ライフサイエンスコーパス: computationally

1 attice-level carbon structures were explored computationally.

2 made in deciphering gene regulatory networks computationally.

3 Thus, we addressed the problem computationally.

4 known microbial biodegradation was predicted computationally.

5 algorithms has the advantage of being faster computationally.

6 vity of C59NH azafullerene has been explored computationally.

7 ab initio, where conformations are generated computationally.

8 as a ligand was explored experimentally and computationally.

9 selectivity was analyzed experimentally and computationally.

10 oses great challenges both statistically and computationally.

11 es have been investigated experimentally and computationally.

12 ates and transition structures were analyzed computationally.

13 rmational landscape, both experimentally and computationally.

14 ion, as demonstrated both experimentally and computationally.

15 NO-OCT were explored both experimentally and computationally.

16 hing its main physical features, while being computationally affordable.

17 The regioselectivities observed computationally allowed the proposal of a set of rules,

18 We compared 15 protocols computationally and 4 protocols experimentally for batch

19 The stability of these complexes is assessed computationally and by (31)P NMR spectroscopy in toluene

20 f the material has previously been predicted computationally and confirmed in our experiments using X

21 In addition, we also analyze both computationally and experimentally the interactions of M

22 portance of considering disulfide bonds both computationally and experimentally when studying the mec

23 dothelial cell biomechanics was investigated computationally and experimentally.

24 trategy on the processing of each miRNA both computationally and in vivo.

25 ts structure on the node was elucidated both computationally and spectroscopically.

26 he ability to design synthetic nanomaterials computationally and to optimize them through evolution n

27 wing demand to identify pathogenic mutations computationally, as their experimental validation is cur

28 In this study, we computationally assembled a large virtual cohort (n = 1,

29 Here, we computationally assess how adaptive foraging (AF) behavi

30 of those objects, it becomes fundamental to computationally assess which of the interfaces in the la

31 nteraction, which has also been investigated computationally, augments the fluoride anion binding pro

32 s of the remarkable product was accomplished computationally (B2PLYP-D/TZVP/ZORA), providing insights

33 e conclusion suggests a cautionary note that computationally based insights into molecular evolution

34 -antigen in complex with Sf6TSP were studied computationally by molecular dynamics simulations.

35 Here, we address this question computationally by using the forward-flux sampling algor

36 Remarkably, we find computationally (CBS-QB3 and G4MP2) that azulenyl nitren

37 lly realistic spiking neurons can solve this computationally challenging problem in a novel way.

38 motifs in biological networks remains to be computationally challenging task as the size of the moti

39 e-explicit individual models which are often computationally challenging.

40 alignments and the analysis of such data is computationally challenging.

41 ns simultaneously in a centralized manner is computationally challenging.

42 approach since it is more precise and still computationally cheap.

43 This study aimed to computationally compare the performance of acquiring EHG

44 we experimentally constructed and tested the computationally constructed designs.

45 locations and eliminate them before invoking computationally costly alignment algorithms.

46 o known biology is a common task but often a computationally costly one.

47 ics as a function of solvent dielectric, and computationally de novo designed a protein SCPZnI3 to bi

48 parately simulating mutant proteins would be computationally demanding and prone to large statistical

49 Fold prediction is computationally demanding and recognizing novel folds is

50 o assembly of a large metagenomic dataset is computationally demanding and the assembled contigs are

51 However, this model can be very computationally demanding as it needs to account for the

52 action step to be calculated explicitly with computationally demanding electronic structure theory.

53 esults highlight that the significantly less computationally demanding parallel-region approach is su

54 Since this a computationally demanding problem, a first step for this

55 ial neural networks, learning from data is a computationally demanding task in which a large number o

56 This computationally demanding task is known as the stereo co

57 he emergence and continuous updates of these computationally demanding technologies require expertise

58 Moreover, elastic models are much less computationally demanding than fully atomistic and coars

59 timation of correlation matrices can also be computationally demanding.

60 Here we computationally demonstrate a promising route to achieve

61 tors were integrated with experimentally and computationally derived interactome data to build a RIG-

62 Selection of computationally derived model structures of proteins rem

63 Analysis of the experimental and computationally derived optoelectronic properties uncove

64 Computationally derived pKa values, NICS aromaticity cal

65 Computationally derived structures of the reaction produ

66 d through empirical data set analyses to the computationally derived transition states.

67 at one of these positions renders Bole1a, a computationally derived, ancestral genotype 1a HCV strai

68 We here computationally design stimulation signals for electrica

69 we examined the evolutionary history of the computationally designed (retro-)aldolase RA95.

70 ed length variants of the basic scaffold and computationally designed de novo loops projecting from t

71 yr-Asn-Tyr tetrad that emerged adjacent to a computationally designed hydrophobic pocket during direc

72 we create synthetic nucleocapsids, which are computationally designed icosahedral protein assemblies

73 Here, by utilizing computationally designed mutations, we demonstrate that

74 with internal cysteines (rubredoxin), and a computationally designed three-helix bundle (alpha3D).

75 mploys a combination of antibodies and novel computationally designed, recombinant affinity proteins

76 has focused on trying to experimentally and computationally determine the set of transcription-facto

77 diaryl dihydrophenazines, identified through computationally directed discovery, as a class of strong

78 Here we report two anatomically and computationally distinct learning signals in lateral orb

79 This strategy is computationally distinguished from associative learning

80 Here we computationally dock over 3 million molecules against th

81 is has led to the design of high-throughput, computationally driven annotation projects.

82 nt it as R- and Matlab-packages which enable computationally efficient analyses of large data sets.

83 ficient of dispersion, to provide robust and computationally efficient analysis of both gene expressi

84 lect an optimal patch size, we develop a new computationally efficient and data-driven cross-validati

85 ocus system in an F2 population and BAT is a computationally efficient and fast method for estimating

86 sional curves is fast, this equation gives a computationally efficient and intuitive method for solvi

87 Here, using a computationally efficient approach, we extend this idea

88 nucleosomal sequence preferences to create a computationally efficient approximation of the full biop

89 h to RNA-seq analysis by: (i) implementing a computationally efficient bump-hunting approach to ident

90 gest that FIQT is more (i) accurate and (ii) computationally efficient by orders of magnitude.

91 A computationally efficient coarse-grained model of the er

92 While the method performs well, it is not computationally efficient due to the exponential increas

93 -step approach is not only powerful but also computationally efficient even when the number of subjec

94 This computationally efficient framework discards the correla

95 We developed a novel formulation and a computationally efficient greedy search algorithm called

96 Our software package biMM is a computationally efficient implementation of a bivariate

97 rmance with arbitrary fracture geometry in a computationally efficient manner.

98 handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets t

99 We propose a computationally efficient method, ALBI (accurate LMM-bas

100 These results show that simplified, computationally efficient models are an attractive choic

101 This Article introduces a new computationally efficient noise-tolerant signal processi

102 C) sampling inference algorithm, and is more computationally efficient on tests of relatively low cov

103 science include the development of reliable, computationally efficient predictive exposure models; th

104 Our method employs computationally efficient technique, thus it is able to

105 demonstrate that our ADMM algorithm is more computationally efficient than a coordinate descent algo

106 Laplace ApproXimation (MALAX), that is more computationally efficient than MACAU and allows to model

107 n model selection that is significantly more computationally efficient than Markov Chain Monte Carlo

108 mechanism of protein association and offer a computationally efficient tool for predicting its rate.

109 hat are minimal in size, and run in a highly computationally efficient way, with the single goal of e

110 PAC-MAN is computationally efficient, allowing the management of ve

111 They are computationally efficient, applicable to a broad range o

112 MARV is a computationally efficient, flexible and user-friendly so

113 The LDM method is a computationally efficient, iterative workflow consisting

114 TASC is programmed to be computationally efficient, taking advantage of multi-thr

115 scalable to extremely large datasets and is computationally efficient.

116 the classical Fisher combination test and is computationally efficient.

117 hat dMAGA-FCMD is capable of effectively and computationally efficiently training large-scale FCMs an

118 This has been facilitated by computationally encoding the thermodynamics of DNA hybri

119 erform chlorination versus hydroxylation was computationally evaluated for different substrates that

120 Here we have experimentally and computationally evaluated the functional contributions o

121 The model additionally was used to computationally evolve highly active 5' UTRs.

122 ne and per-sample mutational frequencies are computationally expensive and have limited precision.

123 nal approaches using sparse optimization are computationally expensive and have no selection criteria

124 n generic assembly, which is error-prone and computationally expensive for complex data.

125 Additionally, MACAU uses a computationally expensive Markov Chain Monte Carlo (MCMC

126 ilable or to numerical solvers based on more computationally expensive methods.

127 d buffer concentrations without resorting to computationally expensive numerical solution of reaction

128 the maximum likelihood model parameters in a computationally expensive process with downhill optimize

129 ng the network, avoiding the need to perform computationally expensive regression methods with specif

130 cluding CVTree, $d_2^*$ and $d_2^S$ are more computationally expensive than measures based solely on

131 However, it is currently computationally expensive to perform hierarchical cluste

132 oxies for true atomistic dynamics, which are computationally expensive to perform routinely.

133 materials screening efforts are hindered by computationally expensive transition state barrier calcu

134 However, such Monte Carlo simulations are computationally expensive, and are therefore not suitabl

135 e transcripts is technically challenging and computationally expensive, focusing on single splicing e

136 tion profile, and whilst powerful, these are computationally expensive, limiting practicality.

137 It is also computationally expensive, limiting the ability to condu

138 In addition, the FPB is substantially less computationally expensive, requires less information on

139 ML schemes are computationally expensive, requiring an eigenvalue decom

140 ch as those based on pupil functions, can be computationally expensive.

141 genotype calls, the prephasing task becomes computationally expensive.

142 eritability scores, while NB-fit is the most computationally expensive.

143 ing penalty terms in the scoring function is computationally expensive.

144 e in computer vision applications, which are computationally expensive.

145 hidden Markov models, which are accurate but computationally expensive.

146 all possible causal configurations, which is computationally expensive.

147 the process of extracting these features is computationally expensive.

148 ical footing now exists to generate and test computationally explicit predictions of behavioral and n

149 in that it will drive new research by making computationally explicit predictions of SC neural popula

150 rformance are now available, which provide a computationally explicit solution for the recognition of

151 er reactivity of (2,7)pyrenophanes have been computationally explored using state-of-the-art Density

152 reactions involving 1,2-azaborines have been computationally explored within the density functional t

153 in full field cryo soft X-ray tomography to computationally extend the depth of field (DOF) of a Fre

154 ystack Heuristic, an algorithm customized to computationally extract disease-associated motifs from n

155 ded by terahertz time-domain spectroscopy to computationally extract occluding content from layers wh

156 tion-based methods are more powerful, though computationally far more intensive.

157 In conclusion, we provide a computationally fast algorithm to implement a statistica

158 pproximate or summary coalescent methods are computationally fast and are applicable to genomic datas

159 Here, we propose a computationally fast score-test-based method that estima

160 The approach is computationally fast, enabling the application to whole-

161 be approximated with good performance using computationally faster heuristic clustering approaches (

162 aplotypes and fitting the regression is made computationally feasible by the low diversity setting.

163 huge-scale testing problems arbitrarily into computationally feasible sets or chunks Results from the

164 t processing of even much larger datasets is computationally feasible.

165 response associations, hypothesized to arise computationally from 'model-free' learning.

166 wn due to low binding affinity, therefore we computationally generated an ensemble of apoCaM-Ng13-49

167 We compare the computationally generated structural ensembles of the ID

168 ructures classified into 129 families; (iii) computationally generated three-dimensional models of TM

169 Computationally guided mutagenesis and kinetic analyses

170 formation processing, there exist classes of computationally hard problems wherein this paradigm is f

171 background activity and without resorting to computationally heavy off-line processing.

172 he possible chain structures were determined computationally, highlighting a delicate balance between

173 We investigate computationally how structural constraints on proteins i

174 Here we study experimentally and computationally how vein patterns affect growth.

175 From this dataset, we computationally identified activity patterns associated

176 8 isolate reference DNA viruses with 264 413 computationally identified viral contigs from >6000 ecol

177 Here we computationally identify second-sphere amino acid residu

178 Computationally identifying circRNAs from total RNA-seq

179 We computationally illustrate that our approach achieves er

180 Tissue dominant effects are first removed computationally in order to define these subtypes, which

181 tify the signaling pathway, was accomplished computationally in reference to the known "closed" apo-P

182 cal assessment of domain predictions and are computationally inefficient for high-resolution Hi-C dat

183 e to their statistical nature and can become computationally inefficient for large systems; analytica

184 We introduce a computationally inexpensive protocol for the systematic

185 ical functions such as a Gaussian, which are computationally inexpensive, may not accurately capture

186 Since the calculations of ES(r) are computationally inexpensive, the descriptor offers fast

187 e combined 1,398 human and mouse datasets to computationally infer ISG modules and their regulators,

188 ES, an approach that improves the ability to computationally infer TRN from time series expression da

189 5mC and 5hmC levels can be computationally inferred at single base resolution using

190 are not directly measured, but they must be computationally inferred from these sequencing data.

191 Our computationally inferred immune infiltrates associate mu

192 y previously described methods but favored a computationally informed method that enabled selection o

193 Normalising affective biases using computationally inspired interventions may represent a n

194 e has remained limited because the method is computationally intensive and conceptually challenging.

195 s to single-cell transcriptomic analysis are computationally intensive and require assay-specific mod

196 These are computationally intensive and time consuming steps, whic

197 ies selecting groups of similar isolates for computationally intensive methods of phylogenetic infere

198 We determined that the computationally intensive PBcR-BLASR assembly pipeline y

199 Complex and computationally intensive pipelines are required to asse

200 static loci is a statistically difficult and computationally intensive problem.

201 en hindered by inaccurate approximations and computationally intensive simulation.

202 ubmission and access to private datasets and computationally intensive workspace-based analysis requi

203 tion and maintenance of distance matrices is computationally intensive, and rapid methods of doing so

204 This approach is computationally intensive, requiring integration of disp

205 o historical air-temperature variability and computationally interpolated to provide high-resolution

206 Evaluating the performance of PBT was computationally intractable and previous attempts succee

207 ansition to be probed directly and revealing computationally intractable features that rely on the lo

208 mation towards a given target is therefore a computationally intractable problem.

209 ing every possible combination of choices is computationally intractable, particularly for longer mul

210 s ME problem is NP-complete, and so probably computationally intractable.

211 interaction structure between genes, or are computationally intractable.

212 We also computationally investigate the variants of this compoun

213 We have computationally investigated the introduction of copper

214 specificity, assigning receptors to pathways computationally is possible.

215 Computationally it was shown that desynchronizing delaye

216 any cultivated representative, most could be computationally linked to dominant, ecologically relevan

217 This strategy provides a non-optimal but computationally manageable solution to the task of vocal

218 the structural biology community that can be computationally mined to complement ongoing research tow

219 two key PUSs acting on mammalian mRNA and to computationally model the sequence and structural elemen

220 ren and adolescents with and without ASD, we computationally modeled individuals' visual orientation

221 In this study, we computationally modeled the effects of missense cancer m

222 In this work, we resolve the issue by computationally modeling a plausible atomistic 3D struct

223 s distinguishing between these mechanisms by computationally modeling goal-directed and habitual beha

224 hat have been regarded as "higher order" are computationally more complex than "simple" associative l

225 second annulation reaction was rationalized computationally on steric grounds.

226 luenza was derived from a methodology termed computationally optimized broadly reactive antigen (COBR

227 fic influenza vaccines, a methodology called computationally optimized broadly reactive antigens (COB

228 described the design and characterization of computationally optimized broadly reactive hemagglutinin

229 lic guanidinocalixarenes whose structure was computationally optimized to dock into MD-2 and CD14 bin

230 procedure on patterns generated in silico by computationally pooling Saccharomyces cerevisiae microsa

231 The methodology is conceptually simple and computationally practical, and provides a broadly effect

232 essed and ethanol-adapted E. coli cells with computationally predicated ethanol-binding proteins and

233 of eCLIP experiments, it is now possible to computationally predict RBP binding sites across the who

234 We computationally predict that cross-family PDGF binding c

235 mportance of multiple scattering effects and computationally predict the impact of local particles' e

236 The ability to computationally predict whether a compound treats a dise

237 tally probed ( approximately 23 million) and computationally predicted (approximately 117 million) RB

238 us PMMs and these mutations are enriched for computationally predicted impacts on splicing.

239 Computationally predicted products of HMX hydrolysis suc

240 encoded on 22q11.2, increased levels of the computationally predicted putative miR-185 targets UDP-N

241 More than 86.6% of these computationally predicted regulatory regions were partia

242 t and leverage experimentally determined and computationally predicted structures are urgently needed

243 Engineered disulfide bridges, computationally predicted to interfere with IFNAR1 dynam

244 y radical (likely catalyzed by Cu), which is computationally predicted to spontaneously trigger C-C b

245 a user-friendly database, ScaPD, to describe computationally predicted, experimentally validated scaf

246 y validated, and their putative targets were computationally predicted.

247 We detail the methods used to computationally process and interpret sequence data to i

248 drochalcone with a POPC membrane was modeled computationally, providing evidence that it is not a pan

249 by detecting features for all compounds in a computationally reasonable time.

250 chromosomal contact data that can be used to computationally reconstruct 3D structures of the genome.

251 Here, we used a marker-free approach to computationally reconstruct the blood lineage tree in ze

252 Computationally reconstructed SC-macrophage molecular ne

253 s petersii allow for the sensory input to be computationally reconstructed, enabling us to link the i

254 forward connectivity are capable of creating computationally relevant mixed selectivity, such a model

255 ate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selec

256 r epistasis on evolutionary trajectories, we computationally removed high-order epistasis from experi

257 red into smaller pieces, sequenced, and then computationally reordered and analyzed, enables fast and

258 Our study addresses how we can computationally represent drug-signaling pathways to und

259 We model these responses computationally, revealing that mismatch and omission re

260 ISAMBARD is open-source, modular, computationally scalable and intuitive to use.

261 We computationally screened vertebrate genomes for conserve

262 ected prostate per patient was digitized and computationally segmented into nuclei, lumen, and combin

263 ssion of integral membrane proteins based on computationally simulated membrane integration efficienc

264 l only with undirected networks, they can be computationally slow and are based on normality assumpti

265 r(IV,IV) and Ir(IV,V) redox states have been computationally studied both with DFT and multiconfigura

266 c acid, 4-methylcatechol, and catechol) were computationally studied using density functional theory,

267 We computationally study how an icosahedral shell assembles

268 luding those that are challenging to predict computationally, such as intermolecular and long-range i

269 Computationally, such asymmetric learning predicts risk

270 nd the fact that these groups of neurons are computationally sufficient to generate decisions.

271 we have demonstrated both experimentally and computationally that a proper selection of the substitut

272 Here, we demonstrate theoretically and computationally that long-lasting sparks emerge as a col

273 hese N-unsubstituted compounds, it was found computationally that the lowest-energy stereodynamic pro

274 Here we investigate experimentally and computationally the translational dynamics of vicinal wa

275 Computationally, the prediction of binding selectivity i

276 Computationally, these two systems of behavioural contro

277 Computationally this has been modeled by allowing partic

278 d whether the drug effects can be formalized computationally to allow single subject predictions.

279 d green fluorescent protein variant designed computationally to have reduced frustration is indeed le

280 corroborate this strategy experimentally and computationally to the microwave absorption of manganese

281 es, shown previously in model membranes, and computationally, to affect bilayer thickness and lipid p

282 g the public health effects of emissions are computationally too expensive or do not fully address co

283 e than the one that we are proposing here or computationally too intensive, thereby limiting their ca

284 By providing a computationally tractable and numerically consistent fra

285 In our novel, computationally tractable approach to RNA-ligand kinetic

286 y level results data, LD score regression is computationally tractable even for very large sample siz

287 tion that does not require prephasing and is computationally tractable for whole-genome imputation.

288 ath-birth (DB) updating, we derive a simple, computationally tractable formula for weak selection to

289 d MRI signals are jointly analyzed through a computationally tractable formulation of partial least s

290 The availability of computationally tractable metadata is especially importa

291 Our technique is computationally tractable, generally outperforms other m

292 Bonferroni correction, though computationally trivial, is overly conservative and fail

293 from high-resolution Hi-C datasets is often computationally unfeasible using existing methods.

294 s complexed to actinide cations are explored computationally using density functional and coupled clu

295 their homolytic fragmentations were studied computationally using hybrid density functional theory (

296 loped system-scale patterns are investigated computationally using numerical continuation methods.

297 ed-linker ZIF structures that were generated computationally using the short-range order (SRO) parame

298 Here we describe and computationally validate a framework that combines the c

299 d to extract only partial information or are computationally very demanding.

300 Computationally, we develop an algorithm using the alter