ライフサイエンスコーパス: prediction

1 amily Study followed by in silico functional prediction.

2 of each mutation accumulates and undermines prediction.

3 tool for improvement of phosphorylation site prediction.

4 dentified as the best cutoff values for MACE prediction.

5 peline to enable prenyltransferase substrate prediction.

6 nction for transcription factor binding site prediction.

7 lation into nonanalog conditions affects the prediction.

8 r method significantly better than the naive prediction.

9 rospectively derived a novel score for VLRAF prediction.

10 dues is indeed useful in DNA-binding residue prediction.

11 ics on accuracy of periodontitis development prediction.

12 e profiling, RNA-seq analysis and RNA target prediction.

13 r in silico structural variation (SV) impact prediction.

14 oxidation peak of MOR coincide well with DFT predictions.

15 r generalization capability to out-of-sample predictions.

16 compound in agreement with quantum chemical predictions.

17 from both co-evolution and machine learning predictions.

18 CD) and discontinuous domain (DCD) structure predictions.

19 nique, providing uncertainty associated with predictions.

20 rvations and to give experimentally testable predictions.

21 ong variability, in agreement with the model predictions.

22 ee crystallographic poses differed from both predictions.

23 er year, roughly consistent with theoretical predictions.

24 nd transmission matching classical ballistic predictions.

25 n sample preparation and varying theoretical predictions.

26 to provide context and enhance precision of predictions.

27 other genes that had no or vague functional predictions.

28 t produce significantly more accurate growth predictions.

29 which significantly improves the quality of predictions (20% increase in sensitivity) in patient sub

30 Considering only top-ranked de novo predictions, 70% of the pairs were deciphered correctly.

31 Is an alteration in early prediction abilities the common cause?

32 Given the proliferation of cataclysmic predictions about antibiotic resistance, cases of which

33 ility of regions of each protein that led to predictions about each's role in tubulin folding.

34 Specifically, it suggests implausible predictions about emotional distancing caused by art sch

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

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

37 methods in both computational efficiency and prediction accuracy.

38 Evaluation of predictions against corresponding experimental data show

39 These predictions agree with experimental observations, thereb

40 In this longitudinal analysis, we built a prediction algorithm for global cognitive impairment (de

41 onservation scores: including 13 genome-wide prediction algorithms and conservation scores, 12 non-sy

42 The server summarizes several in silico prediction algorithms and conservation scores: including

43 s and conservation scores, 12 non-synonymous prediction algorithms and four cancer-specific algorithm

44 from allele frequency databases, consequence prediction algorithms, or genomic datasets can be integr

45 cy to other state-of-the-art DNA methylation prediction algorithms.

46 Our theoretical predictions also indicate that high magnesium ion mobili

47 Additionally, prediction analysis showed inhibition of proinflammatory

48 ectrophysiological recordings confirmed this prediction and also showed gating alterations, a reduced

49 uated the effect of subsampling bootstrap on prediction and computational parameters.

50 ctivation in right Crus I/II during semantic prediction and enhanced resting-state functional connect

51 there are a number of gaps between making a prediction and making a decision, and underlying assumpt

52 izing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understandi

53 Independent validation of the prediction and prognostic performance of the models was

54 By means of in silico prediction and subsequent functional validation, we were

55 rver, namely FUEL-mLoc, using eature- nified prediction and xplanation of m ulti- oc alization of cel

56 nome mining methods, antibiotic gene cluster predictions and 'essential gene screening' to provide an

57 Secretome-wide predictions and confocal microscopy reveal that rust fun

58 udies with purified proteins validate our MD predictions and corroborate the conclusion that this can

59 Here, using multiscale MD simulation-based predictions and FRET sensor-based experiments, we invest

60 owever, these approaches depend on structure predictions and have limited accuracy, arguably due to o

61 nt pain processing as disturbed weighting of predictions and prediction errors.

62 from limited accessibility to the resulting predictions and reduced biological interpretability.

63 entive scheme that rewards accurate minority predictions and show that this produces optimal diversit

64 idue contact prediction, secondary structure prediction, and fold recognition.

65 MN as a biomarker for individual assignment, prediction, and/or monitoring of patient response to pro

66 quency, mutation hotspot residues, in silico predictions, and functional assays were all informative

67 a, either from ChIP-seq experiments or motif predictions, and outputs active TF-gene links as well as

68 Contrary to theoretical predictions, and to phenotypes observed in eudicot leave

69 today's methods for residue-residue contact prediction are based entirely on Direct Coupling Analysi

70 Our model predictions are validated against experimental (1)H NMR

71 These predictions are validated in a spiking neural network mo

72 dimensional cardiac motion improved survival prediction (area under the receiver operating characteri

73 Our results support the idea of prediction as a unifying cerebellar function in motor an

74 s data scattering and significantly improved prediction bands and uncertainties.

75 by combining sensory evidence with internal predictions based on available prior knowledge.

76 d find that they do not consistently support predictions based on the higher predictive ability of pr

77 C results, with an average <9% difference in predictions between the two analytical approaches across

78 utational methods have been proposed for the prediction, but most of them do not consider the relatio

79 compared in terms of the precision of their predictions, but there is no study that compares their u

80 we increased the accuracy of the classic BL predictions by 7%.

81 omposition rates in drylands are higher than predictions by biogeochemical models, which are traditio

82 We cross-validated these predictions by reconstituting the binding and kinetics o

83 operties, experimental verification of these predictions can be obstructed by the challenge in thin f

84 based methods in NCI-DREAM drug sensitivity prediction challenge.

85 score vs age for incident ASCVD and how risk prediction changes by adding CAC score and removing only

86 hat Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2

87 allenging yet important problem since such a prediction could be used to deduce not only a compound's

88 er-order brain areas compare their inputs to predictions derived from higher-order representations an

89 enotypes can facilitate new approaches to BP prediction, diagnosis, and prevention.

90 mpact of using cystatin-C-based eGFR in risk prediction equations for CKD progression and all-cause m

91 ught to encode novelty in addition to reward prediction error (the discrepancy between actual and pre

92 li and food outcomes and computes a negative prediction error in Pavlovian conditioning.

93 that this is mediated by the suppression of prediction error processing through the prefrontal corte

94 roup exhibited greater brain response 1) for prediction error regression within the caudate, ventral

95 Vulnerability modulated the expression of prediction error responses in anterior insula and insula

96 putational model that was adapted to test if prediction error valence influences learning.

97 Consistent with the "prediction error" hypothesis, activation was significant

98 nterval duration, and doesn't reflect reward prediction error, timing, or value as single factors alo

99 attribute this finding to a positive reward prediction error, whereby the animal perceives they rece

100 resentations and signal their deviation as a prediction error.

101 show parallel encoding of effort and reward prediction errors (PEs) within distinct brain regions, w

102 timized to dissociate the subtypes of reward-prediction errors that function as the key computational

103 Using sensory preconditioning, we show that prediction errors underlying stimulus-stimulus learning

104 the opposite valence-induced bias: negative prediction errors were preferentially taken into account

105 It has been suggested that social prediction errors-coding discrepancies between the predi

106 the discontinuation of reinforcement through prediction errors.

107 ng as disturbed weighting of predictions and prediction errors.

108 uracy for classification as well as severity prediction far exceeds any other approach in this field

109 edge graph representing problems of function prediction, finding candidate genes of diseases, protein

110 RACER achieves accurate native structure prediction for a number of RNAs (average RMSD of 2.93 A)

111 thermodynamic model offers the correct null prediction for epistasis between mutations across DNA-bi

112 GRO/PRO-seq data, and provides accurate TSS prediction for human intergenic miRNAs at a high resolut

113 nanoresonators according to the theoretical prediction for the BIC mode.

114 The model prediction for the most tortuous path (s = 395 nm) is re

115 We validate our predictions for 19 compounds using phonon calculations,

116 ions, can provide accurate thermal stability predictions for a wide range of biologically relevant sy

117 Here we generate testable predictions for coupling versus uncoupling of phenotypic

118 om V2 in a different task, deviated from the predictions for optimal linear readout of a population w

119 distances of about 1.1 A, which are close to predictions for solid atomic metallic hydrogen at these

120 data from our institution with estimates and predictions from 10 major international scientific artic

121 t biologists to unlock powerful global plant predictions from a handful of open-access field measurem

122 In agreement with predictions from determinations in vitro, we discovered

123 iously acquired on Q-TOF platforms, matching predictions from known protein interface information.

124 In addition, IslandViewer's integrated GI predictions from multiple methods have been improved and

125 ne of the main sources of uncertainty in SOC predictions from the process-based SOC models.

126 enome-wide association study (GWAS), genomic prediction (GP) is typically based on models incorporati

127 However, most studies of link prediction have focused on social networks, and have ass

128 In silico target prediction identified that insulin-like peptides 7 and 8

129 performance of the features based on contact prediction illustrates the value of using contact inform

130 tures (AUC = 0.82; 95% CI: 0.66-0.94) in ARF prediction improved performance over preoperative featur

131 imizing Risk Assessment and Therapy Response Prediction in Early Breast Cancer) compares pathologic c

132 tor of immunotherapy response and assess its prediction in genomic data from 10,000 human tissues ac

133 anistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not use

134 tion interval, 17 411 to 32 788) and 27 413 (prediction interval, 15 188 to 37 734) excess acquisitio

135 Bernardino shootings, there were 25 705 (95% prediction interval, 17 411 to 32 788) and 27 413 (predi

136 Fold prediction is computationally demanding and recognizing

137 er, clear-cut experimental evidence for this prediction is lacking.

138 Evolutionary prediction is of deep practical and philosophical import

139 The second prediction is that iron-related proteins are dramaticall

140 sequence-based transcription factor binding prediction led to the identification of Hnf4a as the pot

141 Less reliable predictions limit their acquisition of reading expertise

142 We consider the predictions made by these mechanistic approaches for the

143 The comprehensive predictions made in this study provide the basis for lab

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

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

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

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

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

149 s and assessing the performance of footprint prediction methods.

150 tic evaluation of ten publicly available AMP prediction methods.

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

152 of CoA may improve when a multiple-criteria prediction model is adopted.

153 Additionally, we constructed a prognostic prediction model that effectively predicted prognosis an

154 A prediction model using circulating RBP4 concentration an

155 ated CPI affected performance of the updated prediction model was quantified by comparing AUROC curve

156 METHODS AND The original prediction model was retrospectively applied to 3491 con

157 imaging markers selected from a multivariate prediction model was tested with receiver operating char

158 We developed and validated a novel clinical prediction model with good discriminative performance to

159 A prediction model with optimal performance included these

160 n LPD predictions obtained using an existing prediction model, and (iii) agreement of predictions wit

161 ogic study, and affects the performance in a prediction model, has not been researched yet.

162 With the observations, we built a three-step prediction model, namely RPI-Bind, for the identificatio

163 Purpose Several lung cancer risk prediction models have been developed, but none to date

164 In prediction models including FLG expression, FLG genetic

165 lung cancer screening policies based on risk prediction models should be assessed and compared with t

166 eloped and compared 2-class and 3-class DILI prediction models using the machine learning algorithm o

167 size and sample-to-sample heterogeneity, our prediction models were not able to distinguish by sample

168 adding CAC score and removing only age from prediction models.

169 ticularly under limited situations, make the prediction more uncertain.

170 As a result, computational methods for predictioning new drug-target interactions have gained a

171 pectral differences, (ii) differences in LPD predictions obtained using an existing prediction model,

172 Data suggest that the prediction of adult cardiovascular disease using a model

173 ity of polygenic risk score analysis for the prediction of Alzheimer disease have given area under th

174 l predictors, with a substantial improvement prediction of amputation with ACR (difference in c-stati

175 an amino-acid propensity scale that enables prediction of antibodies likely to have delayed retentio

176 y for the waiting list patients and a better prediction of antibody-mediated rejection.

177 In summary, we present a method for prediction of clinical phenotypes using baseline genome-

178 lored as a significant biomarker towards the prediction of diabetic complications.

179 0.731, 95% CI 0.673-0.789, P < 0.01) for the prediction of DMSA defects.

180 ors, the data regarding somatic mutation for prediction of drug sensitivity remains controversial.

181 We apply these embeddings to the prediction of edges in the knowledge graph representing

182 may be used to improve the understanding and prediction of El Nino/La Nina events and also may be app

183 ther development (RiceAntherNet) that allows prediction of gene regulatory relationships during polle

184 A widely used strategy relies on initial prediction of human leukocyte antigen-binding peptides b

185 or in vitro testing of drug efficacy towards prediction of in vivo outcomes and investigation of drug

186 This framework might enable prediction of individual treatment responses to ECCO2R.

187 gh programs are available for sequence-based prediction of lipid accessibility and structure-based id

188 g systems and other evaluated biometrics for prediction of local tumor recurrence after renal cell ca

189 to traditional feature-selection methods for prediction of MMP-2, -3, -7, -8, -9 and -12 substrate-cl

190 eased mortality after TAVR and improves risk prediction of mortality when added to the Society of Tho

191 rker for liver synthesis and allows reliable prediction of mortality.

192 Computational prediction of mutation impacts on protein stability offe

193 mparative analysis of biological systems and prediction of new interactions.

194 om an adjusted model, a prognostic index for prediction of noncancer death was generated and compared

195 mpling method may be invented for non-lethal prediction of ovary development and sex because only a s

196 traightforward machine-learning approach for prediction of overall survival.

197 and early (3-4 years in advance on average) prediction of patients' future suicidal behavior.

198 Specificity and sensitivity for prediction of poor outcome were independent of age, sex,

199 aday 1, Holladay 2, Olsen, and SRK/T) in the prediction of postoperative refraction using a single op

200 rk can be used as a robust biomarker for the prediction of prognosis and treatment response in breast

201 ls of brain activity that allow for accurate prediction of regional pediatric brain metabolism.

202 nts in the U-BIOPRED (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes) study.

203 -FDG PET/CT performed most optimally for the prediction of response on a later CT scan in erlotinib-t

204 event progression of disease, in addition to prediction of success of therapy, and patient participat

205 mation improves prediction of TF binding.The prediction of TF binding is transferable across TFs and/

206 formation with sequence information improves prediction of TF binding.The prediction of TF binding is

207 l species within the cell, our model enables prediction of the cell's condition up to the moment of r

208 al pairs, an important step towards accurate prediction of the full tertiary structure.

209 sampling simulations that allow the accurate prediction of the sugar conformational preferences of ch

210 This observation does not support the prediction of the time-of-flight model and suggests that

211 Prediction of thermal behavior of SOA can be achieved by

212 ly we developed a structure-based method for prediction of transcription factor binding sites using a

213 nct electronic configurations and consequent prediction of unique reactivity and magnetic properties

214 ize that right cerebellar Crus I/II supports prediction of upcoming sentence content.

215 re not restricted to signaling errors in the prediction of value.

216 with RNAcompete motif libraries to provide a prediction of which trans -acting factors binding sites

217 ain connectivity data as input and generates predictions of behavioral measures in novel subjects, ac

218 edigrees can substantially bias animal model predictions of breeding values and estimates of additive

219 Our results are inconsistent with predictions of conventional soil C models and suggest th

220 ating this information into patient-specific predictions of disease progression.

221 a collection of highly accurate, genome-wide predictions of enhancers in the developing limb, availab

222 odestly with decreasing biomass, contrary to predictions of fishing down the food web [7].

223 ng extremes have become central for improved predictions of future reef form and function.

224 to outperform other existing methods in the predictions of globular protein stability changes upon m

225 rain in some places), which would complicate predictions of how primates in general will respond to c

226 uctures and explore the new theories for the predictions of metal ion and ligand binding sites and me

227 x RNA and DNA, and compare our findings with predictions of molecular-dynamics simulations.

228 degradation is a prerequisite to meaningful predictions of near-future CH4 release in the Arctic.

229 aily production will enable efficient global predictions of picophytoplankton productivity including

230 camp residence times conformed well with the predictions of the marginal value theorem, indicating th

231 by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the

232 To test the predictions of the optogenetic assay, we examined the co

233 uss their implications for understanding how predictions of the sensory consequences of behavior may

234 Although the predictions of the SSN have been confirmed in primary vi

235 and that, while their firing may conform to predictions of these models in some cases, they are not

236 imulation tools that allow understanding and predictions of these surface-grafted polymer architectur

237 corresponding experimental data showed good predictions of uptake for all test chemicals from water

238 r for different pH conditions and reasonable predictions of uptake of fluoxetine and diclofenac from

239 ccal conjugate vaccines (PCVs) have exceeded predictions of vaccine impact.

240 and growth rate is fundamental to effective predictions of viability, invasions and evolutionary pre

241 Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictabilit

242 ental change underpins our abilities to make predictions on future biodiversity under any range of sc

243 This work confirmed model-based predictions on the limited relevance of microplastic for

244 l buffering capacity, and generates testable predictions on their effects.

245 be used as a biomarker of injury for outcome prediction or target for rehabilitation intervention.

246 onal representations enable multidimensional predictions, or priors, that are combined with incoming

247 Pathogenicity-related factor prediction, orthology and multigene family classificatio

248 This method makes the model predictions probabilistic with clearly defined uncertain

249 om non-AMPs, but the relative quality of the predictions produced by the various tools is difficult t

250 ing on modeling of macromolecules, structure prediction, properties of polymers, entanglement in flui

251 0, and utilized continuum solvent-based loop prediction protocols to improve sampling.

252 The quality of the prediction provides compelling evidence that these subje

253 carrots and to build statistical models for prediction purposes.

254 Prediction quality depends on segment length, the type o

255 s cross-validations demonstrate the superior prediction quality of this method compared with other st

256 rstood that a potential for skillful climate prediction resides in the ocean.

257 s evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient (R).

258 e-independent diagnostics, the use of sepsis prediction scores, judicious antimicrobial use, and the

259 reas such as protein residue-residue contact prediction, secondary structure prediction, and fold rec

260 Our predictions show that a clear distinction between unicel

261 Crucially, cross-prediction showed that mean reward and variability repre

262 Using the fitted parameters to make predictions shows a high level of variability in the mod

263 the same time, diminished descending sensory prediction signals impede perceptual learning and may, t

264 that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inferen

265 In language, semantic prediction speeds speech production and comprehension.

266 to improve and make consistent the HPO terms predictions starting from virtually any flat learning me

267 can be included as constraints in structure prediction techniques to predict high-resolution models.

268 In this study, we tested the alternative prediction that VWFA development is in fact influenced b

269 avioral data on human participants and makes predictions that could potentially be tested with neurop

270 xpanses of water ice, confirming theoretical predictions that ice can survive for billions of years j

271 thods that guarantee biologically meaningful predictions that obey the true path rule, and can be use

272 rature-nutrient interaction and test a novel prediction: that a species' optimum temperature for grow

273 Contrary to previous predictions, the right whale population is projected to

274 ribes to the cerebellum a role in short-term prediction through internal modeling, we hypothesize tha

275 Prediction to date has focused on disentangling interact

276 ratification have focused mainly on response prediction to existing treatment regimens.

277 lography, and structure-based chemical shift predictions to explore the structural basis of allosteri

278 echnical factors on the quality of footprint predictions to highlight important considerations when c

279 practice; validation of FABP1 as a clinical prediction tool in APAP-ALF warrants further investigati

280 A prediction tool may allow for greater discrimination of

281 was to develop a clinically applicable risk prediction tool.

282 These AMP prediction tools show potential to discriminate AMPs fro

283 mpetitive with that of several other popular prediction tools, including KinasePhos, PPSP, GPS, and M

284 behavioural responses to estimate subjective predictions under an ideal-observer model.

285 largest impact on the accuracy of biological predictions under climate change.

286 Contrary to predictions, urban ponds supported similar numbers of in

287 Model predictions using 0.43 M HNO3 are superior to those usin

288 This prediction was confirmed by expression in cell models, w

289 That prediction was subsequently confirmed by re-analysis of

290 Functional prediction was used to investigate the effect of MDD-ass

291 This prediction was validated by a combination of simulation

292 US children over recent years, and the model prediction was validated using an independent data set f

293 This prediction was verified by tryptic digestion of SERT-exp

294 To validate this prediction, we demonstrated that the modulation of heat-

295 To validate this prediction, we perform electrophysiological analysis of

296 ear lung cancer incidence and mortality risk predictions were assessed for (1) calibration (graphical

297 The model predictions were compared with the experimental data for

298 We tested these predictions with a comparative analysis on the outcomes

299 ing prediction model, and (iii) agreement of predictions with cholesterol concentrations in high- and

300 egrated approach that combines computational predictions with new experimental studies in mice to ide

301 represbyopic eyes (P = .042), and refractive predictions with the Holladay 2 and Haigis formulas diff