ライフサイエンスコーパス: data-driven

1 n a priori; in that respect our approach is "data-driven".

2 These results provide a data-driven algorithm to increase fairness in listing pr

3 loped LaSSO (Lariat Sequence Site Origin), a data-driven algorithm which utilizes RNA-seq data.

4 nd results can help engineer highly scalable data-driven algorithmic management decision support syst

5 r ablation and show exceptional promise as a data-driven alternative to manual annotations.

6 Here, we propose a data-driven alternative.

7 recasts compare to their more computational, data-driven alternatives?

8 e utility for this dataset, we performed two data driven analyses.

9 In conclusion, data-driven analyses defined a hierarchically ordered co

10 fied 56 of a possible 3284 citations for (1) data-driven analyses of the dimensions and factor struct

11 d empirical support in a large data set with data-driven analyses.

12 ymptoms across diagnostic categories using a data-driven analysis (multivariate distance-based matrix

13 ic health threat, Zika virus, we introduce a data-driven analysis by exploiting multiple sources and

14 (226 male), we performed a multivariate and data-driven analysis combining multiple imaging modaliti

15 Here, we show that a data-driven analysis of brain structural variation acros

16 Here we present a data-driven analysis of regulatory elements from a micro

17 Using a data-driven analysis of resting-state functional magneti

18 We adopt a data-driven analysis technique based on the study of sho

19 Here, we address this problem using data-driven analysis to test the hypothesis that the bil

20 We then utilized this data-driven analytical framework to show that the degree

21 The development of a data-driven analytics-based model may assist transplant

22 A data-driven analytics-based model was developed to predi

23 The data-driven anatomic classification identifies biologica

24 d, encompasses two complementary approaches: data driven and theory driven.

25 ns, it is highly desirable to synthesize the data-driven and knowledge-driven modeling approaches.

26 gating biological machinery captured by both data-driven and manually curated ontologies.

27 ut analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for t

28 magnetic resonance imaging at 7 T with novel data-driven and model-based analyses to identify corresp

29 g can be fostered by synergistic use of both data-driven and theory-driven ecological as well as biop

30 The research will be increasingly "data driven," and the powerful machine learning methods

31 We demonstrate the power of our large-scale data-driven annotation during the analysis of cyclin-dep

32 t system (belt gating [BG]) and an automatic data-driven approach (data-driven gating [DDG]).

33 Our data-driven approach allows us to detect event boundarie

34 ng conventional hardware-based gating with a data-driven approach and to describe the distribution of

35 ysis of large metabolite inventories using a data-driven approach based upon a self-organizing map al

36 conditions) risk factors, indicating that a data-driven approach can yield more comprehensive risk p

37 Our data-driven approach confirmed previously described mech

38 INTERPRETATION: This data-driven approach defined novel and clinically releva

39 We developed a data-driven approach for the determination of an optimal

40 cation, characterization, and regression), a data-driven approach for the identification of stratifyi

41 he relationship of EF and ToM deficits via a data-driven approach in a large sample of patients with

42 This data-driven approach in designing DPT protocols is a ste

43 Here a data-driven approach is introduced based on a dimension

44 A data-driven approach of arbitrary Polynomial Chaos (aPC)

45 Simulation results, using data-driven approach on Zika virus, which has a growing

46 tion by controlling model complexity using a data-driven approach that marginalizes or removes irrele

47 ectively classify brain state, we describe a data-driven approach that projects time-varying LFP spec

48 Here, we employed a data-driven approach to assess the interaction between b

49 To address this issue, we used a data-driven approach to describe a large database of sce

50 We present a data-driven approach to determine the memory kernel and

51 variants from five phenotypes, we describe a data-driven approach to determine the tissue and cell ty

52 We use a data-driven approach to evaluate this hypothesis, utiliz

53 We present a data-driven approach to infer reactions in the IgG glyco

54 To address this problem, we developed a data-driven approach to integrate and analyze raw source

55 lying brain dysfunctions, we applied a fully data-driven approach to investigate whether the subjects

56 In the present study, we adopt a data-driven approach to map the spectrotemporal amplitud

57 lock Network Mapping provides an alternative data-driven approach to mapping quantitative trait loci

58 A data-driven approach to model development for controlled

59 Here we applied a meta-analytic data-driven approach to nearly 10,000 fMRI studies to id

60 We provide a data-driven approach to partition the data into subpopul

61 Here, we use a data-driven approach to pinpoint the movements that disc

62 A hypothesis-neutral, data-driven approach to the analysis of connectivity is

63 An unbiased data-driven approach using factor analysis was used to d

64 large sample of stroke survivors, we used a data-driven approach using principal components analysis

65 First, a nonparametric, data-driven approach was used to identify potentially in

66 Here we develop a data-driven approach, illustrated in the context of imag

67 CPM focuses on linear modeling and a purely data-driven approach, neuroscientists with limited or no

68 Using a genetic algorithm in a data-driven approach, our method assigns predictors acco

69 on of removal of unwanted variation (RUV), a data-driven approach, removes systematic noise but also

70 A multi-genomic, data-driven approach, utilizing 106 human non-small-cell

71 Using an unbiased, data-driven approach, we analyzed large-scale coactivati

72 Using a data-driven approach, we show that genes in associated l

73 cets tend to subsume smaller ones-we adopt a data-driven approach.

74 en problematic, and we therefore developed a data-driven approach.

75 However, generic challenges related to data-driven approaches (e.g., data processing, data avai

76 Simulation- and data-driven approaches (promoted by efforts such as the

77 the two best performing teams present their data-driven approaches and computational methods.

78 Data-driven approaches apply machine-learning methods to

79 Data-driven approaches are clinically applicable alterna

80 Data-driven approaches can capture behavioral and biolog

81 We believe that these data-driven approaches can complement the traditional me

82 ur method presents a ripe opportunity to use data-driven approaches for advancing our current knowled

83 Data-driven approaches for identifying homogenous subgro

84 Similar data-driven approaches may provide a framework for futur

85 data emerging from metagenomic studies, but data-driven approaches such as network inference that ai

86 Here, we used novel meta-analytic data-driven approaches to characterize the function and

87 d complexes, there is now an opportunity for data-driven approaches to fragment binding prediction.

88 data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organizati

89 nce algorithm that unify the model-based and data-driven approaches to visualizing and inferring popu

90 This paper reviews several data-driven approaches which play a key role in bringing

91 Newly proposed diagnostic criteria utilize data-driven approaches with very high sensitivity and sp

92 ted a large gap in ecology between model and data-driven approaches.

93 A fully data-driven artificial intelligence-based grading algori

94 els, high-throughput toxicokinetic data, and data-driven assumptions about consumption of water.

95 Based on data-driven assumptions, the simulation took into accoun

96 using local linear regression models with a data-driven bandwidth and with the algorithm for selecti

97 man tissues and cell types developed using a data-driven Bayesian methodology that integrates thousan

98 We used a data-driven Bayesian model to automatically identify dis

99 GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data.

100 Strictly data-driven BN modeling indicates that the strength of i

101 we improve upon previous methods by using a data-driven brain parcellation to compare connectivity p

102 Data-driven cell classification is becoming common and i

103 is great potential for using the outputs of data-driven cell classification to structure ontologies

104 an be extended by integrating the outputs of data-driven cell classifications.

105 structure ontologies and integrate them with data-driven cell query systems.

106 n of cell ontologies can enhance querying of data-driven cell-classifications and how ontologies can

107 on model with a time-series model to capture data-driven changes in country-specific MMRs, and includ

108 We tested how this information impacts data-driven classification between unresponsive and mini

109 Data-driven classification into one of the 10 most commo

110 multivariable models using machine learning data-driven classification techniques can be used to par

111 and for linking ontologies to the outputs of data-driven classification.

112 y terms can play an important role in making data driven classifications searchable and query-able, b

113 The main outcome measures are reproducible, data-driven, clinically meaningful clusters of complicat

114 We used a data-driven clustering approach to show that stimulation

115 Using a data-driven clustering approach, we observed distinct te

116 To this end, we used a data-driven comprehensive proteomic analysis (multiplex

117 dynamics and, where possible, a move toward data-driven, comprehensive models.

118 Evaluations with simulations and data-driven computational experiments demonstrate that t

119 We introduce and perform novel data-driven computational experiments for assessing the

120 A data-driven computational model of corticostriatal funct

121 impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynam

122 A data-driven computational model of the corticostriatal n

123 ics in developing brains of intact pupae and data-driven computational modeling.

124 Our interest is in developing data-driven computational models that can bridge the gap

125 er, gaining such insight requires developing data-driven computational models that can identify and d

126 We present a data-driven computational pipeline for the genome-wide i

127 data in a relational database, facilitating data-driven contact tracing, and improving outbreak data

128 this has traditionally been achieved through data-driven correlative modelling, robustly extrapolatin

129 to machine learning to establish objective, data-driven criteria for pathogenic processes and progno

130 We present data-driven criteria for scoring loop-mediated PPIs, unc

131 develop a new computationally efficient and data-driven cross-validation algorithm.

132 of our knowledge calls upon clinicians to be data driven, cross-disciplinary, and collaborative in un

133 We further developed a new data-driven decision rule, FSindex, for estimating the l

134 s need to be understood in order to optimize data-driven decision-making.

135 Here, we present a data-driven decision-theoretical model of feeding in Cae

136 We present a universal, data-driven decomposition of chaos as an intermittently

137 We developed and evaluated a data-driven deep learning algorithm as a novel diagnosti

138 ntiFERON-TB testing algorithms and provide a data-driven definition of conversion.

139 These data-driven definitions of the gene-sets can be context-

140 empowers users with the ability to construct data driven descriptions of shared and unique biological

141 ional clinical care for purposes of enabling data-driven discovery across disciplines such that every

142 TDA-based data-driven discovery has great potential application fo

143 Data-driven discovery in complex neurological disorders

144 ation of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain inj

145 This approach opens the door to data-driven discovery of new synapse types and their den

146 WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be use

147 with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene r

148 ly derived gene clusters are associated with data-driven ERP subcomponents, assuming a complex etiolo

149 SIfTER is a data-driven evolutionary algorithm, leveraging experimen

150 Ideally, data-driven experimentation could be used to learn accur

151 agment probabilities, and adopting improved, data-driven factorizations of this likelihood, we demons

152 ysically realistic computational model using data driven feedforward and feedback parameters replicat

153 Future studies are needed to determine if data-driven fentanyl dosing algorithms can improve outco

154 tion will prove useful to authors who create data-driven figures intended to be published in the Jour

155 Using a combination of data-driven flexible docking and molecular dynamics simu

156 Our data-driven focus on multiplexing (and de-multiplexing)

157 he evolutionary dynamics of labile traits, a data-driven framework for incorporating such traits into

158 works, we developed a systematic integrative data-driven framework to identify shared disease-associa

159 The data-driven framework we outline here has the potential

160 Principal components analysis derived data-driven frequency bands evoked power.

161 [BG]) and an automatic data-driven approach (data-driven gating [DDG]).

162 Using data-driven gene network analysis, we identified 17 gene

163 ly proposed algorithms for identification of data-driven gene-sets are based on hard clustering which

164 d control subjects with a recently developed data-driven global brain connectivity (GBC) method, both

165 We use a data-driven global stochastic epidemic model to analyze

166 The narrow uncertainties of these data-driven GPP estimates suggest that they could be use

167 Whole-brain data-driven graph theoretical analysis disclosed that st

168 tional connectivity strength, a whole-brain, data-driven, graph theory-based method, was applied to r

169 ReSCUE was useful in (i) providing objective data driven guidance for the number of source factors an

170 We compared a data-driven hierarchical clustering approach to the comm

171 re, an open-access MATLAB tool that provides data-driven, high-throughput analyses of USVs.

172 his framework facilitates the formulation of data-driven hypotheses regarding the processes that stru

173 ily accessed by experimentalists to generate data-driven hypotheses.

174 into the published literature and streamline data-driven hypothesis generation.

175 ch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater e

176 ll types in which "omics" data necessary for data-driven identification are missing.

177 Data-driven independent component analysis was applied t

178 teins from modENCODE, enabled making various data-driven inferences about chromatin profiles and inte

179 plexity of the neocortex and enables further data-driven inquiries.

180 ce they triggered the development of a novel data-driven investigative approach.

181 -world performance data that will facilitate data-driven laboratory test choices for managing patient

182 We apply a data-driven latent class analysis (LCA) to model 54 spec

183 Using a data-driven latent-variable approach, we demonstrate tha

184 l parameters we developed a hypotheses free, data driven machine learning approach.

185 rks known to be abnormal subacutely and in a data-driven manner.

186 employed to tackle this problem in a purely data-driven manner.

187 o reconstruct cellular signaling system in a data-driven manner.

188 We present a data-driven mathematical model of a key initiating step

189 ntal determination of kinetic parameters and data-driven mathematical modelling allowed us to obtain

190 e functional topology of this network, using data-driven matrix factorization, which allowed for part

191 We adopt here a solely data-driven measurement approach in which we first demon

192 interactome discovery strategies to generate data-driven mechanism-of-action hypotheses.

193 framework provides a missing link towards a data-driven mechanistic microbial ecology.

194 We have developed a data-driven mechanistic model using realistic root geome

195 The foundation for a new era of data-driven medicine has been set by recent technologica

196 courses of brain networks extracted using a data-driven method (independent component analysis).

197 dress both problems and develop a principled data-driven method that determines relevant timescales a

198 Here the authors present a data-driven method that determines relevant timescales f

199 Combining this data-driven method with a causal whole-brain computation

200 SELDOM is a data-driven method, in the sense that it does not requir

201 Section I outlines data-driven methods capable of identifying the perceptua

202 processes or diseases, making sophisticated data-driven methods easily accessible to researchers.

203 RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified li

204 Data-driven methods such as hierarchical clustering (HC)

205 ual classification techniques and automated, data-driven methods, hallucinations were associated with

206 This study demonstrates that data-driven methods--commonplace in studies of human neu

207 ial frontal cortex using relatively unbiased data-driven methods.

208 tify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika vi

209 e derive a species- and temperature-specific data-driven model of the rat ventricular myocyte.

210 pretation of measured changes in ADC using a data-driven model that describes sources of measurement

211 Our data-driven model tracked human immunodeficiency virus (

212 The data-driven model we describe supports hypothetical mode

213 a without experimental design--that temporal data-driven modeling can effectively distinguish between

214 synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define

215 e science domains, we establish an authentic data-driven modelling framework to simulate zebrafish sw

216 g biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical

217 n (GPP) estimates using the average of three data-driven models and eleven process-based models.

218 that couples fluorescent cell tracking with data-driven models.

219 using mean-centered Partial Least Squares, a data-driven multivariate technique optimal for identifyi

220 Imaging results were explored using data-driven multivoxel pattern activation.

221 rior longitudinal fasciculus together with a data-driven multivoxel pattern analysis of whole-brain r

222 We provide a new class of neural data-driven musculoskeletal modeling formulations for br

223 cal LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledg

224 Crucially, data-driven neural simulations revealed a clear temporal

225 This method can efficiently implement data-driven non-linear projection and incorporate prior

226 ocampal Schaffer collateral synapse by using data-driven nonparametric modeling.

227 se discovery rates compared to commonly used data-driven normalization methods.

228 This is an established approach in data-driven ontologies such as the Experimental Factor O

229 lts only for a range of cutoff points around data-driven "optimal" cutoffs.

230 c memory network whether it was defined in a data-driven or literature-based manner.

231 We used data-driven parsing of neural connectivity to reveal sub

232 We used data-driven parsing of neural connectivity to reveal sub

233 Data-driven parsing suggests heterogeneous substrates of

234 Data-driven parsing suggests heterogeneous substrates of

235 which was confirmed by means of multivariate data-driven partial least squares analyses.

236 A data-driven partial least squares multivariate analysis

237 Finally, we introduce a data-driven percolation model mimicking rumor spreading

238 ther, these observations provide a valuable, data-driven perspective on both the strengths and limita

239 Data-driven phenotype analyses on Electronic Health Reco

240 This system opens up a new path to data-driven phenotypic diagnosis and better understandin

241 nts, the goal of this study was to develop a data-driven pipeline for discovering QT-DDIs.

242 ask, a remarkable convergence of a bottom-up data-driven plasticity model with the top-down behaviour

243 Two data-driven, polyfunctionality panels (IL-2-associated a

244 Such a data-driven posteriori pattern faces low efficiency, amb

245 ur results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic

246 sses shaping social dynamics and to generate data driven predictions for the asymptotic behaviour of

247 nces of five different types of statistical, data-driven predictive models: multiple linear regressio

248 These methods suggest a data-driven, predictive approach for early screening and

249 To this end, we build data-driven predictors of protein levels using mRNA leve

250 of Drosophila chromatin states derived from data-driven probabilistic modelling of dependencies betw

251 ry Newborn Series, we propose a country-led, data-driven process to sharpen national health plans, se

252 onnectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of

253 Here we describe a data-driven qRT-PCR normalization method, the minimum va

254 We developed a data-driven rabies transmission model fit to human rabie

255 d controlled trials, based on scientific and data-driven rationales for disease and brain target sele

256 This data driven reverse engineering approach is sufficiently

257 c stimulus (i.e., a Hollywood movie) using a data-driven reverse correlation technique.

258 Using a data-driven "reverse correlation" approach, we character

259 le-cell sequencing technology have created a data-driven revolution in immunology.

260 This work underscores the importance of a data-driven, risk-based monitoring program that incorpor

261 This data-driven, risk-stratified approach significantly decr

262 Assess the impact of the implementation of a data-driven scheduling strategy that aimed to improve th

263 describes the successful implementation of a data-driven scheduling strategy that increased the effec

264 ts demonstrate that predictive modelling and data-driven science can now be applied to solve some of

265 a vision to advance microbiome research as a data-driven science.

266 ransform computational protein design into a data-driven science.

267 ies has created a tremendous opportunity for data-driven science.

268 Laying a community-based foundation for data-driven semantic standards in environmental health s

269 Our data-driven simulations and our application to survival

270 ectly combining behavior quantification with data-driven simulations can be applied to more complex s

271 Data-driven simulations suggested that pRSEM has a great

272 Our findings are corroborated by data-driven simulations, where the empirical distributio

273 Data-driven social-networking should facilitate identifi

274 Future efforts should be directed toward data-driven standardization of iNO use to ensure cost-ef

275 bling the development of community-based and data-driven standards that will ultimately improve stand

276 rmed unsupervised hierarchical clustering, a data-driven statistical approach, on histologic, genetic

277 een atrophy and hypometabolism by means of a data-driven statistical model of non-overlapping intensi

278 k for drug action that leverages advances in data-driven statistical modeling and mechanism-based mul

279 Data-driven statistical models calculated from the phosp

280 equirements could be met by developing a Big-Data-driven stem cell science strategy and community.

281 nformation to generate and functionally test data-driven structural models for three diverse HCV RNA

282 d disparities of TNBC.Significance: This big data-driven study comparing normal and cancer transcript

283 We developed a data-driven, supercomputer-based, full-scale (1:1) model

284 ltifaceted metric that provides an effective data-driven supplement to expert opinion.

285 esis of proteolysis-resistant analogs (i.e., data-driven synthesis).

286 is for constructing a biologically informed, data-driven taxonomy of psychological processes.SIGNIFIC

287 Here Brunton et al.develop a data-driven technique to analyze chaotic systems and pre

288 n CML and normal states, and it identified a data-driven threshold to classify strongly co-expressed

289 We undertook a data-driven time-series analysis to examine trends in co

290 to empower scholars with a quantitative and data-driven tool to study culture and society, but its p

291 We discussed data-driven use cases that leverage linkage of CL, CLO a

292 The findings show that data-driven UWM allows us to develop and apply novel met

293 , data costs) and the specific challenges of data-driven UWM need to be addressed, namely data access

294 It critically investigates whether data-driven UWM offers a promising foundation for addres

295 difficulty of assessing the cost benefits of data-driven UWM.

296 FOMC to model background sequences with the data-driven Variable Length Markov Chain (VLMC) in metat

297 s were analyzed using region of interest and data-driven voxel-based approaches.

298 RNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a ce

299 We used unbiased, multimodal, data-driven, whole-brain measures of neural activity (ma

300 literature, this framework-level approach is data-driven, without assuming any pre-known shape attrib