ライフサイエンスコーパス: prediction accuracy

1 le genome prediction methods (74% versus 83% prediction accuracy).

2 oosting (for width), and random forests (for prediction accuracy).

3 methods in both computational efficiency and prediction accuracy.

4 d are the key requisite when aiming for high prediction accuracy.

5 nformational changes have a strong impact on prediction accuracy.

6 ping can lead to significant improvements in prediction accuracy.

7 score normalization procedure to improve the prediction accuracy.

8 n by 39 times without causing degradation in prediction accuracy.

9 t omics data to increase metabolic phenotype prediction accuracy.

10 stress-related genes, with markedly improved prediction accuracy.

11 ructure of the unbound antigen, for enhanced prediction accuracy.

12 fragments are useful features for improving prediction accuracy.

13 ith no influence of taxonomic relatedness on prediction accuracy.

14 he two types of features can further improve prediction accuracy.

15 map matrix and, thus, significantly improves prediction accuracy.

16 The ten-fold cross validation shows high prediction accuracy.

17 ndicating their significant contributions to prediction accuracy.

18 ion and validation data sets, providing 100% prediction accuracy.

19 g complementary information greatly improves prediction accuracy.

20 le time points during acute phase to improve prediction accuracy.

21 in increased uncertainty and thus decreased prediction accuracy.

22 nd binding assay) had significant effects on prediction accuracy.

23 th resampling to generate valid estimates of prediction accuracy.

24 teristic (ROC) analysis was used to quantify prediction accuracy.

25 two variants depending on expected fragment prediction accuracy.

26 Our model has good prediction accuracy.

27 rediction schemes that significantly improve prediction accuracy.

28 with cross-species comparison improves gene prediction accuracy.

29 t and developmental delay with more than 90% prediction accuracy.

30 and computational cost, without sacrificing prediction accuracy.

31 ition into consideration helps improving the prediction accuracy.

32 ter than other competing methods in terms of prediction accuracy.

33 ng different methods may greatly improve the prediction accuracy.

34 tential improvement of the alpha-MoRF-PredII prediction accuracy.

35 over-fitting, and may not provide acceptable prediction accuracy.

36 it a second related genome to improve module prediction accuracy.

37 extinction or reconsolidation, depending on prediction accuracy.

38 performed better than others in terms of the prediction accuracy.

39 ene Ontology (GO) data dramatically improves prediction accuracy.

40 reconstruction with quantitative estimate of prediction accuracy.

41 highly polygenic traits, but not genome-wide prediction accuracy.

42 ion codes have not yet been tested for their prediction accuracy.

43 the multiple traits together may improve the prediction accuracy.

44 chosen from the reference set confirmed high prediction accuracy.

45 All methods resulted in almost equal prediction accuracy.

46 rent techniques be used together to maximize prediction accuracy.

47 eal-time polymerase chain reaction with high prediction accuracy.

48 asured by the IDP coefficients and incidence prediction accuracy.

49 -validation methods were applied to quantify prediction accuracy.

50 is a flexible and powerful tool to maximize prediction accuracy.

51 lected on Escherichia coli and achieved high prediction accuracies.

52 gnificant increase in average PDR values and prediction accuracies.

53 la: see text] at k = 6 gave the highest host prediction accuracy (33%, genus level) with reasonable c

54 Phase space area not only had a high outcome prediction accuracy (80-93%, p < .05) during 85-190 mins

55 PRC2 occupancy showed improved performance (prediction accuracy, 81-88%).

56 e of poly(A) sites, and yet, produces better prediction accuracy across diverse species.

57 Both high prediction accuracy and ability to correctly rank identi

58 ting state-of-the-art algorithms in terms of prediction accuracy and biological significance of the p

59 SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in ident

60 tein localization prediction algorithms, the prediction accuracy and coverage are still low.

61 off value that modulates the balance between prediction accuracy and coverage of the retrieved pairwi

62 s were treated independently suffer from low prediction accuracy and difficulty of biological interpr

63 grating host and HBV profiles have excellent prediction accuracy and discriminatory ability.

64 n improved transcription factor binding site prediction accuracy and dramatically reduced computation

65 We specifically characterize determinants of prediction accuracy and examine the impact of annotation

66 The algorithm substantially improves the prediction accuracy and extends the scope of applicabili

67 ons can be used to further improve the spike prediction accuracy and generalization performance of th

68 Both the prediction accuracy and interpretability of a classifier

69 ation models for HbA1c show a high degree of prediction accuracy and precision--with a limit of detec

70 ta show that our method is superior, both in prediction accuracy and relevant feature discovery, to e

71 hat maximize statistical power, provide high prediction accuracy and run in a computationally efficie

72 The overall prediction accuracy and sensitivity is 71% and 76%, resp

73 sum to the seven-gene classifier raised the prediction accuracy and sensitivity to 83% and 76% respe

74 Systematic comparison of the prediction accuracy and specificity of the different int

75 We discuss avenues for improving prediction accuracy and speculate on the possible use of

76 BE-IDC accommodates both the prediction accuracy and the computational speed that are

77 a strong relationship between IAS values and prediction accuracy, and define a range of IAS values fo

78 However, most of them show limited prediction accuracy, and the number of common predicted

79 ly affecting model performances reveals that prediction accuracies are most strongly influenced by th

80 r, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumo

81 Type 1 Diabetes Consortium, and disease risk prediction accuracies as given by top ranked SNPs by the

82 d the Jack-knife simulation methods with the prediction accuracy as 93% and 92%, respectively.

83 ineering and filtering steps using phenotype prediction accuracy as a metric.

84 scale drug discovery test dataset equivalent prediction accuracy as a random forest.

85 ch significantly improves on microRNA-target prediction accuracy as assessed by both mRNA and protein

86 t PI-LZerD consistently improves the docking prediction accuracy as compared with docking without usi

87 (i) semi-supervised classification improved prediction accuracy as compared with the state of the ar

88 tric constant parameter on the out-of-sample prediction accuracy as measured by cross-validation.

89 TurboFold II also has comparable structure prediction accuracy as the original TurboFold algorithm,

90 own catalytic residue predictors can improve prediction accuracy as well as provide improved ranked p

91 uences, our model significantly improves the prediction accuracy at each of the three steps.

92 The best prediction accuracy AUC = 78.2% (95% confidence interval

93 This model has demonstrated highly stable prediction accuracy (averaged at 99.81%) over three inde

94 ction is to overcome the cross-validated 80% prediction accuracy barrier.

95 ependence methods increased cross-validation prediction accuracies by up to 3.6% compared to their cl

96 s while performing PCA and also improved the prediction accuracy by 34% when using linear discriminat

97 of a subject's reference sample can improve prediction accuracy by as much as 14 %, for the H3N2 coh

98 he statistical framework of ESG improves the prediction accuracy by iteratively taking into account t

99 In this way, Multilign improves prediction accuracy by keeping the genuine base pairs an

100 genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of va

101 supervised learning further improves contact prediction accuracy by making use of sequence profile, c

102 ses in a single-pass screen and confirm high prediction accuracy by means of orthogonal, secondary va

103 In addition, higher prediction accuracies can be obtained by performing base

104 would be possible, for which cases, and what prediction accuracy can be achieved, are currently open

105 prediction tool, HApredictor, showed disease prediction accuracy comparable to other publicly availab

106 r deep 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conve

107 cluded several other brain regions increased prediction accuracy compared with insula-based model alo

108 s disease and show that it leads to improved prediction accuracy compared with single-point analyses.

109 At a given prediction accuracy, computational time is over 10 times

110 ng multiple random effects, we show that the prediction accuracy could be further improved.

111 rs' performance was evaluated in terms of PS prediction accuracy, covariate balance achieved, bias, s

112 than established risk scores with increased prediction accuracy (decreased Brier score by 10%-25%).

113 Also, in patients where the prediction accuracy did not increase significantly, a be

114 Prediction accuracy differed and was better for out-of-h

115 ed with environmental co-variables gave high prediction accuracy due to high genetic correlation betw

116 This model is capable of improving prediction accuracy due to the tolerance of the noise de

117 r organelles were resolved, with exceptional prediction accuracy (estimated at >92%).

118 uantification within all biofluids with high prediction accuracy (expressed as root-mean-square error

119 Prediction accuracies for nSNPs show opposite patterns,

120 The prediction accuracies for the 32 serotypes ranged from 6

121 We showed that the prediction accuracy for a low-heritability trait could b

122 RTK II [P = .01]) than prediction by chance; prediction accuracy for all other molecular parameters w

123 em cell SELEX-Seq data, MPBind achieved high prediction accuracy for binding potential.

124 the data of this training set indicated high prediction accuracy for biomass yield.

125 LOCALIZER shows greater prediction accuracy for chloroplast and mitochondrial ta

126 g multiple traits together could improve the prediction accuracy for correlated traits.

127 ure; this result demonstrates that structure prediction accuracy for globular proteins is limited mai

128 Prediction accuracy for Hginorg and HgDOM strongly depen

129 Generally, the prediction accuracy for protein-protein interactions is

130 nning economic traits, and provide desirable prediction accuracy for quantitative traits, with univer

131 variate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder,

132 R-146b alone was found to have the strongest prediction accuracy for stratifying prognostic groups at

133 rotein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperfor

134 and tongue cancer datasets lead to improved prediction accuracy for the metastasis of primary cervic

135 Sequence prediction accuracy for these genes is 96.26% on average

136 edictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for

137 s, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline

138 man miRNAs as samples, our method achieved a prediction accuracy greater than 95%.

139 ccuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues

140 However, the DeltaDeltaG prediction accuracy has only a marginal dependence on th

141 Significant improvements in prediction accuracy have recently been demonstrated thou

142 hese individual measures to achieve a higher prediction accuracy (i.e. greater than 90%).

143 The prediction accuracy improved when they were combined in

144 hly between nearby residues, functional site prediction accuracy improves.

145 We find that prediction accuracies in excess of 80% of the theoretica

146 ent task contrasts or data sources increased prediction accuracies in some but not all cases.

147 segment level, we achieved up to 0.75 in AUC prediction accuracy in a 10-fold cross validation study

148 MFA/UF is shown to markedly improve flux prediction accuracy in a simulation model of gluconeogen

149 ods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and re

150 is experiment establishes a baseline of gene prediction accuracy in Caenorhabditis genomes, and has g

151 n conjunction with deep phenotyping improves prediction accuracy in cardiovascular event prediction i

152 ads to larger training datasets and improved prediction accuracy in phenotype prediction.

153 complexes; (2) MD-MM/PBSA provided the best prediction accuracy in terms of clustering favorable and

154 at the HMMs proposed demonstrate a very good prediction accuracy in terms of controlling both the fal

155 Our algorithm has also achieved similar prediction accuracy in the Bacillus subtilis genome, sug

156 actions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by inc

157 To improve the prediction accuracy in the regime where template alignme

158 s a priori information could further improve prediction accuracy in the reperfusion group.

159 This approach improves prediction accuracy, in a statistically significant way,

160 but also provides smaller models with better prediction accuracy, in comparison to several alternativ

161 establishing standards for the evaluation of prediction accuracy, in fostering advancements and new i

162 On average, prediction accuracy increases with network size, suggest

163 ules are continually modified to improve the prediction accuracy; increasing rule stringency can impr

164 and may result in substantial improvement in prediction accuracy, irrespective of which peak calling

165 e overly general yet not incorrect, reaction prediction accuracy is 82.5%.

166 ts of sequences with known structure and its prediction accuracy is among the best of available algor

167 For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy

168 od is only slightly reduced when the contact prediction accuracy is comparatively low.

169 The prediction accuracy is further corroborated using 3,809

170 SEPIa's average prediction accuracy is limited, with an AUC score (area

171 .02), no significant genetic contribution to prediction accuracy is observed.

172 The extent to which this variability in prediction accuracy is related to differences in samplin

173 Especially, the prediction accuracy is substantially improved by the inc

174 native conformations may not be examined and prediction accuracy may be compromised due to sampling.

175 gnificant progress has been made in terms of prediction accuracy, most computational methods only pre

176 s and identifies features that contribute to prediction accuracy: neighboring CpG site methylation, C

177 The prediction accuracies obtained by using the fuzzy rule-b

178 brane proteins, the average top L long-range prediction accuracy obtained by our method, one represen

179 The prediction accuracy obtained with tiered learning was fo

180 Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal e

181 oteins of diverse architectures and achieves prediction accuracies of 90% on a manually curated datab

182 Prediction accuracies of each method were also evaluated

183 In this work, it is demonstrated that the prediction accuracies of methods correlate with each oth

184 e identified which achieved 90%, 80% and 60% prediction accuracies of necrosis against independent te

185 Prediction accuracies of our models, however, have not y

186 ics-assisted breeding models, the better the prediction accuracies of the models and the more useful

187 nary classification, CSmetaPred_poc achieves prediction accuracy of 0.94 on CSAMAC dataset.

188 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases a

189 der stringent cross-validation indicate that prediction accuracy of 72% is possible, while a support

190 distribution of drusen, achieving a zygosity prediction accuracy of 76%, 74%, 68%, and 68%.

191 lation mining techniques produce the highest prediction accuracy of 81% precision with the recall at

192 x, beta-strand and coil) secondary structure prediction accuracy of 82.0% while solvent accessibility

193 ur program (called hdfinder) presently has a prediction accuracy of 86%, as validated with CpG region

194 ese two proteins, the OPLS-DA model showed a prediction accuracy of 91.2%.

195 d as 1251 ms and 1400 ms, respectively, with prediction accuracy of 96.7% (95% confidence interval, 8

196 act of different QTL analysis methods on the prediction accuracy of a cross's superior progeny value.

197 While the prediction accuracy of all ML methods decreased as non-c

198 It aims to improve the prediction accuracy of basic centrality measures.

199 t our approach can significantly improve the prediction accuracy of breast cancer metastasis compared

200 rget traits affected prediction performance, prediction accuracy of complex traits (grain yield) were

201 ent trials were used in this study to assess prediction accuracy of different quantitative traits usi

202 e properties that contribute the most to the prediction accuracy of each ligand were also examined.

203 r results reveal a notable difference in the prediction accuracy of expression levels of transcriptio

204 With this, the prediction accuracy of fruit forms can be further improv

205 Improved inference and prediction accuracy of GBLUP may be achieved by identify

206 ability under stress conditions; and (3) the prediction accuracy of GE models was found to be superio

207 Our approach achieves 92% prediction accuracy of genome-wide methylation levels at

208 on and secondary structure thus improves the prediction accuracy of glycan occupancy at the N-X-T/S c

209 en characterizing binding sites improved the prediction accuracy of homeodomain binding specificities

210 The prediction accuracy of muscle-specific expression on an

211 pping data for a sequence improves structure prediction accuracy of other homologous sequences beyond

212 We illustrate the high prediction accuracy of our framework on synthetic data g

213 sequence identity of 30%, the average domain prediction accuracy of our method is 97% for one domain

214 ller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduc

215 The prediction accuracy of our model is similar to those rep

216 to estimate the temporal transferability and prediction accuracy of our models.

217 twork has resulted in significantly improved prediction accuracy of protein complexes.

218 (4) We increased SNP-based prediction accuracy of quantitative eye colour.

219 demonstrated significant improvements in the prediction accuracy of RNA secondary structure.

220 l results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine

221 ins, showed a significant improvement in the prediction accuracy of species-specific AtSubP over some

222 ed with respect to a cost function, here the prediction accuracy of the BMI.

223 less, it is still challenging to improve the prediction accuracy of the computational methods.

224 ts show that it can considerably improve the prediction accuracy of the five centrality measures indi

225 The gain in prediction accuracy of the multivariate approach is equi

226 Our results show that the prediction accuracy of the proposed method is statistica

227 ty changes for all variants, the qualitative prediction accuracy of the Rosetta program reached 65.3%

228 The global quality prediction accuracy of the tool is comparable to other g

229 Prediction accuracy of this new hybrid method was found

230 In this study, we compared prediction accuracy of three different state-of-the-art

231 ask learning is a novel technique to improve prediction accuracy of tumor classification by using inf

232 ession scoring that vastly enhance minimotif prediction accuracy on a test data set.

233 oxon-HOPACH-RF approach achieved the highest prediction accuracy on the RDX data set.

234 auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the puta

235 tly available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a pa

236 PI-LZerD consistently showed better prediction accuracy over alternative methods in the seri

237 Mutants offers roughly 2-fold improvement in prediction accuracy over existing tools.

238 The leave-one-site-out increased average prediction accuracy over pairwise-site for all the trait

239 s show that the new IE function improves the prediction accuracy over the knowledge-based, statistica

240 upstream parameters) by assessing the model prediction accuracy, parameter identifiability and uncer

241 demonstrate that the ITSS algorithm obtains prediction accuracies (precision 97%, recall 77%) compar

242 ion model on the same trait resulted in good prediction accuracy (r = 0.65) with 42% of the S/F % var

243 -wide association study samples, we attained prediction accuracy (R(2)) of 1.000 and 0.994 for the fi

244 The PLS model produced prediction accuracy (R(2)=0.71, RMSEP=1.33 degrees Brix,

245 SVM based models yield prediction accuracy rates in the range of approximately

246 tions for the test set, indicated by average prediction accuracy rates of 78% and 91% for the binding

247 The prediction accuracy reached 75.5%.

248 Total prediction accuracy reached to 71.8% for amino acid comp

249 ide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases,

250 Moreover, we show that prediction accuracy remains when extrapolating beyond th

251 gnificantly improves the secondary structure prediction accuracy, resulting in Q3 ranging from 67.5 t

252 ength correction produces a 0.976 R(2) value prediction accuracy, significantly higher than the addit

253 me models for disease resistance can produce prediction accuracy suitable for application in breeding

254 Genome-wide prediction accuracies tended to be moderate to high (ave

255 Lower prediction accuracy tends to be associated with insuffic

256 hat the resulting meta-model achieves higher prediction accuracy than either model on its own.

257 multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection

258 ing the HCF approach, we achieve much higher prediction accuracy than the standard BMC method.

259 han humans and increases the crystallization prediction accuracy to 82.4+/-0.7 % over 77.1+/-0.9 % fr

260 pproach provides a framework that will allow prediction accuracy to increase as new studies provide m

261 dependent test set and resulted in a similar prediction accuracy to that obtained using the training

262 A particular focus is the sensitivity of prediction accuracy to the docking geometry of the struc

263 tperform conventional approaches in terms of prediction accuracy, transformation pathway identificati

264 data from multiple time points could improve prediction accuracy under certain ischemic conditions.

265 traits (anthesis date and plant height) and prediction accuracy under stress conditions was consiste

266 l regulatory regions in the genome with >95% prediction accuracy using NFR modules and >85% predictio

267 on type is used as an additional constraint, prediction accuracy usually increases, and is particular

268 High prediction accuracy was achieved (~90%).

269 ul PLS-DA modeling of the data as 100% model prediction accuracy was achieved.

270 ML model prediction accuracy was also compared with that of conve

271 The relative prediction accuracy was approximately 2.4% for a 0.05-1.

272 Out-of-sample prediction accuracy was comparable for both types of ana

273 The improvement in prediction accuracy was consistent between simulated and

274 It was found that the prediction accuracy was enhanced by more than 70% for tw

275 Prediction accuracy was evaluated using the area under t

276 Prediction accuracy was high for most models, with cross

277 The disease prediction accuracy was investigated in a subset of the

278 nding and further demonstrated that callers' prediction accuracy was mediated by citizens' nonverbal

279 Prediction accuracy was shown to improve significantly o

280 Prediction accuracy was similarly high in the independen

281 To further boost the prediction accuracy, we extend dRW to dRW-kNN.

282 rove ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach b

283 ddition to identifying markers and improving prediction accuracy, we show how the integration of exis

284 The prediction accuracies were examined by five-fold cross-v

285 The prediction accuracies were greater than 90% for all 13 f

286 The sensitivity and specificity of prediction accuracy were calculated.

287 fication algorithms were used to compare the prediction accuracies when using fitting coefficients as

288 to investigate the potential improvement in prediction accuracy when incorporating MRI diffusion dat

289 A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent g

290 ein interaction pairs with approximately 94% prediction accuracy when using sequence and experimental

291 rified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives

292 st subset of SNPs that guarantees sufficient prediction accuracy, while also solving the unclear thre

293 n PPI prediction, we are able to obtain high prediction accuracies with 77.6% precision and 84% recal

294 bacterial genomes, [Formula: see text] host prediction accuracies with thresholding and consensus me

295 hods, the proposed method can achieve higher prediction accuracy with fewer genes.

296 n-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, reca

297 odFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhe

298 nimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual p

299 ediction accuracy using NFR modules and >85% prediction accuracy with VEB elements.

300 ividual predictors that improves the overall prediction accuracy, with the in-silico two-hybrid metho