ライフサイエンスコーパス: in silico

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

2 o a more native, membrane-bound conformation in silico.

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

4 determined the complete amino acid sequence, in silico 3D structure modeling, and the antiproliferati

5 t enzymes in different amniotes, we identify in silico a pathway for sulfur metabolism present in chi

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

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

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

9 In silico algorithms predicted five variants to be delet

10 Machine learning techniques could provide an in-silico alternative to animal models for assessing dru

11 In silico analyses of of publicly available in vitro dat

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

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

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

15 In silico analyses revealed that microhaplotypes provide

16 In silico analyses showed that these five loci contained

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

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

19 d shorter survival of ccRCC patients through in silico analysis and identified KMRC2 as a highly rele

20 In silico analysis and integration of DNA methylation da

21 The combination of miRNA enrichment assay, in silico analysis and molecular biological approaches r

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

23 ved peptides was enhanced in cFSGS, although in silico analysis did not identify enhanced excretion o

24 In silico analysis evaluated the impact of each variant

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

26 In silico analysis indicated that the indel in GPRC6A ge

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

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

29 In silico analysis of CRX variants was conducted for gen

30 In silico analysis of genomes of closely related species

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

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

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

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

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

36 Experimentally calibrated in silico analysis of transcriptional effects yielded in

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

38 In silico analysis predicted the presence of only two sh

39 In silico analysis predicted the variant to cause aberra

40 In silico analysis predicts tricyclic antidepressants su

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

42 An in silico analysis revealed that the 3'UTR of CPSF6 cont

43 In silico analysis suggested that ataxia-telangiectasia

44 Complementary in silico analysis suggested that c.1107G > T (p.Glu369A

45 In silico analysis suggests that PrgA can interact with

46 ned [C-L peptide; C (1-8)-L (17-30)] through in silico analysis to reduce cytotoxicity and improve th

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

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

49 protein kinase identified in C. burnetii by in silico analysis.

50 Subsequent in-silico analysis revealed possible similarities betwee

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

52 amework for estimating molecular bioactivity in silico and complements conventional empirical approac

53 E STATEMENT In the present study, we provide in silico and experimental evidence for a role of the TF

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

55 In silico and functional studies using cell lines derive

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

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

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

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

60 ling 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'

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

63 Bacteria were evaluated in silico and in vitro using human endometrial epithelia

64 ality of the candidate vaccine was validated in silico and Molecular Dynamics Simulation confirmed th

65 Comparison of in silico and phenotypical features align additional var

66 sement membranes increases membrane tensions in silico and potentiates the progression of invasive sq

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

68 d and validated a putative Michaelis complex in silico and used it to elucidate the hydrolytic mechan

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

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

71 An alternative in silico approach is predicting the phosphoproteomic pr

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

73 Here, we apply this in silico approach to study "Accurate Constant via Trans

74 aining mutp53 stabilization, and by using an in silico approach, we built 3D homology models of human

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

76 We present an in-silico approach to explore genotype-specific variatio

77 Using in silico approaches and various spindle and DNA perturb

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

79 s review, we discuss various signature-based in silico approaches to drug repurposing, its integratio

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

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

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

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

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

85 n analysis, protein structural modeling, and in silico calculations were then used to rank and predic

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

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

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

89 principles of evolution and radiobiology for in silico clinical trial design allows clinicians to opt

90 The in silico cloning and codon optimization supported the p

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

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

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

94 Systematic in silico data analysis followed by immunohistochemical

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

96 E.coli, Drosophila and the DREAM4 simulated in silico dataset show improved predictive accuracy rang

97 ites, 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

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

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

101 presents a generally applicable strategy for in silico design of protein models that are computationa

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

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

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

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

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

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

108 es in laboratory conditions was not noticed, in silico docking analysis supports allosteric binding t

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

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

111 In silico docking of indole on DNA gyrase predicts that

112 diction of substrate channels, combined with in silico docking of SAM in holo MtNifB, suggests the bi

113 In silico docking predicted the active ligands interacte

114 In silico docking studies were undertaken to assess the

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

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

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

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

119 In silico drug target prediction provides valuable infor

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

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

122 our approach in proof-of-concept "synthetic" in silico experiments, in which experimental observation

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

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

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

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

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

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

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

130 ing 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 u

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

133 In silico genotoxicity assessment of all identified olig

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

135 high-throughput experimental data to refine in silico hiPSC-CM populations and to predict and explai

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

137 sessed in the absence of reference standards in silico if the method is built upon deterministic proc

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

139 P11/12(129-143)) and VP11/12(483-497), using in silico, in vitro, and in vivo approaches based on the

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

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

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

143 NanoMIP were computationally designed using "in-silico" insulin epitope mapping and synthesized by so

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

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

146 tability changes upon single point mutations in silico is a challenge that has implications for under

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

148 (FE) models, which have been used to create in-silico LV models for different cardiac health and dis

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

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

151 l sequence reconstruction (ASR), which is an in silico method to resurrect extinct ancestors of moder

152 ease Association Prediction (MAP) method, an in-silico method to predict and prioritize miRNA-disease

153 are expected to improve the predictivity of in silico methodologies for allosteric drug discovery an

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

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

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

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

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

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

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

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

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

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

164 Using a quantitative in silico model for the in vivo delivery of genome edito

165 Lastly, an in silico model of p52Shc/p47(phox) interaction using Ro

166 Here, we expand an in silico model of the developing cortical sheet to expl

167 ntly validate this model by showing that the in silico model reproduces much of the behavior that is

168 covery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and human

169 present work describes the development of an in silico model to predict the retention time (t(R)) of

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

171 ls allowed ultimate construction of a single in silico model which consists of data for three differe

172 A series of prodrugs was designed using an in-silico model for prediction of affinity to chylomicro

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

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

175 In silico modeling by molecular dynamics simulations pro

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

177 vivo SLiM-dependent proximity labeling, and in silico modeling of motif determinants uncovered unant

178 In silico modeling predicted prolonged intravitreal rete

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

180 nterest using only in vitro measurements and in silico modeling, potentially relating outcomes to mat

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

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

183 In silico modelling indicates altered metabolic fluxes (

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

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

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

187 mary screen to identify inhibitors, building in silico models to characterize inhibitors, and leverag

188 monstrate that fusing experimental cues with in silico models, based on known biochemistry, can contr

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

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

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

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

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

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

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

196 g convolutional filters, attention maps, and in silico mutagenesis.

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

198 developed NLR-Annotator, a software tool for in silico NLR identification independent of transcript s

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

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

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

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

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

204 r Research, Hanrahan and colleagues adopt an in silico platform to attempt to distinguish benign MEK

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

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

207 Using in silico prediction combined with experimental validati

208 ity purification (DAP) sequencing coupled to in silico prediction of binding syntaxes to study severa

209 In silico prediction of drug-target interaction can spee

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

211 In silico prediction of specific LMNA mutant-driven chan

212 In silico prediction of variant function was performed w

213 common PPPCD phenotype and was predicted by in silico prediction tools to be damaging to protein fun

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

215 In silico predictions of protein interactions entail sam

216 Mutation Score (MMS) developed by combining in silico predictions of stability, evolutionary conserv

217 In silico predictions revealed that kinase/substrate rel

218 In silico predictions suggest half of Lu. longipalpis sa

219 ulation of nanostructured surfaces providing in silico predictions, complemented with time-lapse fluo

220 In silico predictions, minigene splicing assays, patient

221 Once trained, Akita enables rapid in silico predictions.

222 An in silico promoter analysis helped identify a putative r

223 We also conducted enrichment analyses and in silico protein-protein interaction networks to explor

224 In in silico protein-to-protein networks, we observed key p

225 status in patients with dengue and performed in-silico protein structural analysis to identify epitop

226 ffold 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

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

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

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

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

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

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

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

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

236 ossible cadherin arrangements and perform an in silico screening according to biophysical and structu

237 In silico screening identified compounds that elicit tra

238 a very potent modeling framework to develop in silico screening protocols able to simulate phenotypi

239 y inhibitors and developed a high-throughput in silico screening strategy against homeodomain of MEIS

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

241 In silico searches identified additional motif instances

242 nents of the pathway were identified through in silico sequence comparison, however, a functional hom

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

244 approach where living cells interact through in silico signaling, establishing a new testbed to inter

245 he 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 allel computational advances have shown that in silico simulations can predict drug effects with high

248 These trends were consistent with in silico simulations demonstrating that when only one o

249 onal and mathematical modeling, which blends in silico simulations with molecular and evolutionary pr

250 Using FRAP combined with in silico simulations, we find that the lower membrane d

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

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

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

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

255 In silico spectral library prediction of all possible pe

256 culosis isolates for species identification, in silico spoligotyping, detection of mutations associat

257 We used in silico stochastic simulation of future hybrid perform

258 An in silico structural approach based on docking simulatio

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

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

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

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

263 In silico studies and structural comparisons identify es

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

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

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

267 For the in silico study, we simulate single cells from TF/pathwa

268 This comprehensive in-silico study will help to understand how curcumin ind

269 Using an in-silico subcellular model of rabbit ventricular myocyt

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

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

272 review we collectively describe the field of in silico target prediction in the course of time and po

273 e human genomics and proteomics data to make in-silico target identification, reducing the cost and t

274 etric, scattering, electron microscopic, and in silico techniques, we demonstrate that the two peptid

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

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

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

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

279 An in silico theoretical hydrolysis of amandin subunits cor

280 rendering of our methodology that creates an in silico three-dimensional library of composite peptidi

281 matically design diverse candidate lifeforms in silico to perform some desired function, and transfer

282 vestigate M305L actin in vivo, in vitro, and in silico to resolve emergent pathological properties an

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

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

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

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

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

288 rmation reactions were considered using five in silico tools.

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

290 In silico treatments of neural activity are an important

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

292 In silico trials innovations represent a powerful pipeli

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

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

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

296 l components', multidimensional summaries of in silico variant annotations.

297 y we attempted to overcome the limitation of in silico virtual screening by applying a robust in sili

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

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

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