speed and accuracy maximization.

11 ations in which experience does not increase maximization.

12 iple does exist, although it differs from R0 maximization.

13 NA production during phases of transcription maximization.

14 avior may instead reflect constrained reward maximization.

15 ique termed variational Bayesian expectation maximization.

16 ics deliver strong performance for influence maximization.

17 es and then classified them using modularity maximization.

18 ted with risk-preferences and expected-value maximization.

19 ement-related signals used to sustain reward-maximization.

20 ar, compared with ordered-subset expectation maximization.

21 able and theoretically important for utility maximization.

22 power over interval mapping with expectation maximization.

23 the 3-dimensional ordered-subset expectation maximization (3DOSEM) and the 3-dimensional maximum a po

24 In halophytes, however, profit-maximization (4) predicts systematically higher psi(l) t

25 We use an alternating expectation-maximization (AEM) algorithm, alternating between estima

26 An expectation-maximization algorithm and another two stochastic versio

27 using k-means clustering or the expectation-maximization algorithm applied to a Gaussian mixture mod

28 meters, Bisulfighter can use the expectation-maximization algorithm for HMMs to adjust parameters for

29 Unlike MEME, which uses the expectation-maximization algorithm for motif discovery, EXTREME uses

30 An expectation maximization algorithm is used to infer the parameters o

31 dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models f

32 finite mixture framework with an expectation-maximization algorithm that considers two motifs jointly

33 c Pacemaker (EPM), a conditional expectation maximization algorithm that estimates epigenetic landsca

34 s to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates bas

35 ll SeqEM, applies the well-known Expectation-Maximization algorithm to an appropriate likelihood for

36 CLAM uses an expectation-maximization algorithm to assign multi-mapped reads and

37 rameters are determined using an expectation maximization algorithm to both address missing data and

38 scovery, EXTREME uses the online expectation-maximization algorithm to discover motifs.

39 VGA uses an expectation-maximization algorithm to estimate abundances of the ass

40 We used an expectation-maximization algorithm to estimate the parameters underl

41 We show how to use the expectation-maximization algorithm to find the posterior mode of the

42 t variance and a bootstrap-based expectation maximization algorithm to handle extensive missing data.

43 se probability weighting, or the expectation-maximization algorithm to impute missing data were found

44 lihood approach together with an expectation-maximization algorithm to jointly estimate allelic dropo

45 ovide a tool, L1EM that uses the expectation maximization algorithm to quantify LINE-1 RNA at each ge

46 an inference with the stochastic Expectation-Maximization algorithm to quantify underlying diffusion

47 Our approach uses the Expectation Maximization algorithm to train a site-of-metabolism mod

48 The expectation-maximization algorithm was used to derive estimates for

49 nal on the random effect, and an Expectation-Maximization algorithm where we further condition the ra

50 ratings can be inferred with an expectation-maximization algorithm whose running time scales linearl

51 ordinary Poisson ordered-subsets expectation maximization algorithm with point-spread function and ti

52 mated fusion abundance using the Expectation-Maximization algorithm with sparse optimization, and fur

53 del could be estimated using the expectation-maximization algorithm with the observed distribution of

54 cnvCSEM (CNV-guided ChIP-Seq by expectation-maximization algorithm), a flexible framework that incor

55 mars from training data (via the Expectation Maximization algorithm).

56 We use the expectation-maximization algorithm, a classic statistical technique

57 , we have developed an optimized expectation-maximization algorithm, designated HPV-EM, to address th

58 to the standard HMM based on an expectation-maximization algorithm, leading to more accurate and rel

59 ptimized automatically using the expectation-maximization algorithm, taking the number of open channe

60 -expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to

61 selection and can be fit with an expectation maximization algorithm, we call Cox-assisted clustering.

62 models, l1 minimization, and the expectation-maximization algorithm, we prove that spectrotemporal pu

63 line-of-response ordered-subsets expectation-maximization algorithm, with the baseline algorithm plus

64 data in an implementation of the expectation-maximization algorithm.

65 d the standard Variational Bayes Expectation Maximization algorithm.

66 s and is maximized with a hybrid expectation-maximization algorithm.

67 a PET list-mode ordered-subsets expectation maximization algorithm.

68 triads can contribute through an expectation-maximization algorithm.

69 nference is obtained through the Expectation-Maximization algorithm.

70 son regression together with the expectation-maximization algorithm.

71 plotypes were estimated with the expectation-maximization algorithm.

72 uencies were estimated using the expectation maximization algorithm.

73 ructed with a maximum-likelihood expectation maximization algorithm; the system model includes the TO

74 other two stochastic versions of expectation-maximization algorithms are described.

75 Expectation maximization algorithms were used to perform maximum lik

76 Bayesian inference as compared to likelihood maximization alone, for both accuracy and precision.

77 e minimization compared to reward or utility maximization alone.

78 ribe KODAMA (knowledge discovery by accuracy maximization), an unsupervised and semisupervised learni

79 Accuracy Maximization Analysis (AMA) is a recently developed Baye

80 eralist ME model reflecting both growth rate maximization and "hedging" against uncertain environment

81 ng 2-dimensional ordered-subsets expectation maximization and 3-dimensional maximum a posteriori prob

82 show how the principle of inclusive fitness maximization and a related principle of quasi-inclusive

83 cted using 3D maximum-likelihood expectation maximization and analyzed with software.

84 ircuits seem to drive behavior toward reward maximization and effort minimization.

85 expectations, could account for both reward maximization and effort minimization.

86 to be simulated as a combination of fitness maximization and evolutionary constraints.

87 on procedure was developed that uses entropy maximization and is robust with respect to noise and sig

88 indicate that individual differences in gain maximization and loss minimization are linked to individ

89 ructed using both ordered-subset expectation maximization and Q.Clear (block-sequential regularized e

90 butive decisions are consistent with utility maximization and to decompose underlying preferences int

91 mic choice away from logical calculation and maximization and toward biologically plausible mechanism

92 eneric scheme that places pragmatic (utility maximization) and epistemic (uncertainty minimization) i

93 ehavioural implications of inclusive fitness maximization, and point to a possible way in which evolu

94 l Kidney Allocation Scheme (NKAS) and a QALY maximization approach designed to maximize health gains

95 patients who received a transplant, the QALY maximization approach generated 48 045 QALYs and cost po

96 Compared with the 2006 NKAS, a QALY maximization approach makes more efficient use of deceas

97 lying on imputed data, we use an expectation-maximization approach to estimate marginal density funct

98 ansition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent

99 of pound 110 741/QALY compared with the QALY maximization approach.

100 the model parameters, obtained by likelihood maximization, are transferable.

101 We additionally describe an expectation maximization based iterative algorithmic approach which

102 , we propose two new approaches to influence maximization based on two very different metrics.

103 based posterior inference and an expectation maximization-based algorithm for posterior approximation

104 Consequently, many existing Q maximization-based modularity algorithms yield variable

105 n matrix factorization (BMF) via expectation maximization (BEM).

106 nd for given species densities, simultaneous maximization by all plants yields an equilibrium charact

107 related principle of quasi-inclusive fitness maximization can be derived from axioms on an individual

108 approach, utilizing the powerful expectation maximization classification algorithm to examine regions

109 e Voronoi tessellation and using expectation-maximization clustering and Random Forest classification

110 ream with real-time evolution for activation maximization) combined a generative neural network and a

111 lled Debris Identification using Expectation Maximization (DIEM).

112 n of RNA folding ensembles using expectation-maximization' (DREEM), which reveals the alternative con

113 abilities using a combination of expectation-maximization (EM) algorithm and average information rest

114 ing an empirical approach and an expectation-maximization (EM) algorithm developed for estimation and

115 imum likelihood method using the expectation-maximization (EM) algorithm for optimization is commonly

116 The Expectation-Maximization (EM) algorithm has been successfully applie

117 The accuracy of the expectation maximization (EM) algorithm in the presence of alternati

118 cy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program

119 eloped for motif-finding use the expectation-maximization (EM) algorithm iteratively.

120 this paper a privacy-preserving Expectation-Maximization (EM) algorithm to build GLMM collaborativel

121 ive bias removal with a weighted expectation maximization (EM) algorithm to distribute reads among is

122 We invented an Expectation-Maximization (EM) algorithm to divide patients into low-

123 tistical model and a variational expectation maximization (EM) algorithm to estimate non-reference al

124 We use the Expectation-Maximization (EM) algorithm to estimate the parameters i

125 We developed an expectation-maximization (EM) algorithm to estimate the variance par

126 We develop a fast expectation-maximization (EM) algorithm to fit models by estimating

127 er popular motif finders use the expectation-maximization (EM) algorithm to optimize their parameters

128 hypothesis, we used an iterative expectation maximization (EM) algorithm to quantify transcriptomes a

129 The expectation maximization (EM) algorithm used to fit the model iterat

130 parameters are obtained using an expectation-maximization (EM) algorithm, and pseudogenes are utilize

131 We present an expectation-maximization (EM) algorithm, derived for recombinant inb

132 When coupled with the Expectation-Maximization (EM) algorithm, reads can be assigned far m

133 al domains, we have developed an Expectation-Maximization (EM) algorithm-based Essential Domain Predi

134 ely estimated from the developed expectation-maximization (EM) algorithm.

135 rameters were obtained using the expectation maximization (EM) algorithm.

136 cients from NGS data based on an expectation-maximization (EM) algorithm.

137 ocedure that, when combined with expectation-maximization (EM) algorithms for parameter estimation, y

138 We distribute Baum-Welch Expectation Maximization (EM) algorithms running on our model in two

139 In this report, we extended expectation-maximization (EM) algorithms to incorporate prior networ

140 penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested und

141 del reduction, and their related expectation-maximization (EM) algorithms.

142 We also propose an expectation-maximization (EM) based algorithm to estimate the unknow

143 ning a greedy algorithm with the Expectation-Maximization (EM) method for haplotype inference based o

144 A variational Bayesian Expectation Maximization (EM) with smoothed probabilities (VBEMS) al

145 isher scoring algorithm with the expectation maximization (EM)-based ML method, we also developed a s

146 We implement an expectation maximization (EM)-like method to estimate mutation-speci

147 ikelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specif

148 at least 120 maximum likelihood expectation maximization equivalent iterations, including a point sp

149 Using EvoPrinterHD- and Multiple Expectation Maximization for Motif Elicitation-based computational a

150 evels in a way that approximates information maximization for navigation.

151 s while relying on community-level objective maximization for the outer problem.

152 ltiGPS is based on a generalized Expectation Maximization framework that shares information across mu

153 ParsSNP uses an expectation-maximization framework to find mutations that explain tu

154 We then develop an expectation-maximization framework to obtain the automatic segmentat

155 hat they emerge naturally under future state maximization (FSM).

156 istics (Haploview) as well as by expectation-maximization haplotype phase inference (HAP) showed a gr

157 The mechanisms of reward maximization have been extensively studied at both the c

158 psi(l) , and determined under four different maximization hypotheses: water uptake (1), carbon assimi

159 um repeaters and theory of entanglement rate maximization in an entangled network structure.

160 erically reliable solution method for growth maximization in ME models using a quad-precision NLP sol

161 nization in disease spreading, and influence maximization in opinion dynamics.

162 by 2-dimensional ordered-subset expectation maximization into single-frame images and dynamic images

163 tions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, m

164 Influence Maximization is a NP-hard problem of selecting the optim

165 hat decision threshold modulation for reward maximization is accompanied by a change in effective con

166 Maximization is accomplished by a minorize-maximize (MM)

167 led stability analysis and entanglement rate maximization is conceived for the quantum Internet.

168 The principle of entropy maximization is employed to connect estimated moments to

169 This maximization is performed efficiently by a version of th

170 enbasis of his original reduced state, where maximization is performed over all positive-operator val

171 Influence maximization is the problem of finding the set of nodes

172 An algorithm, based on Expectation-Maximization, is presented here for learning the categor

173 -art methods, including K-means, expectation maximization, latent Dirichlet allocation-based clusteri

174 s then updated using an expectation (E)- and maximization (M)-like procedure.

175 cient version of the Monte Carlo expectation-maximization (MCEM) algorithm.

176 using a Markov chain Monte Carlo expectation-maximization (MCMC-EM) algorithm, and information criter

177 An expectation maximization method assigns likelihood values to all spl

178 resent a weighted-log-likelihood expectation maximization method on isoform quantification (WemIQ).

179 y can be achieved using the soft expectation maximization method, whereby each sequence is attributed

180 ions, which is trained using the expectation maximization method.

181 The framework includes a new minorize-maximization (MM) algorithm for generalized linear model

182 empirical soil moisture function, the profit maximization model improved the simulation of evapotrans

183 We tested a new profit maximization model, where photosynthetic uptake of CO(2)

184 ns may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximiza

185 imization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the format

186 ped a clustering method based on expectation maximization of a Gaussian mixture that accounts for loc

187 Ultimately, this enables both a maximization of assay sensitivity and a reduction in ass

188 optimal sleep surfaces may contribute to the maximization of athletic performances, and further studi

189 We present EMBER (Expectation Maximization of Binding and Expression pRofiles), a meth

190 possible genetic constraints preventing the maximization of both, is crucial from both an evolutiona

191 tion, demonstrating their functional role in maximization of Cl(-) flux.

192 iched towards frameshift mutations, allowing maximization of CRISPR/Cas9 phenotype penetrance in the

193 lassifier through a Monte Carlo procedure of maximization of cross-validated predictive accuracy.

194 to energy (WTE) performance is evaluated by maximization of electrical energy production and through

195 generates directional entropic forces is the maximization of entropy by optimizing local particle pac

196 Statistical mechanics relies on the maximization of entropy in a system at thermal equilibri

197 nd affects optimal breeding date, defined by maximization of fitness.

198 It takes into account the maximization of homogeneity of peak spreading (H(s)/A(s)

199 nse and cell survival by mediating the rapid maximization of hsp70 expression.

200 torsional effects at the BZD ring fusion and maximization of imine and amide resonance are proposed t

201 Maximization of information gain reduces, by orders of m

202 These data suggest maximization of information transmission per energy used

203 ubject to weak but significant selection for maximization of initiation rate and, consequently, prote

204 Here our focus is the maximization of likelihood over branch lengths of a give

205 bitrary elemental stoichiometry of life; and maximization of limiting resource use efficiency across

206 These data question the indication for maximization of lymphadenectomy after nCRT.

207 a softer phase - can theoretically result in maximization of material toughness, with little expense

208 ximization of seawater desalination; and (3) maximization of nonpotable water reclamation.

209 international collaborations will result in maximization of our resources and patients, permitting u

210 e that their assembly can be understood from maximization of packing density only in a first approxim

211 ovements in nocturnal vision would depend on maximization of photon capture at the expense of image d

212 ncements at the star tips contributed to the maximization of plasmon coupling between LSPs and SPs as

213 Under the assumptions of (i) maximization of preference in choosing a friend, (ii) mu

214 sis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic erro

215 Optimality theory predicts the maximization of productivity in social insect colonies,

216 The maximization of reliability and specificity of diagnosis

217 behaviour can be understood in terms of the maximization of reproductive value.

218 wards goals or targets are based upon either maximization of resource acquisition or risk avoidance,

219 1) maximization of traditional supplies; (2) maximization of seawater desalination; and (3) maximizat

220 We compared the effects of such consequent maximization of stroke volume index with a regime using

221 Maximization of surface area is key to the optimization

222 al precursor and the support, resulting in a maximization of the amount of accessible metallic nickel

223 gh-spin N-bonded Mn(II) ion that enables the maximization of the attractive van der Waals interaction

224 ow that the optimal measurements used in the maximization of the classical correlation in terms of li

225 As such, maximization of the electronic delocalization of pai-orb

226 imization problem by drawing an analogy with maximization of the entropy for a given energy in statis

227 However, maximization of the extent of tumor resection is hampere

228 The maximization of the objective function of a computationa

229 een the SV and ST, is thought to result in a maximization of the pressure difference between the SV a

230 ng and the optimization of which becomes the maximization of the ratio of the free energy gap between

231 to the BM in this region, and consequently a maximization of the resulting BM velocity.

232 For the maximization of the surface charge density in triboelect

233 entify thermodynamic conditions leading to a maximization of the thermoelectric response of aqueous s

234 lfa leaves was optimised by the simultaneous maximization of the yield and bioaccessibility as a fact

235 m the two powerful platforms and thereby the maximization of their combined strengths for application

236 nical and practical limitations impeding the maximization of their full clinical and preclinical pote

237 etworks evolve based on the principle of the maximization of their internal information flow capacity

238 rios were evaluated for each study area: (1) maximization of traditional supplies; (2) maximization o

239 the minimization of data redundancy and the maximization of variable relevancy.

240 important for early medical presentation and maximization of visual prognosis in some cases.

241 Maximization of whole-cell affinities may enable these o

242 We show that our alternative carbon-maximization optimization is consistent with plant compe

243 heir decisions either based purely on payoff maximization or by imitating the vaccination behavior of

244 brain circuits drive behavior toward reward maximization or effort minimization.

245 Our method can be used to explore a range of maximization or minimization hypotheses, providing new p

246 imal metabolic phenotypes through concurrent maximization or minimization of multiple metabolic marke

247 estimating model parameters, by expectation maximization or related approaches; however, this requir

248 performed with 2 ordered-subset expectation maximization (OSEM) algorithms: attenuation-corrected (A

249 Ordered-subsets expectation maximization (OSEM) is popular for PET studies because o

250 with standardized ordered-subset expectation maximization (OSEM) known to fulfill EANM harmonizing st

251 range of BPL and ordered-subset expectation maximization (OSEM) reconstructions on a PET/CT scanner.

252 range of BPL and ordered-subset expectation maximization (OSEM) reconstructions on a PET/CT scanner.

253 package including ordered-subset expectation maximization (OSEM) with depth-dependent 3-dimensional r

254 constructed using ordered-subset expectation maximization (OSEM), OSEM with point-spread function (PS

255 ional efficiency of LLR, a novel expectation-maximization-path (EM-path) algorithm has been developed

256 logy, which we call perturbation expectation-maximization (pEM), that simultaneously analyzes a popul

257 using 2 methods: ordered-subset expectation maximization (PET(OSEM)) or ordered-subset expectation m

258 ation algorithm is based on the minorization-maximization principle combined with gradient ascent and

259 ative view which may also be formulated as a maximization principle: The electrostatic noise acting o

260 The Influence Maximization Problem (IMP) aims to discover the set of n

261 Despite that CI applies to the influence maximization problem in percolation model, it is still i

262 Moreover, we exactly map the complex utility maximization problem to the classic K -means clustering

263 single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotyp

264 We derive an Expectation-Maximization procedure with closed-form updates that mon

265 Dynamic filter employs an expectation-maximization process to adjust the kinetic model in coar

266 enabled by a Markov Chain Monte Carlo-based maximization process, executed on up to 24 parallel comp

267 ow that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable

268 porated into any least-squares or likelihood-maximization QTL-mapping approach.

269 The maximum-likelihood expectation maximization reconstructed image of a point source in ai

270 to a widely used ordered-subset expectation-maximization reconstruction (Flash3D).

271 ordinary Poisson ordered-subset expectation maximization reconstruction on quantitative accuracy and

272 per bed position; ordered-subset expectation maximization reconstruction with at least 120 maximum li

273 PET/CT studies (ordered-subsets expectation maximization reconstruction, CT attenuation correction)

274 e time-of-flight ordered-subsets expectation maximization reconstruction, similar to the way time-of-

275 ing 3-dimensional ordered-subset expectation maximization reconstruction.

276 s (IEV) or decreases (DEV), such that reward maximization requires either speeding up (Go learning) o

277 ethod, called the stochastic expectation and maximization (SEM) algorithm, to analyze the association

278 anism, based around such spiking expectation maximization (SEM) networks whose combined outputs are m

279 , we propose the surprise-minimization-value-maximization (SMVM) approach.

280 The proposed algorithm, expectation-maximization sparse discriminant analysis (EM-SDA), prod

281 proposed models by incorporating Expectation-Maximization steps into the extremely fast cyclic coordi

282 scale-invariant as a consequence of entropy maximization that is known as Lewis's Law (scaling param

283 We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters fr

284 e different effects, and employs expectation-maximization to iteratively estimate sgRNA knockout effi

285 rk employs the economist's theory of utility maximization to model people's decision regarding their

286 r using M13 genomic DNA and used expectation maximization to obtain robust maximum-likelihood estimat

287 al gold standard-ordered-subsets expectation maximization-using CT-based AC in PET/CT, as well as the

288 We found that, across participants, gain maximization was predicted by differences in amplitude o

289 Two semiautomatic algorithms, expectation maximization, weighted intensity, a priori information a

290 ariable analysis measured by the expectation maximization, weighted intensity, a priori information,

291 however, rates of statin intensification and maximization were low and varied substantially across ho

292 6 s/frame, using ordered-subsets expectation maximization with 4 iterations and 32 subsets.

293 t-mode unrelaxed ordered-subsets expectation maximization with chronologically ordered subsets and a

294 racterization of how humans trade off reward maximization with effort minimization to examine the neu

295 n, we created bursty Monte Carlo expectation-maximization with modified cross-entropy method ('bursty

296 We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM(2)

297 arameters may be estimated using expectation maximization with only a very limited amount of data (e.

298 on (PET(OSEM)) or ordered-subset expectation maximization with point-spread function (PET(PSF)).

299 ar (block-sequential regularized expectation maximization with point-spread function modeling) and we

300 However, subjects who departed from utility maximization, working more in collaborative situations,