ライフサイエンスコーパス: correctly predict

1 ased substantially (46% of the known operons correctly predicted).

2 asuring the fraction of trophic relations it correctly predicts.

3 statistics on L and myosin concentration is correctly predicted.

4 l test set, 70.3% of positive TdP drugs were correctly predicted.

5 Sixteen of the 17 mutants were correctly predicted.

6 s covered with ruthenium monolayers are also correctly predicted.

7 tion, beta-pinene, the signs of [alpha]D are correctly predicted.

8 ain at least one SFP probe, at least 80% are correctly predicted.

9 ides, about 70% of the native base-pairs are correctly predicted.

10 en DNA-BPs with unbounded structures are all correctly predicted.

11 ring calibration and cross-validation and to correctly predict 100% of oxyphil and 99.8% of chief cel

12 d temporal cortex obtained early in recovery correctly predicted 20 of 22 subjects who did not relaps

13 ated pedigrees and three extended pedigrees, correctly predicting 20% more fourth- through ninth-degr

14 The original CHOP ROP model correctly predicted 452 of 459 infants who developed typ

15 ents analysis, the first principal component correctly predicted 46 of 50 cases and 47 of 50 controls

16 genetically unrelated, in simulations, PADRE correctly predicted 50% of 13(th)-degree relationships t

17 asthma duration, and blood neutrophil counts correctly predicted 64% of sputum neutrophil percentages

18 The gene-specific CNV model from AT correctly predicted 67% (P = 0.041) cases for relapse an

19 ally deleted per fold showed that the method correctly predicts 68% of the deleted edges on average.

20 predicted, but not blood eosinophil counts, correctly predicted 69% of sputum eosinophil percentages

21 ne-specific CNV from tumor, the genome model correctly predicted 73% (receiver operating characterist

22 Our method correctly predicts 73% of species richness.

23 edian-sized CNV from tumor, the genome model correctly predicted 75% (P < 0.001) cases for relapse an

24 enzymes, computational sequence optimization correctly predicts 76% of all active-site residues teste

25 cations, our sequence optimization algorithm correctly predicted 78% of residues from all of the enzy

26 Furthermore, the model correctly predicted 79% of the existing shallow groundwa

27 Our models correctly predicted 80 to 90% of elections in out-of-sam

28 The enhanced predictor correctly predicted 80% of the known E.coli TUs (69% of

29 edian-sized CNV from blood, the genome model correctly predicted 81% (P < 0.001) cases for relapse an

30 under constraints on the folding free energy correctly predicted 83% of amino acid residues (94% simi

31 ively accrued validation set, the classifier correctly predicted 88% of responders and 83% of nonresp

32 The model correctly predicts 92% of occurrences observed outside o

33 , served to create a learning algorithm that correctly predicted 96.4% of the samples as either norma

34 optimized and validated acetaminophen model correctly predicted 98.2%, and the ibuprofen model corre

35 tly predicted 98.2%, and the ibuprofen model correctly predicted 99.0% of the urine specimens contain

36 , the results of comparative transcriptomics correctly predicted a 2AA-dependent motility defect and

37 Logistic models correctly predicted a blood lead elevation of >/=20 micr

38 pletion and un-blinding, the biomarker assay correctly predicted a clinical response in over 90% of t

39 A score of 2 or above correctly predicted a low arousal threshold in 84.1% of

40 rmed by surgeons of mixed experience levels, correctly predicted a pathologically negative neck in 96

41 This model also correctly predicts a plateau-like response of translatio

42 GERV correctly predicts a validated causal variant among link

43 ChIP-Seq data in three mouse cell lines and correctly predicted active and inactive promoters with p

44 Models trained on the dataset correctly predict activity of evolutionarily divergent r

45 ladder stones in 57 of 62 patients (94%) and correctly predicted acute cholecystitis in 6 of 8 patien

46 est that additional information is needed to correctly predict Alefacept-mediated bridge formation.

47 FimTyper correctly predicted all 42 fimH subtypes from the Sanger

48 oB and HemN, the multiple sequence alignment correctly predicted all but one of the core helices in H

49 It correctly predicts all mutations (functional and permiss

50 reover, retention of WASP together with SCAR correctly predicts alpha-motility in disease-causing chy

51 r, 45 different serotypes or serogroups were correctly predicted among the 196 resolvable isolates, w

52 In contrast to other models, this model correctly predicts an experimentally observed negative c

53 validation, the EGFR pathway-based signature correctly predicted anti-EGFR treatment response in eigh

54 ructures in the mass range of 120-500 Da and correctly predicted approximately 70% of the individual

55 f four, three of three, and one of two cases correctly predicted as no adhesion, partial adhesion, an

56 the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%,

57 remaining adjustable parameters, the theory correctly predicts aspects of the fracture-resistant, wa

58 Using only ESTs we were able to correctly predict at least one splice form exactly corre

59 y score that predicts the expected number of correctly predicted base pairs.

60 (autopodium present and absent) can often be correctly predicted based on Hoxa-13 sequences.

61 Notably, reaction outcomes were correctly predicted by a simple thermodynamic formalism

62 Seizure outcome following resection was correctly predicted by EEG-fMRI GM in 8 of 20 patients,

63 tumors; 17 (94%) of 18 patients with LS were correctly predicted by IHC.

64 y WBS were negative (47%), all of which were correctly predicted by negative (124)I PET/CT.

65 lue for the fraction of trophic interactions correctly predicted by the ADBM, or any other model, wit

66 ll as the most penetrating particle size are correctly predicted by the model.

67 ler-Lyer stimulus and its major variants are correctly predicted by the probability distributions of

68 keeps 98% of RefSeq gene structures that are correctly predicted by TWINSCAN when removing 26% of pre

69 MRC correctly predicted CBD stones in 16 of the 17 patients

70 The modeling correctly predicted cell lines' growth rates, tumor lipi

71 al groups of input cytokine combinations and correctly predicts cell population response to new input

72 Facilitated-transport model can correctly predict cellular iron efflux and is essential

73 articipate in backbone hydrogen bonding than correctly predicted Coils.

74 roblasts to mechanical cues was critical for correctly predicting collagen alignment in infarct scar.

75 compares well with experimental results and correctly predicts column order and back pressure effect

76 e HOMO-LUMO gap (referred to as M-functions) correctly predicts conductance ratios of molecules with

77 TS predictions together, the total number of correctly predicted contacts in the Hard proteins will i

78 DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global

79 A six-gene model was used to correctly predict dasatinib sensitivity in 11 out of 12

80 s performance (75% of TUs and 65% of operons correctly predicted), demonstrating that the extra infor

81 The meganv2.1 model correctly predicted diurnal variations in fluxes driven

82 We further use our sensor to correctly predict efficient processing of the glycoprote

83 In a proof of concept, our sensor correctly predicts efficient processing of the GPC of th

84 However, current nucleation theories do not correctly predict either the observed nucleation rates o

85 el succeeded in two real-life MetID tasks by correctly predicting elution order of Phase I metabolite

86 tringent specificity level of 99.98%, we can correctly predict enzyme functions for 80.55% of the pro

87 tical model that combines these observations correctly predicts every complete deglaciation of the pa

88 The models were found to correctly predict experimental data and provide an intui

89 Our model correctly predicts experiments near these points and sug

90 this level, favored endo-phenyl isomers are correctly predicted for styrene reactions, but the calcu

91 ensitive to solvation effects, and these are correctly predicted for the first time including those o

92 er predictive values (with >90% of compounds correctly predicted for those classes of interest).

93 Comparative genomics has correctly predicted functions for many such genes by ana

94 t to interpret and highly subjective and can correctly predict furcation invasions only approximately

95 FGENESH, GeneMark.hmm and GENSCAN correctly predicted gene models in 773, 625, and 371 MAG

96 nome assembly and creating a full catalog of correctly predicted genes.

97 efined genome-scale metabolic model that can correctly predict growth viability over 69 source metabo

98 This is critical for correctly predicting growth yields, contrasting multiple

99 at the simplified GC model and the new model correctly predict haemodynamic and renal excretory respo

100 or two imaging parameters (P < .01), thereby correctly predicting histologic results in 95% (18 of 19

101 This novel algorithm is shown to both correctly predict homeostasis in synaptic weights and so

102 rounded in data from birds and mammals, that correctly predicts how growing animals allocate food ene

103 Finally, the model correctly predicts how lesions in the feed forward loop

104 Finally, the model correctly predicts how PPI depends on pulse intensity.

105 nd, however, that: (1) the radical mechanism correctly predicts HR-O3 but vastly overestimates HPR-O3

106 apture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendeli

107 model that makes center-of-gravity fixations correctly predict human eye movements.

108 The SVM classifier can be used to correctly predict human, mouse and rat piRNAs, with over

109 in CYP2C19, as recommended by the FDA, only correctly predicted if a patient would respond to clopid

110 Based on subnetwork connectivity, we can correctly "predict" if a disease is age-related and prio

111 cts essentiality with an accuracy of 83% and correctly predicts improvements in growth under increase

112 t, we used our in vitro and model results to correctly predict in vivo information capacity and inter

113 Graft outcome was correctly predicted in 27 of 29 BKVN patients by either

114 Tumor behavior was correctly predicted in 83% of patients (P = .02).

115 Susceptibility and resistance were correctly predicted in 87% and 95% of cases, respectivel

116 ection of the change in binding affinity was correctly predicted in a majority of the cases, and agre

117 Fetal genotypes were correctly predicted in all cases studied.

118 FA status was correctly predicted in the replication cohort with an ac

119 The in vitro binding assay correctly predicted in vivo uptake in a mouse liver mode

120 This measure correctly predicts in situ hybridization patterns for ma

121 es, part of the observed accommodations were correctly predicted; in two structures, the receptor con

122 These models correctly predicted increased expression of Ech hydrogen

123 Abnormal hippocampal activity correctly predicted individual patient diagnostic status

124 coverage of actual interfaces (percentage of correctly predicted interface residues in actual interfa

125 uracy in predicted interfaces (percentage of correctly predicted interface residues in the predicted

126 The location of the hot spot was correctly predicted irrespective of the protein conforma

127 us electrophilic sites within a molecule and correctly predict isomeric distributions.

128 ortant proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose

129 ecular complexes must all be incorporated to correctly predict large-scale behavior in the actin-base

130 deling receptor flexibility is important for correctly predicting ligand binding, it still remains ch

131 The model correctly predicted MDRR activities for 82.2% of 185 com

132 Overall, IOPTH correctly predicted MGD in only 22%.

133 A dynamic parathyroid CT correctly predicted multiglandular disease in 1 of 7 pat

134 patients (14%), while sestamibi scintigraphy correctly predicted multiglandular disease in 8 of 23 pa

135 sensitive targets of ubiquitin depletion and correctly predict multiple effects of modulating additio

136 Ability of MMRpro to correctly predict mutation carrier status, as measured b

137 The map correctly predicted Nova's effect to inhibit or enhance

138 rate that this extended motif can be used to correctly predict novel targets for SUMO modification.

139 ly, the integrated kinetic model was used to correctly predict observed abundances of H3K27-K36 methy

140 coring system reached an accuracy of 75% for correctly predicting occurrence or nonoccurrence of majo

141 Importantly, this fall correctly predicted operative success in 100% of patient

142 f gland resection and in all cases this fall correctly predicted operative success.

143 eveals a new characteristic time scale which correctly predicts order 10,000-fold speed-up of chemica

144 The model correctly predicted OS distributions in each arm as well

145 udes the elastic energy of the membranes and correctly predicts our findings both quantitatively and

146 When operon structures are correctly predicted, our algorithm can predict 81% of kn

147 rs reported previously, MYCN and CD44, which correctly predicted outcome for 98% of these patients.

148 Luminex serum analysis was able to correctly predict outcomes of 95% of T and B cell FLXM.

149 AMOEBA correctly predicted over 80% of the observed NOEs for al

150 It correctly predicts over 90% of the amino acids and over

151 nalyzed the decoys in terms of the number of correctly predicted pair conformations in the decoys.

152 We termed the fraction of correctly predicted pairs (RMSD at the interface of less

153 works in silico, selecting for networks that correctly predict particular phases of the day under lig

154 MAPPIN also correctly predicts pathogenicity for 87.3% of mutations

155 key features of wild-type pattern formation, correctly predicts patterning defects in multiple mutant

156 oss the modality of reproduction and that it correctly predicted perceptual discrimination.

157 ifying entire kernels based on the number of correctly predicted pixels, improved results were achiev

158 odposin-based receptor homology model, which correctly predicted potent agonism of UDP-fructose, UDP-

159 The probability of correctly predicting presence was low, peaking at 0.5 fo

160 More importantly, this model also correctly predicted previously unidentified binding site

161 This computational method correctly predicts rank order experimental permeability

162 In some cases FABS-NC(') produces over half correctly predicted ranking experiment trials than FABS-

163 ETM correctly predicts regio- and stereoselectivity for a br

164 wo predictive models (with >80% of compounds correctly predicted) resulted in models with even better

165 In the validation set, the model correctly predicted risk category in 52.5% (248 of 472).

166 The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with app

167 ent and permanent repressions, the ENR model correctly predicts several key features of this regulato

168 quired to generate correct auxin patterning; correctly predicts shoot to root auxin flux, auxin patte

169 ne-complex-out cross-validation accuracy and correctly predict SMISPs of known PPI inhibitors not in

170 ected areas in the world, it is important to correctly predict SOC dynamics in salt-affected soils.

171 S-Filter correctly predicts specificity determinants that were de

172 elop an allergy diagnostic method that could correctly predict symptomatic peanut allergy by using pe

173 XenoSite correctly predicts test molecule's sites of glucoronidat

174 tably, in every case, the simulation results correctly predict that a given ligand will bind selectiv

175 Importantly, the results correctly predict that affinity will increase as a resul

176 tumorigenic U87PTEN cells were then used to correctly predict that stable EGFR signaling occurs for

177 We correctly predicted that 8 intensive-care beds and 7 ven

178 Notably, the simulations correctly predicted that blocking PFKFB3 normalizes the

179 specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be

180 The design method also correctly predicted that reusing identical ribosome bind

181 alysis of additional ebolavirus isolates and correctly predicted that the newly identified ebolavirus

182 Importantly, this model correctly predicted that the response magnitude is indep

183 eLAMP correctly predicted that the two tested primer sets woul

184 ulation of the model captures clustering and correctly predicts that (i) essential gene clusters are

185 CBS-QB3 theory alone correctly predicts that acetylnitrene has a singlet grou

186 As such, the model correctly predicts that hippocampal involvement in class

187 ntally determined rates for cargo import and correctly predicts that import is limited principally by

188 In addition, the model correctly predicts that protecting/denaturing osmolytes

189 It also correctly predicts that responses to small stimuli grow

190 veated ideal observer with a central scotoma correctly predicts that the human optimal point of fixat

191 The model correctly predicts that the regiochemistry for R = OMe,

192 the experimentally validated in silico model correctly predicts that they are not.

193 The theory also correctly predicts that urgency signals in the brain sho

194 idation, we show that the resulting networks correctly predict the (de)-activated functional connecti

195 Notably, the classifier could correctly predict the cancer type in non-TCGA datasets f

196 These structures correctly predict the critical residues for binding dopa

197 t an alarming rate, highlighting the need to correctly predict the development of this disease in ord

198 t-descent paths in mass-weighted coordinates correctly predict the direction of the isotope effects,

199 l-Marcus (RRKM)-master equation calculations correctly predict the direction of the trend in selectiv

200 ns, which are based on the stalk hypothesis, correctly predict the effects of both membrane curvature

201 d, partially loop-inserted, prelatent state; correctly predict the effects of PAI-1 mutations on the

202 Quantum-chemical calculations correctly predict the experimental spectroscopic respons

203 intensities and habitat matches are able to correctly predict the identity of the next invading mari

204 on of these lineage-specific microRNAs could correctly predict the lineage of B-cell malignancies in

205 Theoretical calculations correctly predict the most active catalyst and suggest t

206 tive agreement with experimental results and correctly predict the negative response behavior observe

207 rved kinetic features of clozapine block and correctly predict the overall affinity and apparent nons

208 ke-Ernzerhof exchange correlation functional correctly predict the palladium porphine (PdP) low-spin

209 Computations correctly predict the preferred site of attack observed

210 le elevated MMP-13 serum levels were able to correctly predict the presence of active bone disease.

211 interactions between birds are sufficient to correctly predict the propagation of order throughout en

212 Bailar and Ray-Dutt transition states, which correctly predict the relative kinetic barriers of compl

213 d microwave conductivity measurements, which correctly predict the relative magnitudes of the convers

214 d on an idealized model beta-hairpin peptide correctly predict the vibrational coupling patterns.

215 Here we show that our method correctly predicted the absence of El Nino events in 201

216 ites at Gusev crater and at Meridiani Planum correctly predicted the atmospheric density profile duri

217 dentified unique transcriptional codes which correctly predicted the cause of many congenital disorde

218 The four-body scoring function correctly predicted the changes to the stability of 169

219 The DAT correctly predicted the development, or not, of AHA afte

220 Computational models correctly predicted the diastereoselectivity of antagoni

221 pling in governing phosphorylation potential correctly predicted the directional changes in ATP/ADP/P

222 modeling successfully accounted for PheB and correctly predicted the dynamics of a Tat mutant that we

223 Third, the model correctly predicted the dynamics of extracellular lactat

224 combined method, CryptSplice, identified and correctly predicted the effect of 18 of 21 (86%) known s

225 ered from the initial clinical diagnosis and correctly predicted the eventual diagnosis as the clinic

226 The coculture model correctly predicted the exchange of both H2 and formate

227 This signature gene set correctly predicted the four independent patients as wel

228 This method correctly predicted the genetic basis for strain-specifi

229 d as "Tolerant" or "Benign." Both algorithms correctly predicted the impact of 26 functionally charac

230 This gene expression profile correctly predicted the in vitro and in vivo lymphoid-ho

231 The model correctly predicted the key PAP-1-sensing residues in th

232 fier based on this gene expression signature correctly predicted the likelihood of progression of sup

233 The AMD pharmacokinetics model correctly predicted the measured serum ranibizumab conce

234 The ANN correctly predicted the N stage in 99.2% of cases, compa

235 alidation on proteins of known structure, we correctly predicted the number of domains in 69% of all

236 The model correctly predicted the observed fractionation of petrol

237 Furthermore it correctly predicted the outcome for 85/102 (83%) NB pati

238 a set in a leave-one-out prediction strategy correctly predicted the outcome for 90% of the samples.

239 l accurately reproduced these data, and also correctly predicted the possible emergence of a split sl

240 tion function of its five isoforms, but also correctly predicted the precise direction of the regulat

241 and high-likelihood PAS and Alvarado scores correctly predicted the presence of appendicitis in 61.1

242 With an accuracy of 77%, the model correctly predicted the presence/absence of an alien spe

243 all but one of the core helices in HemN, and correctly predicted the residues in the enzyme active si

244 Finally, the model correctly predicted the SCN lesion phenotype in squirrel

245 The docking of the high-energy intermediates correctly predicted the stereoselectivities for 18 of th

246 We correctly predicted the structure of a N-aryl peptoid tr

247 olving populations, we showed that the model correctly predicted the success of the two most benefici

248 Fourth, the model correctly predicted the temporal dynamics of tissue lact

249 On most trials, the cue correctly predicted the upcoming stimulus.

250 An additional pre-cue (S1) correctly predicted the weight in 75% of the trials.

251 ategies showed a very poor performance of in correctly predicting the considered parameters within th

252 faithfully captures the scaling behaviour by correctly predicting the critical exponent of the dynami

253 Correctly predicting the disulfide bond topology in a pr

254 In addition to correctly predicting the divergent pattern of cell sizes

255 buting factors in the hydantoin series while correctly predicting the experimentally observed oxidati

256 pful clinical algorithm to aid clinicians in correctly predicting the genetic basis of various forms

257 We confirmed this by correctly predicting the LDC activity of a DABA DC homol

258 tween mouse lines and validated the model by correctly predicting the repeat length of a blinded mous

259 This model also correctly predicts the actions of etomidate on GABA(A) r

260 The convergent rule correctly predicts the affected loops in every case.

261 compete profiles for diverse RBPs, our model correctly predicts the binding affinities of held-out pr

262 r-law response theory with a plastic element correctly predicts the cell behaviour under cyclic loadi

263 cytes, HEK293 cells and hippocampal neurons; correctly predicts the dose-dependent activation of GIRK

264 d activation; and (5) the mathematical model correctly predicts the existence of at least one protein

265 the observed behavior, our stochastic model correctly predicts the experimental dynamics without any

266 ed spherical cutoff method conserves energy, correctly predicts the experimental helical content, and

267 The present model correctly predicts the experimentally observed two-state

268 The model correctly predicts the expression patterns of mutations

269 A CFD model with a flagellar vane correctly predicts the filtration rate of D. grandis, an

270 An algorithm implemented in Rosetta correctly predicts the folding capabilities of the 17-re

271 A minimal model correctly predicts the increased gene expression variabi

272 The model correctly predicts the knockout fitness effects in 88% o

273 stabilization, and we show that this method correctly predicts the location of a stabilizing PEGylat

274 e of tetracyanoethene, the maximin principle correctly predicts the most common dimer crystal packing

275 The model correctly predicts the observed honeycomb architecture o

276 segregation based on conformational entropy correctly predicts the positioning of the replication te

277 The putative W chromosome haplotype correctly predicts the sex of 97% of male and 85% of fem

278 Our classifier correctly predicts the SF2 value in 22 of 35 cell lines

279 two field sites demonstrate that our theory correctly predicts the size of the smallest valleys with

280 As a result, our method correctly predicts the spatially dependent diffuse refle

281 Although this magnetostrophic theory correctly predicts the strength of Earth's magnetic fiel

282 We show that the KPL-IFF model correctly predicts the T-cell response to antigen copres

283 The model correctly predicts the time to exclusion observed in exp

284 Mogrify correctly predicts the transcription factors used in kno

285 Furthermore, the model correctly predicts the transcriptional dynamics of cells

286 x mechanism using a computational model that correctly predicts the wild-type dynamics of BR expressi

287 The model correctly predicted these results and outperformed an al

288 Finally, modulatory profiling of compounds correctly predicted three previously uncharacterized com

289 The Gusev crater site was correctly predicted to be a low-relief surface that was

290 tumor measurements initially increased were correctly predicted to be responders with SHAPE and subh

291 Strikingly, 55% of the patients could be correctly predicted to have recurrence 13 months (on ave

292 Vessel tortuosity measurements enabled us to correctly predict treatment failure 1-2 months earlier t

293 mbination therapies is therefore crucial for correctly predicting treatment outcomes.

294 the test set of TATA promoters, the program correctly predicted TSS for 35 out of 40 (87.5%) genes w

295 DGE-NET correctly predicts various drug-disease indications by l

296 For this test set, our algorithm correctly predicts water mediation/displacement in appro

297 For this training set, our algorithm correctly predicts water mediation/displacement in appro

298 Using this information we correctly predicted whether any given topography within

299 intervals with digital chromoendoscopy were correctly predicted with >90 % accuracy.

300 tions of the selected side chains were often correctly predicted within crystallographic error.