ライフサイエンスコーパス: modeling

1 nsights that help in further experiments and modeling.

2 (MPC) via computer-assisted chromatographic modeling.

3 ross-linking mass spectrometry, and homology modeling.

4 for systemic administration require further modeling.

5 opened up new opportunities for T2D disease modeling.

6 randomized trials using parametric survival modeling.

7 l explained through analytical and atomistic modeling.

8 y tubulin polymerization assay and molecular modeling.

9 t to use deep learning (DL) for LV in-silico modeling.

10 ) were analyzed with nonlinear mixed-effects modeling.

11 amined using longitudinal cross-lagged panel modeling.

12 (HTS) in vitro approaches and computational modeling.

13 gh the lens of normative, optimization-based modeling.

14 y developed for use in mathematical oncology modeling.

15 HCpan 4.0, with three-dimensional structural modeling.

16 nalizing symptoms, using structural equation modeling.

17 -target interactions without resorting to 3D modeling.

18 N-terminal SC(1-246) variants preselected by modeling.

19 tructured 3D gridding, and discrete fraction modeling.

20 d forecasting climate change impacts through modeling.

21 lab/Octave to perform personalized metabolic modeling.

22 cale personalized constraint-based metabolic modeling.

23 titutional IR databases without mathematical modeling.

24 stomatal behavior and improving carbon cycle modeling.

25 s is now reinforced by extensive theoretical modeling.

26 ation was assessed using structural equation modeling.

27 clinical applications as well as for disease modeling.

28 for the RRM2,3 isolated domains and homology modeling.

29 ptic partners and with comprehensive circuit modeling.

30 relationship (multiple comparison procedure-modeling, 2-sided p < 0.001) was found in the reduction

31 paper, we describe dynamic weighted survival modeling, a method for estimating an optimal ATS with su

32 , aided by molecular dynamics simulation and modeling, a pertussis-LPS-like pentasaccharide was chemi

33 Experimental and mathematical modeling analyses suggest that active cargo loading redu

34 The range of structure-based tools includes modeling and analysis of protein complexes, delineation

35 Results from secondary structure modeling and analysis of RNase E cleavage of the rimO-cr

36 analyses, interactive exploration, metabolic modeling and bulk download.

37 Using finite-difference, time-domain modeling and comparisons with melanosomes found in other

38 id derivatives (OTIs), designed by molecular modeling and docking, were synthesized.

39 tes (PSC-CMs) hold great promise for disease modeling and drug discovery.

40 nality of human organs on a chip for disease modeling and drug testing, shows great potential for rev

41 Building on previous modeling and experimental work suggesting that striatal

42 plications such as drug repurposing, disease modeling and gene function prediction.

43 ctional characterization, as well as disease modeling and in vivo validation capabilities.

44 types of important data in cancer prognosis modeling and into lung cancer overall survival.

45 Regime shift modeling and management generally focus on tipping point

46 based on docking simulations, pharmacophore modeling and molecular dynamics was applied and computat

47 Using 3-dimensional molecular modeling and mutational analyses, we identified the nucl

48 We use hydrological modeling and new 1200-year tree-ring reconstructions of

49 This study uses air quality modeling and observations over the past four years to se

50 Using a combination of theoretical modeling and quantitative live-cell imaging experiments,

51 Combined approaches of computational modeling and quantitative measurement revealed that the

52 reconstitution, and supporting computational modeling and simulation, to demonstrate that Arabidopsis

53 Here, using mathematical modeling and statistical analyses of T cell receptor seq

54 Molecular modeling and structure-activity relationships support bi

55 using the multiple comparison procedure and modeling and the Cochran-Mantel-Haenszel test, with a 2-

56 bioprinting technologies for in vitro cancer modeling and their applications.

57 tors and hence may have potential in disease modeling and therapeutic development.

58 nstrating their future potential for disease modeling and therapeutic screening applications.

59 D model of MpBgl3 was generated by molecular modeling and used for the evaluation of structural diffe

60 SONATA is used in multiple modeling and visualization tools, and we also provide re

61 wing-up adults vaccinated at >=60 YOA and by modeling, and (2) immunogenicity of 2 additional doses a

62 for FRET-assisted coarse-grained structural modeling, and all-atom molecular dynamics simulations-ba

63 re analyzed using bioinformatics, structural modeling, and epidemiological methods.RESULTSWe identifi

64 Here, we combined theory, numerical modeling, and field observations to develop a mechanisti

65 light and EM tomography, live-cell imaging, modeling, and high-resolution structural analyses has re

66 henotype-driven analysis, protein structural modeling, and in silico calculations were then used to r

67 se high-resolution microscopy, computational modeling, and in vitro and in vivo cell invasion assays

68 e, we used in situ behavioral trials, visual modeling, and laboratory psychophysics.

69 tic analyses, biochemical assays, structural modeling, and molecular docking, we demonstrate that End

70 t/content screening, drug discovery, disease modeling, and personalized medicine.

71 roaches (e.g., the bifactor model, normative modeling, and the functional random forest).

72 We expand this modeling approach by incorporating directionality of edg

73 catotelm peat C legacies using an empirical modeling approach that allows calculating the future cat

74 considerations, this work proposes a hybrid modeling approach that combines a first-principle model

75 rgy budget (DEB) theory offers a mechanistic modeling approach to describe the entire life history of

76 We developed a modeling approach to optimize NG/CT screening strategy i

77 differences based on a full-length homology modeling approach.

78 d by a combined experimental and statistical modeling approach.

79 k-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT

80 gories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predic

81 of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically im

82 ological, genomic, isotopic, and geochemical modeling approaches have led to new paradigms and questi

83 xpression, immunofluorescence, and in silico modeling approaches in the adult mouse brain following 9

84 A comparison of several modeling approaches reveals the partly different behavio

85 electrolyte pH, and it used experimental and modeling approaches to elucidate such impacts in flow-el

86 served detrimental effects across all tested modeling approaches when metabolite time course data wer

87 ively noise-free input functions for kinetic modeling approaches.

88 siderations and caveats in the use of effect-modeling approaches.

89 nd (ii) the relaxation of common restrictive modeling assumptions.

90 ultivoxel pattern analysis and computational modeling based on inverted encoding model simulations.

91 On the neural level, computational modeling-based fMRI analyses revealed that 5-HT depletio

92 ameters of the model are found using inverse modeling, by comparison of model results and measurement

93 ro studies, in combination with mathematical modeling can help optimize and guide the design of clini

94 Modeling cancer-associated mutations in this domain reve

95 rformance and interoperability between broad modeling capabilities.

96 standing at the eighth position in the free modeling category of CASP13.

97 in the electronic health record poses unique modeling challenges.

98 iles with responses to specific drugs and by modeling chimeric antigen receptor T cell immunotherapy.

99 ultivariable conditional logistic regression modeling compared the odds of undergoing screening mammo

100 However, modeling copy number evolution is a substantial challeng

101 Thus, our multiscale modeling correlates cytoskeletal filament size with conf

102 ative to problem formulation, data cleaning, modeling decisions, and interpretations.

103 copic experiments coupled with computational modeling demonstrate that the compelling performance ste

104 s that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred inc

105 Structural modeling demonstrates a TB19/TB38 biparatopic antibody w

106 Mathematical modeling demonstrates that the NAC-induced regulations o

107 The Rosetta software for macromolecular modeling, docking and design is extensively used in labo

108 e-cell transcriptomic data sets and new data modeling drug-resistance acquisition in leukemic T cells

109 This is clearly inadequate in modeling dynamic gene-drug interactions, especially for

110 Prior modeling efforts are briefly surveyed, clinical data col

111 urther determine if casting variability into modeling empowers bottom-up predictions.

112 Here, our experimental design and modeling encompass both.

113 mal schedules generated through mathematical modeling entirely, but travelers who better followed the

114 ge plus treatment against treatment alone by modeling establishment rates of nonindigenous zooplankto

115 inside of budding necks to perform membrane-modeling events necessary for particle abscission.

116 Dynamic causal modeling evidence suggested that treatment decreased lef

117 Predictive modeling explains the sorption data in considering that

118 We illustrate our proposal by modeling fast recalibration of speech sounds after exper

119 rtunities in the application of microkinetic modeling for catalyst design.

120 els has the potential to revolutionize mouse modeling for melanoma.See related article by Bok et al.,

121 easure was derived using convex optimization modeling for microstructure informed tractography (COMMI

122 her highlight the strengths of mixed-effects modeling for reviving a conceptual cornerstone of dendro

123 age of computational advances and multiscale modeling for the analysis of complex, high-density data

124 sing a linear and binary logistic regression modeling for the continuous and categorical outcomes, re

125 o constrain urban emissions using an inverse modeling framework and (2) quantify the information that

126 gnition tasks and are increasingly used as a modeling framework for neural computations in the primat

127 Therefore, our modeling framework provides the crucially lacking evalua

128 Our modeling framework recovers and explains a large diversi

129 study uses a high-resolution, process-based modeling framework to assess the impacts of changing cli

130 We applied our modeling framework to three published examples of multi-

131 ts on this unknown, a dynamic root-hydraulic modeling framework was developed that set up a feedback

132 Unbiased computational modeling further predicted an interaction between Gly48

133 Biophysical modeling has been used over the past two decades to mode

134 While Ocean modeling has made significant advances over the last dec

135 Sexually transmitted diseases (STD) modeling has used contact networks to study the spreadin

136 O(2) (HCHO/NO(2)), developed from theory and modeling, has previously been used to indicate O(3) form

137 vious biophysical experiments and structural modeling have suggested that the N-terminal myristoylati

138 However, modeling healthy and pathological aging of the human vas

139 Additionally, modeling hierarchical variation provides estimates of th

140 Statistical modeling, however, indicates that the FTSJ3 variant is t

141 pproach, hierarchical statistical mechanical modeling (HSM), capable of accurately predicting the aff

142 Structural equation modeling identified CO(2) as the dominant limitation on

143 oneous parameter estimates in tracer kinetic modeling in brain PET studies.

144 genotype-phenotype correlations, 3D-protein modeling, in vitro mutation analyses, magnetic resonance

145 gh-complexity DNA barcoding and mathematical modeling indicate a high rate of de novo acquired resist

146 omatin immunoprecipitation and computational modeling indicate that because unmodified histones dilut

147 Multinomial logistic regression modeling indicated that Drymarchon couperi had a higher

148 Modeling indicates that SARS-CoV-2 control requires the

149 Mathematical modeling indicates that the Plk4 oscillation can be gene

150 sults from natural samples and thermodynamic modeling indicating that percolation of reducing fluids

151 Bayesian structural time-series modeling is a promising new approach to interrupted time

152 Problematic with class modeling is determining which one-class classifier to us

153 Genealogical tree modeling is essential for estimating evolutionary parame

154 Finite element modeling is used to show that the structure of the nanob

155 nificant role in cancer progression and thus modeling it will advance our understanding of cancer gro

156 tistic that is gradually gaining currency in modeling literature due to its demonstrated ability in u

157 In this review, we discuss advances in modeling liver tissue and the latest developments in und

158 d LUSC data, we examine and directly compare modeling lung cancer overall survival using gene express

159 Modeling maternal-fetal transport in FcgammaR/FcRn human

160 By modeling MCM10 deficiency in primary NK cell precursors,

161 sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de

162 mportant HTE, methodological aspects of risk-modeling methods, considerations for translation to clin

163 aracterizing alternative myosin isoforms and modeling muscle diseases, but high-resolution structures

164 t, its computational underpinnings for joint modeling of a common information space and idiosyncratic

165 in gerbils of both sexes with computational modeling of a single cell.

166 cts between GDPD1 and retinal enhancers, and modeling of all RP17 SVs was consistent with neo-TADs le

167 a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forest

168 recognition methods such as soft independent modeling of class analogy (SIMCA) and partial least squa

169 This finding warrants the modeling of concurrent treatment of TB and HIV to potent

170 Coarse-grained modeling of conjugated polymers has become an increasing

171 Emerging studies in computational modeling of decision making, caregiver-related transmiss

172 Machine learning approaches to modeling of epidemiologic data are becoming increasingly

173 e nonhuman primate species for comprehensive modeling of genetic mutations.

174 this new culture medium for chronic disease modeling of IL-13-induced airway hyper-responsiveness.

175 For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hund

176 o handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and c

177 rough comprehensive analytical and numerical modeling of myosin V diffusion and stepping.

178 ermal actuation, we presented the design and modeling of NanoThermoMechanical AND, OR, and NOT logic

179 complexes, delineation of interfaces and the modeling of peptides acting as inhibitors of protein-pro

180 This optimization allows for accurate modeling of receptors using templates as low as 20% sequ

181 MM-TF) method which integrates probabilistic modeling of scRNA-Seq data with the ability to assign TF

182 Lastly, mathematical modeling of tGD spread within populations reveals potent

183 ubstituted cyclobutane product via atomistic modeling of the CdSe surface and substrates, determinati

184 ture of the aptamer and enable computational modeling of the docked complex with RT.

185 and sorting experiments are complemented by modeling of the droplet motion in the channel flow using

186 Hence, accurate modeling of the intra- and interannual variability of fo

187 nting an important step toward comprehensive modeling of the MHC class I pathway.

188 using profiling techniques and bioinformatic modeling of the network effect of multiple miRNAs.

189 Implementing complex modeling of the relationships between individual dynamic

190 ameworks, including multiscale, multiphysics modeling of this complexity, are fueled by the data and,

191 ipotent stem cell methodology enabled better modeling of this disorder.

192 wn of a critical regulatory gene, and permit modeling of viral infection.

193 muscle area on multiple linear mixed-effect modeling (P = .055); however, patient height and height

194 These findings introduce a computational modeling platform and software package for combination t

195 Based on the pore network modeling (PNM) of OCT images, larger pores and connectio

196 Homology modeling predicts that contact between the envelope V1 l

197 in the mouse sinoatrial node where computer modeling predicts that its presence increases HCN4 curre

198 Pharmacokinetic modeling predicts that most high dose regimens trialled

199 Structural modeling predicts that SY242CS confers a conformational

200 collected are presented, and finally thermal modeling results are presented and discussed.

201 disease management planners which translates modeling results into actionable control advice adaptabl

202 ompared experimental data with computational modeling results.

203 Structural modeling revealed a higher binding energy of tadalafil t

204 Modeling revealed attenuated directed exploration under

205 Computational modeling revealed that dominant learning mechanisms unde

206 Mathematical modeling revealed that SOC reduces mechanical stress wit

207 Theoretical modeling revealed that these regulatory strategies (burs

208 with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways

209 ed faults and Coulomb static stress transfer modeling reveals that earthquake interactions promote co

210 Pore pressure modeling reveals that pore pressure changes initiate sei

211 x interaction-network effects in complicated modeling scenarios in high-dimensional data, such as GWA

212 work considerably simplifies and extends the modeling scope for granular dynamics, with potential app

213 Finally, CA1 network modeling showed that desynchronized inputs can impair th

214 Our modeling showed that dialysate electrolyte composition,

215 g biphasic and excitatory effects, which our modeling shows can be explained by intracellular chlorid

216 Cortical brain organoid modeling shows reduced proliferation of radial glial cel

217 Quantitative kinetic modeling shows that only a fraction of RNApII binding ev

218 In modeling simulations, tumor cell doubling time, administ

219 hese results have important implications for modeling SOA formation and growth in the ambient atmosph

220 be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free mo

221 m resolution EM density map-guided structure modeling starting from amino acid sequences.

222 tion based on distinct feature selection and modeling strategies.

223 e designed, implemented, and characterized a modeling strategy based on Dynamic FBA (DFBA), called Li

224 However, recent experimental and modeling studies have begun to question this long-held b

225 Molecular modeling studies provided new insights into the chemical

226 0 N during ramming, and prior finite element modeling studies showed the bony horncore stores 3 x mor

227 egative allosteric modulators, and molecular modeling studies suggested an extracellular binding site

228 Modeling studies unveil the specific binding sites for a

229 identified were substantiated by a molecular modeling study, based on a receptor-driven docking model

230 icles that explore using ray-tracing optical modeling suggest an "illumination gap," in which some te

231 Structural modeling suggested that C-terminal processing increases

232 Modeling suggests that climate change mitigation actions

233 ed a 2D implementation of the Regional Ocean Modeling System (ROMS) to downscale global climate predi

234 In this ex vivo modeling system, clinically used valve-sparing aortic ro

235 troscopy and density functional theory (DFT) modeling, targeting methanol formation from CO(2)/H(2) f

236 are independent of normalization and general modeling techniques; these factors might include reducti

237 Through statistical modeling that combines individual genetic and survival i

238 Here we demonstrate by Monte Carlo modeling that two mechanisms could underlie this aneuplo

239 The major issue is modeling the complex crosstalk among transcription facto

240 Modeling the interactions of the BBSome with membranes a

241 Given its power of precisely modeling the mixed effects from multiple sources of rand

242 ino ether ligands prove to be foreseeable by modeling the reaction with the parent achiral 1,2-bident

243 ake advantage of exciting recent advances in modeling the relations between perturbations and system

244 Modeling the reporting delay distribution is a common fe

245 Modeling the spatial structure of the fossil record tran

246 research efforts have been directed towards modeling the structure and dynamics of the underlying ne

247 Modeling the thermodynamics of a transition metal (TM) i

248 on to our findings, an alternative method of modeling time-dependent inhibition that simplifies assay

249 We used random coefficient modeling to account for the nesting effect of multiple o

250 Here we use metabolic modeling to ask whether acetate and glycerol cross-feedi

251 Here, we use computational modeling to assess whether known GTPase dynamics can giv

252 We used Poisson regression modeling to calculate the prevalence ratios (PRs) and 95

253 urce apportionment photochemical air quality modeling to characterize the contribution of emissions r

254 s to identify eating behavior patterns, twin modeling to decompose correlations into genetic and envi

255 ar cryo-electron tomography, and integrative modeling to determine an in-cell architecture of a trans

256 to large spectral congestion, necessitating modeling to elucidate key spectral features.

257 reversal learning behavior and computational modeling to estimate belief updating across individuals

258 We used mathematical modeling to estimate HDV-HBsAg-host parameters and to el

259 We used Cox proportional hazards modeling to estimate the hazard ratio (HR) and 95% confi

260 itions (NESARC), we used structural equation modeling to examine the shared and specific effects of t

261 ampling can be overcome with spatio-temporal modeling to follow species density redistributions.

262 tes dimensionality reduction and statistical modeling to grapple with the heterogeneity.

263 We used random forest modeling to identify differences in signatures across su

264 Additionally, we use ecological niche modeling to infer current and past (Last Glacial Maximum

265 weeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communi

266 Here, we utilize mathematical modeling to investigate how small outward currents in st

267 cs, time-lapse microscopy, and computational modeling to investigate how the type I interferon (IFN)-

268 genetic variation with demographic and niche modeling to investigate the historical biogeography of a

269 We use metabolic modeling to predict basal ROS production levels (ROStype

270 Full kinetic modeling to quantify tau load was investigated.

271 d egg extract experiments with computational modeling to show that differences in intrinsic propertie

272 We then use modeling to show that over 80% of this variation can be

273 he RAD51-ssDNA interaction with mathematical modeling to show that the flexibility of DNA positively

274 onmental components, and structural equation modeling to test mediation models between the PRSBMI, ea

275 Trajectory modeling together with lineage tracing revealed that air

276 Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building m

277 Modeling tools that can capture heterogeneity in infecti

278 Here we apply modeling tools to better understand adaptive divergence

279 es in ultrahigh vacuum, and first-principles modeling using density functional theory calculations.

280 Metabolic-modeling, using a novel model for MG1655 and continuous

281 By modeling viral lattice assembly and recapitulating oscil

282 sess the expected inundation hazard, tsunami modeling was conducted based on several scenarios involv

283 Computational modeling was then applied to quantify the stability of s

284 Cox proportional hazards modeling was used to compare outcomes including death, a

285 Cox proportional hazards regression modeling was used to determine hazard ratios for coronar

286 proaches (household survey analysis, process modeling), we elucidate factors associated with user sat

287 Using genetics and mathematical modeling, we develop an alternative model of scaling dri

288 Using data-constrained modeling, we find that temperature-driven increases in t

289 Using computational modeling, we find that the early pathological loci of NF

290 Using mathematical modeling, we found that ratio-sensing is a general pheno

291 Based on mathematical modeling, we here demonstrate that clusters of strongly

292 molecular dynamics simulations with kinetic modeling, we quantify cavitation rates on biologically r

293 ples simulations combined with thermodynamic modeling, we show that magma oceans of Earth, Mars, and

294 Supported by molecular dynamics modeling, we show that mildly selective self-assembled m

295 Together with mathematical modeling, we unambiguously demonstrate no cooperativity

296 he immune history of vulnerable populations, modeling when and where the next ZIKV outbreak might occ

297 atomic force microscopy (HS-AFM) and kinetic modeling which allowed us to determine that septin filam

298 e findings, we performed structural equation modeling, which showed that plants but not microbial com

299 mportant and successful targets of cognitive modeling, with decades of model development and assessme

300 genetics studies and recent in utero animal modeling work suggest that precise control of ionic flux