ライフサイエンスコーパス: silico

1 on the 3'UTRs of the CYPs were identified in-silico.

2 a pathologies such as Parkinson's disease in silico.

3 ed and their binding to ALK5 investigated in silico.

4 turn, affect the rate of EPC in vitro and in silico.

5 G pathologies such as Parkinson's disease in silico.

6 ates for bacterial actins were discovered in silico.

7 ms, but has been challenging to reproduce in silico.

8 to cover relevant epitopes and evaluated in silico.

9 on-experts to explore biomolecular design in silico.

10 role of all of the relevant interactions in silico.

11 ulation in thrombi ex vivo, in vitro, and in silico.

12 te a membrane-bound configuration of Tim1 in silico.

13 In this work we propose, and investigate in silico, a possible experimental strategy to illuminate t

14 odel led us to an unexpected finding that in silico Abeta10-40 and experimental Abeta1-40 constants a

15 putative function analyses were performed in silico according to Gene Ontology annotations.

16 ivotal protease of the complement system: in silico active site mapping for hot spot identification t

17 idance on how to initiate and maintain an in silico ADME-PK infrastructure in an industrial setting.

18 w on the benefits, caveats, and impact of in silico ADME-PK should serve as a resource for medicinal

19 ty relationship (QSAR) models to generate in silico ADMET profiles for hit series to enable more comp

20 000 small molecule compounds was screened in silico against the available crystal structure of the Gt

21 This is also in line with systematic, in silico Alanine scanning free-energy simulations, which i

22 increased concordance that should improve in silico algorithm usage during assessment of clinically r

23 There exists a plethora of in silico algorithms designed to help identify and prioriti

24 lete concordance of predictions among all in silico algorithms used without specifying the number or

25 man leukocyte antigen-binding peptides by in silico algorithms, but the predictive power of this appr

26 Using extensive in silico analyses and comparison to cytometry immunophenot

27 In silico analyses and experimental validation demonstrated

28 In silico analyses confirmed their pathogenic potential.

29 osylation are modulated by radiation, and in silico analyses give insight into the mechanism by which

30 In silico analyses indicated a periplasmic transaminase in

31 In silico analyses indicated that IPF lung fibroblasts have

32 In silico analyses located a DNase I hypersensitivity site

33 In silico analyses of HCC, utilizing published profiling st

34 In silico analyses suggest that membrane-exposed polar resi

35 We performed in silico analyses to identify targets of MIR122 and chroma

36 ts were predicted as likely pathogenic by in silico analyses.

37 eumoniae pneumonia and performed in depth in silico analyses.

38 In silico analysis and chromatin immunoprecipitation assays

39 A comprehensive in silico analysis assessed homologies between virus- and a

40 Moreover, In-silico analysis confirmed probable binding polar and non

41 SMA-like point mutant of LEN followed by in silico analysis for a subset of these proteins.

42 In-silico analysis further highlighted potential associatio

43 In silico analysis has not identified any plant CCR4 protei

44 In silico analysis identified 390 novel anticancer drug pai

45 In silico analysis identified additional regions for PARTIC

46 In silico analysis identified that several putative Klf5 bi

47 We performed in silico analysis in online platforms, in pooled datasets

48 In silico analysis indicated that coding amino acids encode

49 In silico analysis indicated that this 49-mer Aalpha-chain

50 In silico analysis of genome-wide data from >4,000 psoriasi

51 In-silico analysis of human brain expression and network da

52 In silico analysis of metabolic processes likely affected b

53 In silico analysis of RNA-seq data from The Cancer Genome A

54 In silico analysis of Sap6 predicted four amyloidogenic reg

55 developing mouse lung were combined with in silico analysis of the developing mouse salivary gland.

56 Homology modeling and in silico analysis of the GmSACPD-C enzyme revealed that mo

57 Further in silico analysis of this SVA sequence revealed multiple m

58 ur unbiased genetic linkage study and the in silico analysis positions genes known to affect NK cell

59 Although in-silico analysis replicated both variants in Europeans, d

60 In silico analysis revealed the presence of a core microbio

61 In silico analysis showed that the E. coli stringent starva

62 In silico analysis suggested an important role of ZNF143 fo

63 All electrophysiological data and in silico analysis suggested that JZTx-27 trapped VSM of Ns

64 We performed an in silico analysis to identify potential epitopes that may

65 In silico analysis using a computer model of ventricular ti

66 A preliminary in silico analysis using an electrolyte thermodynamic model

67 Our in silico analysis, suggesting a novel function for APOE4 a

68 In silico analyzes of 19 to 25-nt tRFs derived from 5' (tRF

69 Using in silico and biochemical approaches, we identified two hem

70 ited tyrosine kinase inhibitory potential in silico and biochemically; cyanidin-3-O-glucoside had one

71 Through in silico and biological analyses, we identified a novel ln

72 This finding, along with in-silico and biological evidence indicating the potential

73 tools that predict fragmentation patterns in silico and compare these to experimental MS/MS spectra.

74 dependent variants were taken forward for in silico and de novo replication (11 common and 5 rare).

75 Furthermore, our in silico and experimental analyses identified miR-141-3p a

76 ersensitive sites in breast cancer (using in silico and experimental approaches) confirms that they a

77 Based on published data and our in silico and high-throughput analyses, we propose a system

78 In silico and in vitro analyses were used to predict how th

79 Here we combine in silico and in vitro approaches to characterize the SOS t

80 In silico and in vitro characterisation show that these mut

81 inase domain interface, we show with both in silico and in vitro experiments that perturbation of thi

82 y of 1780 prescription drugs by combining in silico and in vitro methods.

83 Finally, both in silico and in vitro results exploring two derivatives 5a

84 In silico and in vivo expression analyses unraveled differe

85 We also provide in silico and in vivo secondary structure predictions for c

86 We created in-silico and in-vitro mixtures of tumour clones, in which

87 Mechanisms were identified by in silico and molecular approaches and validated in mouse l

88 In silico and phylogenetic analyses of these protein famili

89 , kinases and phosphatases were estimated in silico and these were capable of building predictive mod

90 rresponding target transcripts, predicted in silico and validated by RT-qPCR, often showed opposite e

91 eed to predict protein-protein interfaces in silico and various methods for this purpose.

92 ing toward C-RING1B as shown by in vitro, in silico, and in cellulo studies.

93 NUPR1 and the paralogue RING1B in vitro, in silico, and in cellulo.

94 We also show that the in-silico annotation by pathway enrichment analysis of the

95 yotic species using an intra/interspecies in silico approach based on a cross-species similarity sear

96 Here we report an in-silico approach for identification, characterization and

97 This in silico approach indicates the potential of this molecule

98 spective 2D separations assessed using an in silico approach, followed by testing examples of one suc

99 However, current in silico approaches for protease specificity prediction, r

100 ation, and implementation of in vitro and in silico approaches that reduce and replace the use of ani

101 creened 961 random radiolabeled molecules in silico as substrates for essential metabolic pathways in

102 CT1 ligands, defined as ligands predicted in silico as well as found by HTS, were identified.

103 The sequenced genomes can be sorted in silico based on characteristic sequences.

104 benefits and limitations of interventions in silico, before their implementation in human populations

105 In silico benchmarking on simulated tumour phylogenies acro

106 An in silico bioinformatic analysis identified 4 influenza- an

107 Four antibodies, designed entirely in silico, bound the minimal FLAG sequence with high specif

108 Together with in silico breeding, GS is now being used in oil palm breedi

109 we here show that short peptides designed in silico by a recently developed algorithm are capable of

110 nalytical procedure on patterns generated in silico by computationally pooling Saccharomyces cerevisi

111 ion on a pseudodiploid genome constructed in silico by merging the two haploids, we find that approxi

112 nal candidate genes have been prioritized in silico by their co-expression in the brain.

113 The use of in silico calculations allowed us to rationalize the obtain

114 ese metabolites, a result consistent with in silico calculations of reactivity parameters.

115 In silico calculations predicted residue substitutions that

116 These results support in silico CDR design of antibody specificity as an emerging

117 In silico cell sorting identified macrophages/microglia, CD

118 this article, we study the dynamics of an in silico chemical network with random connectivity in an e

119 Two in silico clinical trials with experimental agents ricolino

120 ents and simulations but manage to create in silico configurations that have no experimental analog y

121 lso observed the same motif module in the in silico constructed ancestral TE that also acted cooperat

122 lely to membrane structural changes since in silico cooling of the membrane alone, while maintaining

123 extract lattice level information through in silico correlation provides fundamental insights into th

124 Accordingly, these in silico data can direct ADMET experimentation and profoun

125 Together, our in vivo and in silico data provide a framework for understanding how ce

126 Using in silico data we were able to detect populations at as low

127 Furthermore, in silico data, based on the molecular docking simulation,

128 h and up to four bonds linkers to give an in silico database of approximately 14 million molecules.

129 sing our model as a framework, artificial in silico DBS was applied to find potential alternative tar

130 n - 1) = 0.83) derived indexes along with in silico descriptors.

131 r computational work a second generation, in silico designed catalyst emerged, where replacing Bpin w

132 se were not even accessible by additional in silico digestion with either Asp-N, Arg-C, Glu-C, Lys-C,

133 In silico, direct numerical simulations of whole blood pred

134 We used both in silico docking software, and in vitro molecular and bioc

135 ing detailed kinetic characterization and in silico docking studies, we found that replacing this sin

136 In this study we evaluated in silico docking to develop MMP-subtype-selective tumor-ac

137 plausible binding modes obtained through in silico docking, which provide insights into the structur

138 y exploitation of the obtained models, an in silico drug repositioning approach allowed for the ident

139 By using an "in silico drug repurposing" approach and by validating our

140 In silico drug-target interaction (DTI) prediction plays an

141 th the cellular metabolic activity and an in silico electrophysiology model.

142 rate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kin

143 ped, enabling independent verification of in silico estimates for relative organelle abundance.

144 In silico estimates of protein abundances from publicly ava

145 In summary, signalling entropy allows in silico estimation of the differentiation potency and pla

146 Having used a similar strategy for the in silico evaluation of 150 mutations of CYP21A2, the disea

147 A simple in silico evaluation of novel missense mutations could help

148 Recently, we reported for the first time in silico evidence of RIP encoding genes in metazoans, in t

149 Overall, our in silico evolution experiment offers a window to study the

150 with a fitness-based approach inspired by in silico evolution.

151 ich are then used to query the "metabolic in-silico expansion" database (MINE DB) to obtain possible

152 is capable of performing an unprecedented in silico experiment-simulating an entire mammal red blood

153 l interaction, laying the foundations for in-silico experiments of zebrafish behaviour.

154 Assisted by the model, we conduct in silico experiments to compare the efficacy of different

155 We next perform over 80,000 in silico experiments to infer how metabolic interdependenc

156 uct a valid publicly available method for in silico fimH subtyping of Escherichia coli particularly s

157 modeled on that of 1A1 and used to screen in silico for endogenous metabolite 1A3 allosteres.

158 es the results from database searches and in silico fragmentation analyses and places these results i

159 LipidMatch leverages the most extensive in silico fragmentation libraries of freely available softw

160 cies spanning 56 lipid types contained in in silico fragmentation libraries.

161 mation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemical

162 it becomes practical to mine STR profiles in silico from genome sequences.

163 combination with results from a range of in silico functional analyses and wet bench experiments, ou

164 Gene set analysis and in silico functional evaluation revealed pathways and cell

165 s of the NIMH BD Family Study followed by in silico functional prediction.

166 Furthermore, models developed using in silico generated mixture gene expression profiles from s

167 Using in silico genome-wide sequence analyses, we identified miR-

168 Next, we used a novel in silico genomic analysis, searchable platform-independent

169 We treat in silico granulomas with recommended daily doses of each F

170 y, we developed infrastructure to perform in silico, high-throughput hypotheses testing on such predi

171 d optimization of the benzene-sulfonamide in silico hit compound 3.

172 novel variants predicted to be pathogenic in silico in 15 cases.

173 In sum, our complementary in silico, in vitro, and in vivo analysis argues that inter

174 analysis of ANGPT1 variants in a combined in silico, in vitro, and in vivo approach, supporting a cau

175 A systematic approach involving in silico, in vitro, ex vivo and in vivo studies is employe

176 An integrated in silico/in vitro approach was used to investigate the eff

177 These in silico insights guided corroborating functional studies,

178 hips between model constituents following in silico knockouts were uncovered, and steady-state analys

179 1,000 compounds on the Galphai-GIV PPI by in silico ligand screening and separately by a chemical hig

180 In silico methods for phosphorylation site prediction can p

181 n interactions using previously described in silico methods.

182 Using in silico miRNA target database analyses combined with prot

183 n-maps of adhering particles using a new, in silico model confirmed that adhesion to surfaces is irre

184 alysis across E1E2 in order to propose an in silico model for the ectodomain of the E1E2 heterodimer.

185 To fill the gap, we present an in silico model of affinity maturation to examine two reali

186 e proposed three-dimensional, multiscale, in-silico model of dynamically coupled angiogenic tumour gr

187 itro and using these data in a predictive in silico model of the adult human ventricular myocyte.

188 We developed an in silico model of two-dimensional actomyosin meshwork cont

189 was selected to develop and calibrate the in silico model.

190 l site-directed mutagenesis combined with in silico modeling and docking studies for the first time o

191 More importantly, by combining in silico modeling and our BiLC assay, we identified a smal

192 m a high throughput screen and subsequent in silico modeling approaches.

193 In silico modeling confirms that AMHB effects outweigh thos

194 ) and human studies with PF-04958242, and in silico modeling of AMPAR-NMDAR interactions in the hippo

195 Hence, in silico modeling of brain co-expression is an efficient m

196 dings from auditory brainstem neurons and in silico modeling revealed that application of AUT00063 re

197 In silico modeling showed that theaflavins bind to Asn263 a

198 Additionally, in silico modeling suggests that the neutralizing VHH binds

199 Importantly, in silico modeling validated that this Ser-to-Arg mutation

200 Using in silico modeling, we discovered that anisotropic prolifer

201 g systems immunology approaches combining in silico modelling of a reconstructed gene regulatory netw

202 ogens; incorporate epigenetic biomarkers, in silico modelling, high-performance computing and high-re

203 We also discuss in silico models that reach the same general conclusions an

204 vides valuable information for developing in silico models to simulate the lipid digestibility and ca

205 Using a combination of in silico molecular docking and in vitro directed evolution

206 In silico molecular modeling using atomic resolution and co

207 ns with RAD51 and DNA are correlated with in silico molecular modeling.

208 We apply our technique to an in silico motor control neuroscience experiment, using the

209 Here, the authors take an in silico naive Bayesian classifier approach to integrate m

210 ngs to infer the synaptic connectivity in in silico networks of neurons and compare its performance a

211 Docking in silico of 121 pesticide contaminants of American hives i

212 putative single nucleotide polymorphisms in-silico, of which 8,967 high value SNPs were incorporated

213 ild-type inhibitors, previously predicted in silico, offer an explanation for the lack of antiviral a

214 e carried out dynamic force manipulations in silico on a variety of coiled-coil protein fragments fro

215 At the same time, in silico orthology prediction tools often require large co

216 associations of our scores with diversity in-silico (p < 0.001) and moderate associations in-vitro (p

217 not identical for patients and pigs, but in-silico pathway analysis of proteins with >/=2-fold highe

218 Thus, we used in silico pattern searches to define a pneumococcal secreto

219 ibed, we evaluate by Boolean modeling and in silico perturbations the import of given circuit feature

220 The model may be used as an in silico platform for future research on the regulation of

221 The model is used to explore in silico potential drugs that could slow the progression o

222 In silico predicted target genes were confirmed in reporter

223 unin 1 and 2, were enzymatically digested in silico predicting 10 and 14 peptides, respectively.

224 The server summarizes several in silico prediction algorithms and conservation scores: in

225 Using in silico prediction and functional validation, we identifi

226 ANGPT1 variants was investigated by using in silico prediction and plasma and transfected cells from

227 Through a combination of in silico prediction and site-directed mutagenesis, we have

228 ry structure revealed differences between in silico prediction and structure probing.

229 By means of in silico prediction and subsequent functional validation,

230 (MHC-II) epitope was identified, based on in silico prediction combined with ex vivo screening, and w

231 that OeGLU is a homomultimer with high Mr In silico prediction modeling of the complex structure and

232 RNA on FXI regulation was performed using in silico prediction tools and in vitro luciferase assays.T

233 integration of other analytical data, and in silico prediction tools for modern drug discovery.

234 ole exome sequencing (WES), burden tests, in silico prediction, unbiased in vivo analyses of the muta

235 In silico predictions and in vitro transport studies across

236 tion (false concordance) where concordant in silico predictions are opposite to the evidence provided

237 ng increased specificity as compared with in silico predictions based on motifs from methods such as

238 rvation highlights the need for improving in silico predictions of peptide immunogenicity.

239 mputational tools have been developed for in silico predictions of protein stability in recent years,

240 Our in silico predictions were experimentally confirmed using r

241 es, frequency, mutation hotspot residues, in silico predictions, and functional assays were all infor

242 of the inhibitory capacity supported the in silico predictions, suggesting that evaluating the elect

243 of two cell lines, further confirming the in silico predictions.

244 cids, and are predicted to be damaging by in silico programs.

245 d delivered complementary constraints for in silico protein docking.

246 ased proteomic analysis was combined with in silico quantum mechanical calculations to improve unders

247 a large space of experimental parameters in silico, rather than through costly experimental trial an

248 In silico reconstruction of the proliferation process and t

249 een of 1.5 million compounds, followed by in silico refinement and screening for biological activity

250 beta-cells were detected using ANOVA and in silico replications of mouse and human islet cell genes

251 In silico results indicated that fatty acid (but not choles

252 Although in silico Rosetta simulations correctly identified position

253 ribute to cardiac disease, we employed an in silico screen for cardiac-enriched cDNAs.

254 lattice-energy searches, which provide an in silico screening method to evaluate candidate molecular

255 In silico screening reveals that the majority of the predic

256 isms like Pseudomonas sp. were chosen for in-silico screening toward polyester hydrolyzing enzymes.

257 They were subjected to 2 different in silico screenings and 6 peptides were shortlisted.

258 Overall, our in silico search for post-transcriptional regulators identi

259 Based on the in-silico search, a cutinase from Pseudomonas pseudoalcalig

260 es, live-cell microscopy, and simulations in silico showed that fused mitochondria become critical fo

261 ed SELDOM on a number of experimental and in silico signal transduction case-studies, including the r

262 In summary we present an in silico simulation of the glucocorticoid receptor interac

263 Here we uncover, using in silico simulation, two feed-forward Hox-miRNA loops acco

264 In silico simulations performed after model parameterisatio

265 ilistic model of cell fate allocation and in silico simulations predict a transient wave of acinar di

266 thesis, or complex mathematical models or in-silico simulations.

267 related species, followed by RNA-seq and in silico species separation.

268 ass fragmentation rules are combined with in silico spectra prediction programs CFM-ID and MS-FINDER

269 therapeutics would benefit from improved, in-silico structural modeling of the kinase's solution ense

270 Here we present SVScore, a tool for in silico structural variation (SV) impact prediction.

271 Here we identify anti-VEEV agents using in silico structure-based-drug-design (SBDD) for the first

272 In silico studies suggested that the Si horizontal lineSi b

273 target oriented synthesis (TOS) based on in silico studies, molecules with significant docking score

274 type of evidence ( in vivo , in vitro or in silico ) supporting these influence the predictions.

275 In silico target prediction identified 519 potential cro-mi

276 In silico target prediction identified that insulin-like pe

277 In Silico target predictions revealed that miR-100-5p targe

278 Here, using a broad set of in vitro and in silico techniques we addressed molecular mechanisms of a

279 r optimizing the dialysis regimen and for in silico testing of novel approaches to enhance removal of

280 Moreover, we show in silico that cuRRBS-defined restriction enzymes consisten

281 hnique to simulate enzymatic fingerprints in silico that were used to build the PLS models for FA det

282 HLA-A*0201 (A2) as a model and predicted in silico the 41 highest-affinity, A2-binding 8-11-mer pept

283 ic and stochastic modeling, we reproduced in silico the different dynamic responses of Msn2 to glucos

284 ynamic mathematical model that identifies in silico the most effective administration schedule for ge

285 ble to markedly increase therapy response in silico These results can help guide the rational design

286 cy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and sing

287 ext could be rationally combined together in silico to create promoters with highly predictable activ

288 s species, and second, 26 genes predicted in silico to have metabolic functions likely involved in sy

289 The domain structures were then docked in silico to provide a generic model for the NOX family.

290 macokinetic (PBPK) modeling is a powerful in silico tool that can be used to simulate the toxicokinet

291 We developed an in-silico tool to identify physical clusters of co-regulate

292 of identified variants was examined using in silico tools.

293 Reporter gene and in silico transcription factor binding analyses indicated p

294 We assessed in-silico tumour-specific neoantigen predictions by mutatio

295 This study used in silico virtual screening methodology to identify several

296 on of rigorous experimental screening and in silico virtual screening, we recently identified novel c

297 amework to optimize combinatorial therapy in silico We constructed a detailed kinetic model of the BC

298 ure how these could impact their analyses in silico , we have developed gargammel, a package that sim

299 In silico, we identified 19,420 EPIC probes (referred as mE

300 e the first to predict hydroxymethylation in silico with high whole-genome accuracy, paving the way f