ライフサイエンスコーパス: machine learning

1 ewpoints and leveraging recent advances from machine learning.

2 n using a hybrid method of deep learning and machine learning.

3 r's disease, have also been identified using machine learning.

4 as well as opportunities and challenges for machine learning.

5 learn) for file handling, visualization, and machine learning.

6 collects >2000 behavioral features based on machine learning.

7 of MeONP-induced inflammatory potential via machine learning.

8 Machine learning, a subfield of artificial intelligence,

9 Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in

10 a comprehensive prediction model applying a machine learning algorithm allowing selection of the mos

11 cross disparate sources using an integrative machine learning algorithm and optimization-based featur

12 CH4) could be explained by the random forest machine learning algorithm and traditional linear regres

13 The machine learning algorithm classified with 81% overall a

14 ionally, we focused on the results using the machine learning algorithm in the context of multi-locus

15 d estimation (TMLE) paired with the ensemble machine learning algorithm Super Learner, and compared t

16 In this study, a commercially available machine learning algorithm that flags abnormal noncontra

17 A recently developed semi-supervised machine learning algorithm was used to delineate neuroco

18 This work presents a system equipped with a machine learning algorithm, capable of continuously moni

19 es and optimising a multivariate statistical machine learning algorithm.

20 he literature and used to train a supervised machine-learning algorithm utilizing boosting, achieving

21 he correction from first principles for each machine-learning algorithm, we observe that there is typ

22 In this study, by using machine learning algorithms and the functional connectio

23 wever, once a dataset is sufficiently large, machine learning algorithms approximate the true underly

24 Machine learning algorithms are then typically employed

25 ddress concerns regarding the use of complex machine learning algorithms in the clinic, for each pati

26 of this work is to evaluate the efficacy of machine learning algorithms to cluster the patients into

27 In these settings, we adopt machine learning algorithms to investigate the extent to

28 T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or na

29 Machine learning algorithms trained to predict the regul

30 In addition, the performance of multiple machine learning algorithms was evaluated by comparing i

31 Multiple machine learning algorithms were applied to predict assa

32 Machine learning algorithms were developed to identify a

33 In conjunction with machine learning algorithms, scRCAT-seq demarcates RNA t

34 evalent with advances in data collection and machine learning algorithms.

35 ngevity or anti-longevity using a variety of machine learning algorithms.

36 the security and transparency risks posed by machine learning algorithms.

37 This was more evident for some of the machine learning algorithms.

38 stoniaNet significantly outperformed shallow machine-learning algorithms in benchmark comparisons, sh

39 ogether with HLA-binding properties by using machine-learning algorithms may increase the prediction

40 e into a training and a testing set and used machine-learning algorithms to classify participants bas

41 ition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language t

42 The survival hazard ratio was presented by machine-learning algorithms using survival statistics an

43 Machine learning analysis demonstrates that baseline pla

44 optogenetics, in vivo electrophysiology, and machine learning analysis, we find that a subset of neur

45 Advances in machine learning and contactless sensors have given rise

46 ing ranking (learning to rank) is a class of machine learning and deep learning algorithms that perfo

47 ough the use of nonlinear kernel methods for machine learning and dimension reduction of high-dimensi

48 Applications of machine learning and graph theory techniques to neurosci

49 Machine learning and high-throughput computational scree

50 Here, we use a combination of machine learning and massively parallel computing to pre

51 Previously, we developed a model that used machine learning and natural language processing of text

52 eatures governing stability using supervised machine learning and Shapley values.

53 vent of modern technologies and coupled with machine learning and wireless communication, represent t

54 les simulation, high throughput computation, machine learning, and artificial intelligence work colle

55 incorporating behavioral data, unsupervised machine learning, and network analysis to identify epige

56 g and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the

57 ur results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to sugges

58 tudied using multiscale mechanistic docking, machine learning, and X-ray crystallography, pointing th

59 sing a novel combination of computer-vision, machine-learning, and time-series analyses.

60 For each area, early instances of successful machine learning applications are discussed, as well as

61 Machine learning applied to features manually extracted

62 We sought to assess the utility of a novel machine learning approach for quantifying 3-dimensional

63 A novel machine learning approach generated severity estimates t

64 We develop a machine learning approach that takes only the stoichiome

65 We further employed a machine learning approach to identify novel associations

66 a large ovarian tumour cohort and develop a machine learning approach to molecularly classify and ch

67 In this study, we have developed a new machine learning approach to predict candidate lncRNAs a

68 he specificity for LASSO was 41% and for the machine learning approach was 62%.

69 90%, the precision for LASSO was 65% and the machine learning approach was 74%, while the specificity

70 Sequentially, a machine learning approach was applied to identify the to

71 Using a machine learning approach, we built gene expression-base

72 Here, we developed a machine-learning approach to identify small molecules th

73 Here, we introduce a bespoke machine-learning approach, hierarchical statistical mech

74 Using a machine-learning approach, we compute and validate trans

75 Taking a machine-learning approach, we predict personality at bro

76 termixed tactile and thermal stimuli using a machine-learning approach.

77 view may serve to further the development of machine learning approaches for this important use case.

78 Machine learning approaches promise to accelerate and im

79 We apply machine learning approaches to a comprehensive vascular

80 However, existing machine learning approaches to diagnosis are purely asso

81 Therefore, we have applied different machine learning approaches to generate models for predi

82 Machine learning approaches to modeling of epidemiologic

83 nd how new discovery may be advanced through machine learning approaches.

84 ems theories, including network analysis and machine learning, are well placed for analysing these da

85 pathology, gastroenterology, and cardiology machine learning articles published in 2015-2019.

86 a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally co

87 lysis Pipeline using Statistical testing and Machine Learning (ASAP-SML), to identify features that d

88 extracellular vesicle (EV) therapy, and (iv) machine learning-assisted therapy.

89 Here, a machine-learning-assisted composition space approach was

90 lly, we provide a very efficient, robust and machine-learning-assisted method for mapping the user-re

91 d fluid features were extracted using an OCT machine-learning augmented segmentation platform.

92 Machine learning augments burn sepsis prediction.

93 e paediatric early warning (PEW) score and a machine learning automated approach: a Real-time Adaptiv

94 DDAP is so far the first machine learning based algorithm for type I PKS DD affin

95 s, together with a "logical" statistical and machine learning-based approach, identified a number of

96 Our data suggest that machine learning-based automatic quantification offers a

97 attributes were subsequently integrated with machine learning-based framework to identify the probabi

98 The highest-performing model used a machine learning-based genetic algorithm, with an overal

99 from moldy and no homes were used to train a machine learning-based model to classify the mold status

100 In response, we employed high-performance machine learning-based NER tools for concept recognition

101 This study compares machine learning-based prediction models (i.e. Glmnet, R

102 icine paid a particular attention to develop machine learning-based techniques for the detection and

103 ature Biotechnology describe two independent machine-learning-based algorithms that demonstrate impro

104 We performed machine-learning-based analyses on functional magnetic r

105 ed study, the UK Biobank, using an automated machine-learning-based analysis pipeline.

106 ve geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITE

107 sensory neuron differentiation, integrating machine-learning-based quantification of arbor patternin

108 In this Review, we discuss how machine learning can aid early diagnosis and interpretat

109 entation, radiomics features extraction, and machine learning can be used in a pipeline to automatica

110 es and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa

111 Machine learning can be used to improve surgical risk pr

112 These efforts demonstrate how machine learning can be used to predict AR-mediated bioa

113 havioral rhythm sensing with smartphones and machine learning can help better understand and predict

114 Instead, novel tools from the field of machine learning can potentially solve some of our chall

115 On the basis of machine learning, CEFCIG reveals unique histone codes fo

116 Finally, machine learning classification algorithms applied to gr

117 ain nuclear magnetic resonance (TD-NMR), and machine learning classification models (ML) for monitori

118 Furthermore, a strong metabolite-based machine-learning classifier was able to successfully pre

119 lectance spectroscopy, and LIBS coupled with machine learning classifiers can be used to identify bot

120 Current machine learning classifiers have successfully been appl

121 efficiently processed by linear regularized machine learning classifiers.

122 rved in aged cells, and we develop effective machine-learning classifiers for cell age.

123 Transcriptome-based machine-learning classifiers revealed that half of the m

124 structure of themodel used is set purely by machine learning considerations with little consideratio

125 efforts on both sensor device innovation and machine learning data analytics, this paper shows that t

126 Machine learning-driven, physics-based simulations provi

127 Here, we present expert-augmented machine learning (EAML), an automated method that guides

128 2000 combinations with less than 10 mg, and machine-learning-enabled autonomous experimentation iden

129 Machine learning feature selection methods are needed to

130 ating respondent-driven sampling weights and machine learning feature selection were used to identify

131 a when the scores they generated are used as machine learning features. MASS outperforms almost all o

132 well-known feature ranking methods from the machine learning field and five high-dimensional dataset

133 also use classification algorithms from the machine-learning field.

134 cribe tricks and problems when using them in machine learning for excited states of molecules.

135 The use of machine learning for multivariate spectroscopic data ana

136 cure wireless data transfer electronics, and machine learning for predictive data analysis.

137 We used machine learning for processing laboratory findings of 1

138 topological data analysis and interpretable machine learning for quantifying both agent-level featur

139 We propose an innovative machine learning framework combining multiple holdouts a

140 erence that naturally fits into the standard machine-learning framework where the data are divided in

141 sequence context and biological effect in a machine-learning framework.

142 arch in finding more effective ways to adopt machine learning frameworks in achieving robust performa

143 imized using feature selection algorithm and machine learning, from which the accuracy of detection o

144 To address these challenges, machine learning has been broadly applied to analyze mul

145 Although machine learning has enabled unbiased analysis of behavi

146 t, we review how the different approaches of machine learning have been applied to porous materials.

147 This large-scale project used machine learning (i.e., Random Forests) to 1) quantify t

148 Machine learning identified different cognitive profiles

149 Here we argue that the recent resurgence of Machine Learning in combination with the availability of

150 iew, we examine the literature pertaining to machine learning in hepatology and liver transplant medi

151 The latest developments in machine learning (including deep learning) provides a gr

152 e advances in computational neuroscience and machine learning into improved outcomes for patients suf

153 Machine learning is a branch of computer science and sta

154 Machine learning is a powerful tool for creating computa

155 or accelerated MR image reconstruction using machine learning is presented.

156 Machine learning is processed by dividing the dataset in

157 In this paper, we present a method, termed machine-learning iterative calculation of entropy (MICE)

158 The machine learning medial prefrontal cortex-posteromedial

159 the availability of a more accurate and fast machine learning method for the identification of circul

160 echanism for dynamic data analysis and using machine learning method predicts the basic reproduction

161 Here, we present Ens-Grad, a machine learning method that can design complementarity

162 Here, we present a machine-learning method comparing projected typhoon trac

163 ances and biological interpretation across 8 machine learning methods and 4 different types of metage

164 Given that machine learning methods applied to neurological signals

165 data to assign a bioactivity score, Bayesian machine learning methods can be used for prospective pre

166 Bayesian machine learning methods can be used for prospective pre

167 found minimal evidence that state-of-the-art machine learning methods can forecast the occurrence of

168 Standard supervised machine learning methods enabled development of discrimi

169 aluating multivariate statistical models and machine learning methods for the classification of 6 typ

170 Various machine learning methods have been proposed, including a

171 To this end, several statistical and machine learning methods have been proposed.

172 Modern machine learning methods may be used to probe relationsh

173 ity functional theory (DFT) calculations and machine learning methods to determine their magnetic pro

174 Among other advances, deep-learning machine learning methods, including convolutional neural

175 perspectral Imaging (NIR-HSI), together with machine learning methods, is valuable to improve the eff

176 cts the performance and outcomes of chemical machine learning methods.

177 ancestral recombination graph inference and machine-learning methods for the prediction of selective

178 ucture prediction enables the development of machine-learning methods to predict the likely biologica

179 We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH(4) da

180 Advances in machine learning (ML) and artificial intelligence (AI) p

181 ascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL).

182 t this choice, we evaluated well-established machine learning (ML) classifiers including random fores

183 (PL), to create silver standard for training machine learning (ML) for disease classification.

184 CCS prediction using machine learning (ML) has recently shown promise in the

185 Use of machine learning (ML) in clinical research is growing st

186 In this effort, machine learning (ML) is applied in a systematic manner

187 op experimentation combined with advances in machine learning (ML) is uniquely suited for high-throug

188 Machine learning (ML) may guide interventions to reduce

189 Application of machine learning (ML) methods for the determination of t

190 Machine learning (ML) provides powerful dimensionality r

191 Machine learning (ML) utilizes artificial intelligence t

192 The machine learning model group selected for further studie

193 In the external validation cohort, the machine learning model identified patients who met the c

194 chemical shift calculation protocols using a machine learning model in conjunction with standard DFT

195 The machine learning model pretrained on Fourier spectrum fe

196 The machine learning model that considered both radiomic and

197 iners as most probative, to build a standard machine learning model that predicts (based on covariate

198 We have trained a machine learning model to analyze the correlation betwee

199 We present a new machine learning model to distinguish AVPs from non-AVPs

200 We developed a machine learning model to identify patients at risk for

201 A machine learning model trained on VirScan data predicted

202 In conclusion, our machine learning model was able to identify EGFR-mutant

203 The machine learning model was then adapted to predict the D

204 odel significantly outperforms a traditional machine learning model, as well as accurately produces s

205 0 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmen

206 The developed machine-learning model shows good predictability and agr

207 The resulting machine learning models and segregation database are key

208 Overall, regression-based machine learning models are efficient techniques for map

209 henotypes can be accurately identified using machine learning models based on readily available clini

210 tility of the complete feature set, we train machine learning models for health state-prediction in 3

211 g both genetic and non-genetic factors, four machine learning models have close prediction results fo

212 Machine learning models have the potential to address th

213 -quality data is critical for the success of machine learning models in the era of big data.

214 Here, we highlight specific achievements of machine learning models in the field of computational ch

215 st" methods might improve the performance of machine learning models on radiomics images and increase

216 mbeddings, BioConceptVec, via four different machine learning models on ~30 million PubMed abstracts.

217 tely than previous methods, and we show that machine learning models that exploit this representation

218 We aimed to develop machine learning models to accurately predict bronchioli

219 The authors used functional MRI and machine learning models to address individual variabilit

220 ights the power of combining mechanistic and machine learning models to effectively direct metabolic

221 We used four machine learning models to produce flood susceptibility

222 Using machine learning models trained on data from the simulat

223 Machine learning models using transporter gene frequenci

224 n, the generation and evaluation of multiple machine learning models utilizing different sources of a

225 Machine learning models utilizing volatile organic compo

226 ovides a walkthrough for creating supervised machine learning models with current examples from the l

227 The training performance of all machine learning models, including six other algorithms,

228 Two machine learning models, one exclusively trained on VF d

229 of vector representations of concepts using machine learning models-have been employed to capture th

230 then fed the selected variables to multiple machine learning models.

231 eepHE significantly outperforms the compared machine learning models.

232 response is needed to support future work of machine learning models.

233 comes predicted with arbitrarily complicated machine-learning models including random forests and dee

234 eature selection can improve the accuracy of machine-learning models, but appropriate steps must be t

235 build more interpretable neuroimaging-based machine-learning models, contributing to the cumulative

236 Combined with machine learning, morphometric markers form intuitive vi

237 This letter is devoted to the application of machine learning, namely, convolutional neural networks

238 We explored unsupervised machine learning of ECG waveforms to identify CRT subgro

239 Machine learning of new parameterizations from high-reso

240 ificity~90-93% through the sparse regression machine learning of patterns.

241 Here, we conduct machine learning of serum metabolic patterns to detect e

242 All-printed electronics, incorporating machine learning, offers multi-class and versatile human

243 Using a combination of machine learning, optical character recognition, and man

244 ich was 19.1% (or 5.3%) higher than the best machine learning (or threading)-based method.

245 In this study, we present a machine learning pipeline for rapid, accurate, and sensi

246 escriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone

247 h of electronic medical records coupled with machine learning presents an opportunity to improve the

248 The machine learning produced highly accurate and robust cla

249 When these challenges are met, machine learning promises a future of rigorous, outcomes

250 Machine learning promises to revolutionize clinical deci

251 erein Raman spectroscopy in combination with machine learning provides the first glimmer of hope for

252 Machine learning ranking (learning to rank) is a class o

253 n UKBiobank brain images against established machine learning references.

254 nd retinal morphology) using correlation and machine learning regression.

255 pEF, and a multimarker approach coupled with machine-learning represents a promising strategy for enh

256 utional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in

257 Machine learning scoring functions for protein-ligand bi

258 also envisage the potential of applying our machine learning strategy to other diseases and purposes

259 In the largest multimodal 7 tesla machine learning study to date, we overcome this limitat

260 , the model-based strategy targeted 27%, and machine learning targeted 18%.

261 Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs).

262 Previous work using machine learning techniques suggested that ASD detection

263 In general, traditional shoreline models and machine learning techniques were able to reproduce shore

264 een analyzed, using a wide array of advanced Machine Learning techniques, to quantify how regulated r

265 Our study suggests that machine learning techniques, when combined with rigorous

266 ss the promises and pitfalls of the involved machine learning techniques.

267 on computing correlation functions or using machine learning techniques.

268 dologies, tests for convergent evolution and machine learning techniques.

269 Machine-learning techniques are more and more often appl

270 C-P with Raman micro-spectroscopy (RmuS) and machine learning technology following a protocol suitabl

271 and to generate classification models using machine learning technology.

272 create density functionals using supervised machine learning, termed NeuralXC.

273 current study, we capitalise on advances in machine learning that allow continuous neural data to be

274 researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives

275 hich focuses on using Raman spectroscopy and machine learning to address the need for better screenin

276 enomic and metabolomic platform that employs machine learning to automate the selective discovery and

277 uses optical superresolution microscopy and machine learning to create a quantitative and higher thr

278 simulation, high-performance computing, and machine learning to create a risk estimator to stratify

279 ons with direct long-read RNA sequencing and machine learning to detect secondary structures in cellu

280 We hypothesized that application of machine learning to electrogram frequency spectra may ac

281 his quantitative signal can be combined with machine learning to enable microscopy in diverse fields

282 We applied machine learning to explain how specific interactions co

283 method provides encouragement for the use of machine learning to extract meaningful structural inform

284 Here we apply machine learning to generate a spatial embedding (multid

285 To prioritize functional sites, we used machine learning to identify 59 features indicative of p

286 Applying modern analytical tools such as machine learning to increasingly high dimensional data o

287 These results illustrate the potential of machine learning to inform nanomanufacturing processes.

288 undamentally innovative in that it also uses machine learning to perform cell tracking and lineage re

289 Here, we used EEG and machine learning to study how the brain processes audito

290 Finally, we use machine learning to test whether pairwise trophic intera

291 eraging the power of modern-technologies and machine-learning to this field.

292 the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recogniti

293 gitudinal progression patterns using a novel machine-learning tool called "Subtype and Stage Inferenc

294 With the availability of machine learning tools coupled with vast data sources, t

295 e probabilistically estimated using advanced Machine Learning tools.

296 infrared (mu-FTIR) hyperspectral imaging and machine learning tools.

297 We demonstrate such kernel-based quantum machine learning using specialized multiphoton quantum o

298 Furthermore, fluorescent imaging and machine learning was used to load single K562 cells amon

299 Using machine learning, we extract detailed behavioral statist

300 We developed a machine learning workflow to classify single cells accor