ライフサイエンスコーパス: silico

1 substrates, we predicted APC/C substrates in silico.

2 more native, membrane-bound conformation in silico.

3 on the level of inter-areal connectivity in silico.

4 ermined the complete amino acid sequence, in silico 3D structure modeling, and the antiproliferative

5 nzymes in different amniotes, we identify in silico a pathway for sulfur metabolism present in chick

6 n ablation lesions was the most effective in-silico ablation strategy.

7 We foresee that in silico accuracy assessment, demonstrated here with ACTIS

8 Furthermore, in silico alanine scanning calculations of the last 21 resi

9 In silico algorithms predicted five variants to be deleteri

10 hine learning techniques could provide an in-silico alternative to animal models for assessing drug t

11 In silico analyses of of publicly available in vitro data s

12 We performed comprehensive in silico analyses of several features of SARS-CoV-2 genomi

13 In this study, in silico analyses of the lysine histone demethylases (KDMs

14 In silico analyses revealed that 1 ATP synthase is [Formula

15 In silico analyses revealed that microhaplotypes provided m

16 In silico analyses showed that these five loci contained ce

17 Employing in silico analyses, we ranked polymorphisms in C57Bl/6N sub

18 iments, or more computationally intensive in-silico analyses.

19 horter survival of ccRCC patients through in silico analysis and identified KMRC2 as a highly relevan

20 In silico analysis and integration of DNA methylation data

21 he combination of miRNA enrichment assay, in silico analysis and molecular biological approaches reve

22 ng properties of Siglec-7, we carried out in silico analysis and site-directed mutagenesis, and found

23 peptides was enhanced in cFSGS, although in silico analysis did not identify enhanced excretion of p

24 In silico analysis evaluated the impact of each variant on

25 Comparative transcriptomics and in silico analysis identified a small secreted effector pro

26 In silico analysis indicated that the indel in GPRC6A gener

27 In this study, our combined in vitro and in silico analysis indicates that the bound S-citalopram or

28 Using in silico analysis of ATAC- and ChIP-Seq databases in conju

29 In silico analysis of CRX variants was conducted for genoty

30 In silico analysis of genomes of closely related species sh

31 mutations and adaptive potential based on in silico analysis of large sequence datasets.

32 c blueberry plants and in Florida through in silico analysis of plant transcriptomes.

33 breast cancer patient cohort coupled with in silico analysis of publicly available cohorts, high expr

34 An in silico analysis of SNPs on chromosome 4D identified two

35 In this study, in silico analysis of TCGA lung cancer data sets revealed a

36 Experimentally calibrated in silico analysis of transcriptional effects yielded infer

37 We performed a comprehensive in silico analysis of viral peptide-MHC class I binding aff

38 In silico analysis predicted the presence of only two short

39 In silico analysis predicted the variant to cause aberrant

40 In silico analysis predicts tricyclic antidepressants such

41 nt melanoma cells and human biopsies, and in silico analysis revealed an enrichment of Cav3.1 express

42 Subsequent in-silico analysis revealed possible similarities between S

43 An in silico analysis revealed that the 3'UTR of CPSF6 contain

44 In silico analysis suggested that ataxia-telangiectasia mut

45 Complementary in silico analysis suggested that c.1107G > T (p.Glu369Asp)

46 In silico analysis suggests that PrgA can interact with ano

47 [C-L peptide; C (1-8)-L (17-30)] through in silico analysis to reduce cytotoxicity and improve the a

48 supported these predictions, and further in silico analysis was then performed to seek a putative me

49 The in silico analysis was validated ex vivo, through T cell pr

50 otein kinase identified in C. burnetii by in silico analysis.

51 Here, by combining in silico and biochemical screening strategy, we have ident

52 work for estimating molecular bioactivity in silico and complements conventional empirical approaches

53 TATEMENT In the present study, we provide in silico and experimental evidence for a role of the TFs N

54 Recent advances in both in silico and experimental tools for off-target analysis ha

55 In silico and functional studies using cell lines derived f

56 that the method has clear advantages over in silico and genetic screening.

57 The combined in silico and in vitro approaches, which allow for predicti

58 s as molecular force probes and developed in silico and in vitro assays to measure drugs' bilayer-mod

59 LDN in vitro Using numerous complementary in silico and in vitro experimental approaches, we demonstr

60 g pathways, to develop hybrid models with in silico and in vitro measurements, respectively.

61 We approached this issue by combining in silico and in vitro methods to interrogate patients' T c

62 , we showed that active K-Ras4B dimerizes in silico and in vitro through two major interfaces: (i) be

63 Bacteria were evaluated in silico and in vitro using human endometrial epithelial c

64 ty of the candidate vaccine was validated in silico and Molecular Dynamics Simulation confirmed the s

65 Comparison of in silico and phenotypical features align additional varian

66 ent membranes increases membrane tensions in silico and potentiates the progression of invasive squam

67 over a million druggable small molecules in silico and selected putative MEIS inhibitors (MEISi) wit

68 nd validated a putative Michaelis complex in silico and used it to elucidate the hydrolytic mechanism

69 terest clinically, and this in vitro- and in silico approach could also be applied to other rare canc

70 We used an in silico approach for predicting the bioactivities of pept

71 An alternative in silico approach is predicting the phosphoproteomic profi

72 e on five datasets demonstrates that this in silico approach reveals a similar magnitude of global ch

73 We present an in-silico approach to explore genotype-specific variations

74 Here, we apply this in silico approach to study "Accurate Constant via Transien

75 ing mutp53 stabilization, and by using an in silico approach, we built 3D homology models of human DN

76 In a hypothesis-driven in vitro and in silico approach, we turn to early and lower vertebrates

77 Using in silico approaches and various spindle and DNA perturbati

78 In particular, in silico approaches are relevant to compute theoretical CC

79 eview, we discuss various signature-based in silico approaches to drug repurposing, its integration w

80 Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single

81 r interest in using macrocyclic cores for in silico-based lead generation and also inspire the design

82 y using the nitrocefin reporter assay and in silico binding studies.

83 An uncharted frontier for in silico biology is the ability to simulate cellular proce

84 characterized via structural modeling and in silico calculations to predict how specific variants mig

85 nalysis, protein structural modeling, and in silico calculations were then used to rank and predict t

86 with previous data and were confirmed by in silico calculations.

87 lineage-restricted cells both in vivo and in silico, causes a shift of the fate of progenitors away f

88 We present a systematic in silico characterization of the possible DNA triplexes th

89 nciples of evolution and radiobiology for in silico clinical trial design allows clinicians to optimi

90 The in silico cloning and codon optimization supported the prof

91 Further in silico comparison of MscS, YnaI, and YbiO highlighted di

92 ng the efforts of the international Crops in silico consortium.

93 l growth and biofilm formation, automated in silico control of optogenetic systems, and readout of mu

94 Systematic in silico data analysis followed by immunohistochemical val

95 tool for alternatives assessment based on in silico data and multicriteria decision analysis (MCDA) m

96 coli, Drosophila and the DREAM4 simulated in silico dataset show improved predictive accuracy ranging

97 s, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs.

98 Following an in-silico derived hypothesis we found that fentanyl and the

99 This toolbox enables the in silico design and testing of broad-based dHRM screening

100 In silico design of a more sensitive qPCR assay was perform

101 sents a generally applicable strategy for in silico design of protein models that are computationally

102 A key step in this process is the in silico design of single guide RNAs to efficiently and sp

103 However, current in silico design tools have not been developed in view of t

104 zation of histone H2a by antibodies or by in silico designed cyclic peptides enables us to reduce lum

105 e and purify both naturally occurring and in silico-designed DIs as fully encapsidated, infectious vi

106 SNP-7/8a delivering in silico-designed mock neoantigens also induced CD8 T cell

107 tion and further multiplied by performing in silico digestion into peptides.

108 in laboratory conditions was not noticed, in silico docking analysis supports allosteric binding to g

109 Using in silico docking and site-directed mutagenesis, we identif

110 In silico docking of 2,3,5,6TMP-TQS in the putative alloste

111 In silico docking of indole on DNA gyrase predicts that ind

112 tion of substrate channels, combined with in silico docking of SAM in holo MtNifB, suggests the bindi

113 In silico docking predicted the active ligands interacted w

114 In silico docking studies were undertaken to assess the pre

115 n-source and cross-platform R package for in silico drug phase I/II biotransformation prediction and

116 prediction, biomarker identification and in silico drug prioritization by the integration of multiom

117 co virtual screening by applying a robust in silico drug repurposing strategy.

118 states, which can potentially be used for in silico drug screening, as well as contributing to unders

119 In silico drug target prediction provides valuable informat

120 Such an in silico evolution also suggests original and elegant solu

121 In that context, in silico experiments are a powerful tool to understand fun

122 approach in proof-of-concept "synthetic" in silico experiments, in which experimental observations w

123 Our work now leads the way for further in silico exploration of the developmental and evolutionary

124 followed up on relevant top findings with in silico expression quantitative trait loci (eQTL) analyse

125 The importance of these in-silico findings is validated experimentally by site-dire

126 We tested in silico five drugs (astemizole, dofetilide, ibutilide, be

127 sociated with lower lymphocyte counts and in silico follow-up suggests a potential effect on T-lympho

128 Our in silico framework is based on an on-lattice, hybrid, mult

129 ometer, able to deliver transient spectra in silico from first principles.

130 is emulated with light inputs calculated in silico from real-time gene expression measurements.

131 In silico functional follow-up of the GWAS results was unde

132 ed by combining association analysis with in silico genomic feature annotations.

133 In silico genotoxicity assessment of all identified oligome

134 Here, we report a highly efficient in silico-guided approach that led to the discovery of nove

135 gh-throughput experimental data to refine in silico hiPSC-CM populations and to predict and explain d

136 he following key results are demonstrated in silico: (i) HAP and ionising radiation (IR) monotherapie

137 sed in the absence of reference standards in silico if the method is built upon deterministic process

138 ective immune response was assessed by an in silico immune simulation.

139 /12(129-143)) and VP11/12(483-497), using in silico, in vitro, and in vivo approaches based on the fo

140 th a suspected NKD followed by subsequent in silico, in vitro, and in vivo laboratory research.

141 this series was further characterized by in silico, in vitro, and in vivo studies that have demonstr

142 pathogenicity, each variant was assessed in silico; in addition, 32 variants were assessed by functi

143 oMIP were computationally designed using "in-silico" insulin epitope mapping and synthesized by solid

144 Furthermore, the in silico investigation yielded mechanistic insights; e.g.,

145 Our results are based on in silico investigations and a case example focused on body

146 ility changes upon single point mutations in silico is a challenge that has implications for understa

147 Then, we translated these sequences into in silico Isoform Junction Peptides, and created a customiz

148 E) models, which have been used to create in-silico LV models for different cardiac health and diseas

149 Using an in silico meta-repertoire generated from 108 replicates, we

150 r systems to further broaden the scope of in silico metabolic investigation.

151 e Association Prediction (MAP) method, an in-silico method to predict and prioritize miRNA-disease as

152 equence reconstruction (ASR), which is an in silico method to resurrect extinct ancestors of modern p

153 e expected to improve the predictivity of in silico methodologies for allosteric drug discovery and b

154 To overcome this issue, a series of in silico methods have been developed with the primary aim

155 cies and POPs individually is unfeasible, in silico methods have been developed.

156 described a new, comprehensive system of in silico methods that take only protein sequence as input

157 Thus, a variety of in silico methods to detect and predict binding sites was p

158 complement the experimental results, the in silico methods were further employed to add single molec

159 rs was studied using NMR spectroscopy and in silico methods.

160 d L5a showed appropriate drug-likeness by in silico methods.

161 ce for at least two Mycoplasma genitalium in silico minimal genomes.

162 We assess performance using in silico mixtures of real samples, at known proportions, c

163 s using simulated noisy data from a small in silico model and a larger model of central carbon metabo

164 series of prodrugs was designed using an in-silico model for prediction of affinity to chylomicrons

165 Using a quantitative in silico model for the in vivo delivery of genome editors

166 Lastly, an in silico model of p52Shc/p47(phox) interaction using Roset

167 Here, we expand an in silico model of the developing cortical sheet to explore

168 y validate this model by showing that the in silico model reproduces much of the behavior that is obs

169 ery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans.

170 sent work describes the development of an in silico model to predict the retention time (t(R)) of a l

171 Finally, the in silico model was applied to predict the t(R) of an exter

172 allowed ultimate construction of a single in silico model which consists of data for three different

173 Here, we present an in silico modeling and scoring method which exploits the st

174 gene expression, immunofluorescence, and in silico modeling approaches in the adult mouse brain foll

175 In silico modeling by molecular dynamics simulations provid

176 We validated the in silico modeling in cultured adult mouse ventricular card

177 vo SLiM-dependent proximity labeling, and in silico modeling of motif determinants uncovered unantici

178 In silico modeling predicted prolonged intravitreal retenti

179 ology in combination with mutagenesis and in silico modeling to describe the interaction of PES with

180 rest using only in vitro measurements and in silico modeling, potentially relating outcomes to materi

181 ugh a combination of cellular imaging and in silico modeling, we demonstrate that vascular stem cell

182 e sought to use deep learning (DL) for LV in-silico modeling.

183 In silico modelling indicates altered metabolic fluxes (Kre

184 microscopy of mammary tumours in mice and in silico modelling, we identify cell density regulation by

185 biae (OBP1 and OBP47) were analysed using in silico modelling.

186 We use ConvNet units as in silico models of neurons, enabling experiments that woul

187 understand heart function, respectively, in-silico models play an important role.

188 y screen to identify inhibitors, building in silico models to characterize inhibitors, and leveraging

189 strate that fusing experimental cues with in silico models, based on known biochemistry, can contribu

190 Mathematical and computational (in silico) models can predict the optimum geometric conditi

191 Additionally, in silico molecular docking suggests that Abeta can bind fa

192 vitro and in vivo systems, together with in silico molecular modeling, it is determined herein that

193 Here, using various biophysical methods, in silico molecular modeling, microbiological and cellular

194 This study combines in silico, molecular genetics, and biochemical analyses to

195 nt inhibition of the deamination process. In silico mutagenesis examinations further underpin the mol

196 onvolutional filters, attention maps, and in silico mutagenesis.

197 We define structural E/I ratio in an in silico neuronal network, investigate how it relates to p

198 eloped NLR-Annotator, a software tool for in silico NLR identification independent of transcript supp

199 NU-1000 is supported by the physical and in silico observations of a change around the heme ferric a

200 were used for statistical comparisons and in silico pathway analysis.

201 have developed a strategy for generating in silico patients consistent with target population charac

202 718 aa of HSV-1 VP11/12 sequence; (ii) an in silico peptide-protein docking analysis and in vitro bin

203 A from gRNA, is a unique advantage of our in-silico pipeline.

204 esearch, Hanrahan and colleagues adopt an in silico platform to attempt to distinguish benign MEK mut

205 can be made quantitative with the aid of in silico predicted electrospray ionization efficiencies an

206 Genome-wide in silico prediction combined with an in vivo amidase repor

207 Using in silico prediction combined with experimental validation,

208 purification (DAP) sequencing coupled to in silico prediction of binding syntaxes to study several b

209 In silico prediction of drug-target interaction can speed u

210 of stemness, tumorigenesis and survival, in silico prediction of Hsp70 interactions has great value

211 In silico prediction of specific LMNA mutant-driven changes

212 In silico prediction of variant function was performed with

213 mmon PPPCD phenotype and was predicted by in silico prediction tools to be damaging to protein functi

214 Our in silico predictions and in vitro assays suggest that both

215 In silico predictions of protein interactions entail sampli

216 tation Score (MMS) developed by combining in silico predictions of stability, evolutionary conservati

217 In silico predictions revealed that kinase/substrate relati

218 In silico predictions suggest half of Lu. longipalpis saliv

219 tion of nanostructured surfaces providing in silico predictions, complemented with time-lapse fluores

220 In silico predictions, minigene splicing assays, patients'

221 Once trained, Akita enables rapid in silico predictions.

222 An in silico promoter analysis helped identify a putative resp

223 tus in patients with dengue and performed in-silico protein structural analysis to identify epitope s

224 We also conducted enrichment analyses and in silico protein-protein interaction networks to explore t

225 In in silico protein-to-protein networks, we observed key prot

226 ld of natural compounds identified by the in silico protocol.

227 Here we apply a recently developed in silico rational design strategy to produce a bicyclic pe

228 ntial of consortium members, we performed in silico reconstructions of metabolic pathways involved in

229 We contrast our in silico results given defined ecological challenges with

230 To validate these in silico results, we cloned genes encoding candidate acid

231 We illustrate the utility of Rhapsody by in silico saturation mutagenesis studies of human H-Ras, ph

232 In one approach, we used in silico saturation mutagenesis, i.e. the scanning of all

233 onstrate how Akita can be used to perform in silico saturation mutagenesis, interpret eQTLs, make pre

234 An in silico screen and characterization of HCT 116 cells lack

235 Arising from an in silico screen of the MR1 ligand-binding pocket, we ident

236 ible cadherin arrangements and perform an in silico screening according to biophysical and structural

237 In silico screening identified compounds that elicit transc

238 very potent modeling framework to develop in silico screening protocols able to simulate phenotypic s

239 nhibitors and developed a high-throughput in silico screening strategy against homeodomain of MEIS pr

240 (DSF), DNA-encoded library selection, and in silico screening.

241 In silico searches identified additional motif instances fu

242 ts of the pathway were identified through in silico sequence comparison, however, a functional homolo

243 or robust cell growth and to construct an in silico sgRNA library spanning the human genome.

244 roach where living cells interact through in silico signaling, establishing a new testbed to interrog

245 means to study dynamic properties through in silico simulations and perturbations.

246 We test our code both via in silico simulations and with synthesized DNA.

247 We conclude that using DL, LV in-silico simulations can be provided for applications requ

248 el computational advances have shown that in silico simulations can predict drug effects with high ac

249 These trends were consistent with in silico simulations demonstrating that when only one orth

250 Using in-silico simulations of this model and root mean square er

251 l and mathematical modeling, which blends in silico simulations with molecular and evolutionary princ

252 Using FRAP combined with in silico simulations, we find that the lower membrane diss

253 evaluated DRAM performance on a defined, in silico soil community and previously published human gut

254 g, optimize collision energy and generate in silico spectral libraries.

255 In silico spectral library prediction of all possible pepti

256 osis isolates for species identification, in silico spoligotyping, detection of mutations associated

257 We used in silico stochastic simulation of future hybrid performanc

258 An in silico structural approach based on docking simulations,

259 Based on in silico structural modeling, we show that 5-methylcytosin

260 Yeast two-hybrid and subsequent in silico structural prediction uncovered a specific intera

261 Shigella dysenteriae First identified by in silico structural predictions, genetic analyses have dem

262 of recognition modalities in binding and in silico studies along with the relationship between affin

263 In silico studies and structural comparisons identify essen

264 addition to complementarity, in vitro and in silico studies have suggested that RNA structure may inf

265 In silico studies, based on a model of the enzyme in comple

266 was extracted from both experimental and in silico studies, employing different prioritization algor

267 This comprehensive in-silico study will help to understand how curcumin induce

268 For the in silico study, we simulate single cells from TF/pathway p

269 Using an in-silico subcellular model of rabbit ventricular myocyte,

270 e UV-pH titration method combined with an in silico support can be used as a medicinal chemistry tool

271 We created all-atom in silico systems of influenza neuraminidase with experimen

272 uman genomics and proteomics data to make in-silico target identification, reducing the cost and the

273 iew we collectively describe the field of in silico target prediction in the course of time and point

274 ic, scattering, electron microscopic, and in silico techniques, we demonstrate that the two peptides,

275 We combine these in vitro and in silico technologies and demonstrate the utility of high-

276 can provide a roadmap and potentially an in silico testbed for future explorations of seizure mechan

277 In silico tests using known stability data, and in vitro te

278 Here we demonstrate, in silico, the efficacy of an approach from artificial inte

279 An in silico theoretical hydrolysis of amandin subunits corrob

280 dering of our methodology that creates an in silico three-dimensional library of composite peptidic m

281 ically design diverse candidate lifeforms in silico to perform some desired function, and transferabl

282 tigate M305L actin in vivo, in vitro, and in silico to resolve emergent pathological properties and d

283 lamin-associated-domains and provides an in silico tool for quantifying domain length distributions

284 we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA-sequencin

285 sense coding mutations were identified by in silico tools and the ClinVar database.

286 s undertaken by X-ray crystallography and in silico tools to assess the ligand/target interaction mod

287 Furthermore, in silico tools were used to expand our chemical knowledge

288 tion reactions were considered using five in silico tools.

289 Using a genome-based study, we showed in silico translatable genes encoding Vgamma9, Vdelta2, and

290 In silico treatments of neural activity are an important to

291 U and Indian DBT funded project STriTuVaD-In Silico Trial for Tuberculosis Vaccine Development-is sup

292 In silico trials innovations represent a powerful pipeline

293 The wide spectrum of in silico tumors also had a wide variety of responses to an

294 eation, drug reversal potency scoring and in silico validation.

295 An in-silico variable frequency active low-pass filter was dev

296 omponents', multidimensional summaries of in silico variant annotations.

297 e attempted to overcome the limitation of in silico virtual screening by applying a robust in silico

298 In silico, we identified myogenic as well as other cell typ

299 hesis on three experimental platforms: 1) in silico, where modeling ligand-protein docking suggested

300 mature peptides (MPs) has been performed in silico, with a new computational method, for over 200 sp