ライフサイエンスコーパス: prediction method

1 ixed model (G-BLUP) and a Bayesian (Bayes C) prediction method.

2 age of these results to develop a structural prediction method.

3 ovement of our IntFOLD-TS tertiary structure prediction method.

4 ure was generated using the nearest template prediction method.

5 atorial counting approach independent of any prediction method.

6 ossible directions for further improving the prediction method.

7 d MapPred, a new deep learning-based contact prediction method.

8 ore (for binding) 1.5-fold over the original prediction method.

9 he precision and relative sensitivity of the prediction method.

10 pace, and machine-learning probability-based prediction method.

11 members, i.e. are too small for such contact prediction methods.

12 esian framework represent the main family of prediction methods.

13 urgent need for unbiased haploinsufficiency prediction methods.

14 likely influenced many previous DeltaDeltaG prediction methods.

15 ith significant improvement over existing MP prediction methods.

16 A target sites and improve miRNA target site prediction methods.

17 ent progress and challenges in RNA structure prediction methods.

18 sted, motivating our efforts to benchmark pI prediction methods.

19 compare against the current leading contact prediction methods.

20 g contact information into protein structure prediction methods.

21 ASP-winning template-based protein structure prediction methods.

22 framework that can be applied to driver gene prediction methods.

23 ve quality of binding predictions over other prediction methods.

24 presents new challenges to protein function prediction methods.

25 ng PconsC in comparison with earlier contact prediction methods.

26 ficant challenge for computational structure prediction methods.

27 and increase the power of protein structure prediction methods.

28 and compared it with the available competing prediction methods.

29 , underlies numerous potential functions and prediction methods.

30 roviding an opportunity to assess and refine prediction methods.

31 creased accuracy versus generic miRNA target prediction methods.

32 rforms the four off-the-shelf subchloroplast prediction methods.

33 a simplified system for testing new affinity prediction methods.

34 gnificantly outperformed alternate, analogue prediction methods.

35 ying the framework to direct protein complex prediction methods.

36 ting phylogenies can be used as features for prediction methods.

37 s substitutes in the absence of good epitope prediction methods.

38 een used to evaluate many other binding site prediction methods.

39 cilitate further development of permeability prediction methods.

40 RNA structure prediction by RNAG over extant prediction methods.

41 ble benchmark compound for crystal structure prediction methods.

42 e future models created by protein structure prediction methods.

43 ent of successful protein tertiary structure prediction methods.

44 bias in estimating the accuracy of function prediction methods.

45 large-scale evaluation of sequence-based SDP prediction methods.

46 the effect scores to evaluate variant effect prediction methods.

47 oes, suggesting limitations with current NES prediction methods.

48 develop and benchmark a variety of different prediction methods.

49 is, in part, because of uncertainty in motif prediction methods.

50 ms of comprehending and developing novel bio-prediction methods.

51 ry drug susceptibility testing and in silico prediction methods.

52 than any features suggested by textbooks and prediction methods.

53 n with the state-of-the-art protein function prediction methods.

54 o commonly used kinase inhibitor selectivity prediction methods.

55 combinations of omic datasets with different prediction methods.

56 significantly outperforms all existing link prediction methods.

57 s and assessing the performance of footprint prediction methods.

58 tic evaluation of ten publicly available AMP prediction methods.

59 ell as the state-of-the-art binding affinity prediction methods.

60 ent, state-of-the-art, local and global link-prediction methods.

61 re dependency that should be considered by a prediction method?

62 re dependency that should be considered by a prediction method?

63 Using computational prediction methods, 20 of the remaining 62 variants were

64 volving the combination of crystal structure prediction methods, ab initio calculated chemical shifts

65 A sequence-based prediction method able to accurately predict the propens

66 On a non-redundant test set, our epitope prediction method achieves 44% recall at 14% precision a

67 It outperforms other prediction methods, achieving an AUC of 0.92 compared to

68 d accuracy is not a property of a particular prediction method: after conditioning on the SDE set, na

69 unction makes correct assessment of function prediction methods an issue of great importance.

70 We utilized an unbiased sub-motif prediction method and reported CW as the representative

71 Meval through evaluation of our SCRMshaw CRM prediction method and training data.

72 he heart of development of protein structure prediction methods and comparison of their performance.

73 In recent years, successful contact prediction methods and contact-guided ab initio protein

74 ndings assess state-of-the-art cancer driver prediction methods and develop a new and improved consen

75 ng a catalog of characteristics about the 98 prediction methods and identifying common and exclusive

76 Both well-established hot spot prediction methods and new approaches to analyze individ

77 ting the knowledge encoded by different sRNA prediction methods and optimally aggregating them as pot

78 f-based modeling is complementary to current prediction methods and provides a promising direction in

79 sers to compare and choose between different prediction methods and provides estimates of the expecte

80 and highlights the need for generalized risk prediction methods and the inclusion of more diverse ind

81 PLEXD with three state-of-the-art expression prediction methods and two novel logistic regression app

82 ing methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy result

83 Risk factor epidemiology, prediction methods, and causal inference strategies are

84 Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently impro

85 on method was developed for is the first PPI prediction method applied on benchmark datasets of Arabi

86 ated procedure for carrying out this epitope prediction method are presented.

87 Theoretical structure prediction methods are an attractive alternative.

88 While numerous prediction methods are available, their performance is i

89 Existing cancer driver prediction methods are based on very different assumptio

90 nd, current computational kinase selectivity prediction methods are computational intensive and can r

91 Existing homology-based structure prediction methods are designed for globular, water-solu

92 of the machine learning based essential gene prediction methods are lack of skills to handle the imba

93 g increasingly commonplace, existing miR-TSV prediction methods are not designed to analyze these dat

94 se are summarized, and their calibration and prediction methods are overviewed.

95 racterized protein sequences, where accurate prediction methods are still required.

96 ditional experimental approaches and current prediction methods are still unreliable.

97 Comp-utational function prediction methods are therefore essential as initial st

98 estigation, semiempirical NMR chemical shift prediction methods are used to evaluate the dynamically

99 d algorithms and that alchemical free energy predictions methods are close to becoming a mainstream t

100 red the accuracies of four genomic-selection prediction methods as affected by marker density, level

101 improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DT

102 shown the comparative effectiveness of each prediction method, as well as provided guidelines as to

103 viously described missense mutation function prediction methods at discriminating known oncogenic mut

104 Our experimental results show that our IDR prediction method AUCpreD outperforms existing popular d

105 ximately 300 GPa using an unbiased structure prediction method based on evolutionary algorithm.

106 ng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regr

107 ene patterns further, we propose an ortholog prediction method based on our gene pattern mining algor

108 A prediction method based on the physicochemical propertie

109 encing Project, we tested the utility of the prediction method based on the ratio of non-synonymous t

110 s paper proposes an efficient blood pressure prediction method based on the support vector machine re

111 network-based inference applications, i.e., prediction methods based on interactomes, that can be us

112 odel refinements and alternate RNA structure prediction methods beyond the physics-based ones.

113 Mapping de novo binding residue prediction methods (BindPredict-CCS, BindPredict-CC) ont

114 r small proteins using the Rosetta structure prediction method, but for larger and more complex prote

115 obustness, we also develop a committee-based prediction method by pooling together multiple personali

116 ecent CASP11 blind test of protein structure prediction methods by incorporating residue-residue co-e

117 stance, we design a new embedding-based link prediction method called global and local integrated dif

118 A novel gene expression prediction method (called TFChrome) using both MTTFsite

119 Although contemporary structure prediction methods can assemble the correct topology for

120 swer comprehensively, while state-of-the-art prediction methods can.

121 tion (BLUP) appears to be the most efficient prediction method compared to the other commonly used ap

122 Our prediction method complements experimental efforts, and

123 While link prediction methods connect proteins on the basis of biol

124 MS will be crucial to improve variant effect prediction methods, data diversity hindered simplificati

125 Most domain-centric function prediction methods depend on accurate domain family assi

126 Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neig

127 er friendly website that gives access to the prediction method devised in this work.

128 efficiency and accuracy of RNA 3D structure prediction methods during the succeeding challenges of R

129 xperiments demonstrated that our three-level prediction method effectively increased the recall of fu

130 r the last decade in the accuracy of epitope prediction methods, especially for those that rely on th

131 vely when compared to other state of the art prediction methods, especially when sequence signal to r

132 The prediction method (EVfold_membrane) applies a maximum en

133 Most prediction methods exploit evolutionary sequence conserv

134 Many prediction methods face limitations in learning from the

135 Available tRNA prediction methods fail to accurately predict tRNASec, d

136 Hence, traditional sequence-based prediction methods focusing on a single residue (or a sh

137 this study, we aimed to develop a genotypic prediction method for antimicrobial susceptibilities.

138 pose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins,

139 We created SIFT Indel, a prediction method for frameshifting indels that has 84%

140 We recently developed a marker-guided prediction method for hybrid yield and showed a substant

141 In this work, we schemed out a new prediction method for low-similarity datasets using redu

142 , has emerged as an alternative to the motif prediction method for the identification of T cell epito

143 We propose a novel prediction method for the prediction of DNA-binding resi

144 y outperformed the existing state-of-the-art prediction method for the same purpose.

145 , we present BOCTOPUS2, an improved topology prediction method for transmembrane beta-barrels that ca

146 A new de novo protein structure prediction method for transmembrane proteins (FILM3) is

147 -specific and whole-protein terms and select prediction methods for different classes of GO terms.

148 is therefore useful to develop computational prediction methods for DNA methylation.

149 m current homology-based secondary structure prediction methods for many proteins.

150 Reliable structure-prediction methods for membrane proteins are important b

151 Here, we extended general function prediction methods for predicting the toxicity of protei

152 standing can be used to design more powerful prediction methods for protein structural class.

153 We study these module prediction methods for simulated benchmark networks as w

154 cdotal due to the requirement of the contact prediction methods for the high volume of sequence homol

155 Compared to previous PPI prediction methods, FpClass achieved better agreement wi

156 We have developed a novel gene prediction method FragGeneScan, which combines sequencin

157 omparison against the sequence-based contact prediction methods from CASP9, where our method presente

158 We show that our prediction method generalizes to pairs of neural oscilla

159 Traditional protein structure prediction methods generally use one or a few quality as

160 ggests a set of peptides for which different prediction methods give divergent predictions as to thei

161 We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly

162 The results showed that the site-prediction methods have a low probability of identifying

163 In this study we show that the sequence prediction methods have accuracies nearly comparable to

164 technologies, computational protein function prediction methods have become increasingly important.

165 Protein function prediction methods have been actively studied in the bio

166 proteins, many computational protein-protein prediction methods have been developed in the past.

167 In recent years many function prediction methods have been developed using various sou

168 Although many deleteriousness prediction methods have been developed, their prediction

169 Machine-learning prediction methods have been extremely productive in app

170 Several previous variant pathogenicity prediction methods have been proposed that quantify evol

171 biological noise, and current computational prediction methods have high false positive rates.

172 d contact-guided ab initio protein structure prediction methods have highlighted the importance of in

173 However, since the first studies contact prediction methods have improved.

174 Recently, several groups using computational prediction methods have independently reported possible

175 Protein tertiary structure prediction methods have matured in recent years.

176 This paper presents a new indel functional prediction method HMMvar based on HMM profiles, which ca

177 This paper proposed a quantitative prediction method, HMMvar, to predict the effect of gene

178 alysis has shown that MMSE has no value as a prediction method in determining minimal HE and in respe

179 and comprehensive assessment of the contact-prediction methods in different template conditions.

180 can greatly outperform the state-of-the-art prediction methods in identifying TISs.

181 By using transmembrane prediction methods in mouse and human orthologs, models

182 n ranks FunFHMMer as one of the top function prediction methods in predicting GO annotations for both

183 ass learners, as well as currently available prediction methods in terms of F1 score, accuracy and AU

184 edictor remarkably outperformed the existing prediction methods in this field.

185 MetaPred2CS integrates six sequence-based prediction methods: in-silico two-hybrid, mirror-tree, g

186 ion) often outperforms a host of alternative prediction methods including random forests and penalize

187 It outperformed several state-of-the-art prediction methods including Rosetta de-novo, MAINMAST,

188 Using six prediction methods, including least absolute shrinkage a

189 Prediction methods indicate that variants in seemingly h

190 The performance of our prediction methods indicates the potential of correlatin

191 e previous version of our tertiary structure prediction method, IntFOLD-TS.

192 We find that the new CRM prediction method is superior to existing methods.

193 The basis for the prediction method is that cancer samples of the same sta

194 iction accuracy of current popular numerical prediction methods is not high.

195 However, current MHC-binding prediction methods lack an analysis of the major conform

196 We test four popular function prediction methods (majority vote, weighted majority vot

197 A new generation of automated RNA structure prediction methods may help address these challenges but

198 nnotations, we used a sequence-based de novo prediction method, MetalDetector, to identify Cys and Hi

199 pared with state-of-the-art coding potential prediction methods, MiPepid performs exceptionally well,

200 tation, we propose and develop a novel miRNA prediction method, miRank, based on our new random walks

201 as the basis for a quantitative miRNA target prediction method, miRNA targets by weighting immunoprec

202 and outperforms state-of-the-art TF binding prediction methods, MocapG, MocapS, and Virtual ChIP-seq

203 lts of our three deep learning-based contact prediction methods (MULTICOM-CLUSTER, MULTICOM-CONSTRUCT

204 he-art non-template-based functional residue prediction methods must predict ~25% of a protein's tota

205 tworks, we propose a novel essential protein prediction method, named SON, in this study.

206 deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging.

207 which makes the development of computational prediction methods of substantial interest.

208 We assessed our prediction method on an independent set of RNA-seq data

209 he accuracies of the three type III effector prediction methods on a small set of proteins not known

210 nt advantages over existing coding potential prediction methods on micropeptide identification.

211 form a comprehensive assessment of 18 driver prediction methods on more than 3,400 tumor samples from

212 gorithm over current top-performing function prediction methods on the yeast and mouse proteomes acro

213 spite significant development in active-site prediction methods, one of the remaining issues is ranke

214 perimental annotation, most protein function prediction methods operate at the protein-level, where f

215 g. obtained as a result of protein structure prediction methods or small molecule docking.

216 Compared to most existing genetic risk prediction methods, our method does not need to tune par

217 ssifiers and show that our cross-sample TFBS prediction method outperforms several previously describ

218 or evaluation that can be applied to any CRM prediction method, particularly a supervised method.

219 iled analysis of two sequence-based function prediction methods, PFP and ESG, which were developed in

220 h the development of a new protein interface prediction method, PredUs, that identifies what residues

221 information predicted by two protein contact prediction methods PSICOV and DNcon to generate a new sc

222 We surveyed nine poly(A) site prediction methods published between 1999 and 2011.

223 ths of most widely used BLAST-based function prediction methods, rarely used in function prediction b

224 sets generated by different RNA 3D structure prediction methods (raw, for-evaluation and standardized

225 The accuracy of a sequence-based antigenic prediction method relies on the choice of amino acids su

226 Traditional RNA function prediction methods rely on sequence or alignment informa

227 Almost all protein residue contact prediction methods rely on the availability of deep mult

228 protein pairs (positive PPIs), computational prediction methods rely upon subsets of negative PPIs fo

229 re presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regul

230 ere, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can

231 The most effective prognostic prediction methods should use all available data, as thi

232 Our prediction method shows an area under the Receiver Opera

233 is information into current RNA 3D structure prediction methods, specifically 3dRNA.

234 tly in models generated by protein structure prediction methods such as Rosetta.

235 In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) re

236 f the model to those obtained through simple prediction methods, such as through an analytical approx

237 Traditional template-based protein structure prediction methods tend to focus on identifying the best

238 iases, we have developed a composition-based prediction method that accurately predicts PrLD assembly

239 A novel de novo protein structure prediction method that combines global exploration and l

240 Here, we propose a polygenic risk prediction method that does not require explicitly model

241 We propose a similarity-based drug-target prediction method that enhances existing association dis

242 iDTI-ESBoost is a novel prediction method that has for the first time exploited

243 Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of

244 ubject to negative selection, we developed a prediction method that measures paucity of non-synonymou

245 on prediction (PFP) is an automated function prediction method that predicts Gene Ontology (GO) annot

246 iDNAProt-ES is a novel prediction method that uses evolutionary and structural

247 e present iDNAProt-ES, a DNA-binding protein prediction method that utilizes both sequence based evol

248 ntegrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to inte

249 Thus, gene prediction methods that explicitly take into account ins

250 nome, as compared with four current function prediction methods that precisely predicted function for

251 In this work, we develop gene expression prediction methods that relax the independence and addit

252 ore, there is a pressing need to develop new prediction methods that use an updated set of 14-3-3-bin

253 ant to developers and users of gene function prediction methods that use gene co-expression to indica

254 the original 3dRNA as well as other existing prediction methods that used the direct coupling analysi

255 , when comparing with known state-of-the-art prediction methods, the accuracy of our method is 6.31-2

256 he opportunity for building binding affinity prediction methods, the accurate characterization of TF-

257 le computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web s

258 limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to t

259 ovides a small sample to train parameters of prediction methods, thus leading to low confidence.

260 wide association analysis and an Elastic Net prediction method to analyze the relationship between DN

261 Moreover, we present a novel flexible prediction method to calculate initial mutant allele fre

262 analytical method and, for the first time, a prediction method to enable enhancement of researches on

263 SPOCS implements a graph-based ortholog prediction method to generate a simple tab-delimited tab

264 ion approach using the proposed binding site prediction method to predict CaM binding proteins in Ara

265 differences and subsequently determine which prediction method to use would require further specifica

266 using an ensemble of two secondary structure prediction methods to guide fragment selection in combin

267 does not respond, and the use of simple risk prediction methods to individualise the amount and type

268 ovide guidance for immunologists as to which prediction methods to use, and what success rates are po

269 Most IDR prediction methods use sequence profile to improve accur

270 Most ligand-binding sites prediction methods use the protein structures from the P

271 lso review many previous treatments of these prediction methods, use the latest available annotations

272 A comparison with other domain prediction methods used in the CASP7 competition indicat

273 The PrISE family of interface prediction methods uses a representation of structural e

274 s, we examined the reliabilities of the site-prediction methods, using nucleotide sequence data for t

275 Using the evolutionary prediction method USPEX, we found stable reconstructions

276 The performance of driver prediction methods varied considerably, with concordance

277 This is the first work where a PPI prediction method was developed for is the first PPI pre

278 ide Predictor (PPTPP), a Random Forest-based prediction method was presented to address this issue.

279 dification sites and state-of-the-art target prediction methods we re-estimate the snoRNA target RNA

280 Based on our function prediction method, we developed a neural network model,

281 Using an established MGW-prediction method, we generated a MGW census for 199 038

282 To demonstrate the generality of the prediction method, we have also applied the method to RN

283 Using this morphology prediction method, we identified two promising molecular

284 en Markov model-based transmembrane topology prediction method, we now propose a comprehensive topolo

285 l sensitivity and specificity of the genomic prediction method were 0.97 (95% confidence interval [95

286 Alternatively, other prediction methods were based on the observation that mi

287 Five dose prediction methods were compared: 2 methods using only c

288 In the case of supervised prediction methods-when training data composed of valida

289 f Inheritance for Nonsynonymous variants), a prediction method which utilizes a random forest algorit

290 o-PFP, a new sequence-based protein function prediction method, which mines functional information fr

291 ave previously developed successful function prediction methods, which were shown to be among top-per

292 years and emerged as a potentially reliable prediction method with reasonable throughput.

293 for development of new multiple localization prediction methods with higher coverage and accuracy.

294 rocess regression with several commonly used prediction methods with simulations.

295 ructure determination accuracies of sequence prediction methods with the empirically determined value

296 olutional neural network (CNN)-based contact prediction methods with three coevolution-based methods

297 incorporates ConFunc, our existing function prediction method, with other approaches for function pr

298 Computer-assisted live-prediction method would be an additional approach to fac

299 alidation of these new generation of epitope prediction methods would benefit from regularly updated

300 parameters used in state-of-the-art membrane prediction methods, yet achieves very high segment accur