ライフサイエンスコーパス: machine

1 o estimate the trust that humans assign to a machine.

2 into catalysis by this enigmatic respiratory machine.

3 s genome stability as a DNA micro-compaction machine.

4 curity against all adversaries, human and/or machine.

5 Thus, lncRNAs can represent powerful RNA machines.

6 ale cell biology and the design of synthetic machines.

7 design and synthesis of artificial molecular machines.

8 ture use in flexible devices or soft-robotic machines.

9 other AAA+ proteases and protein-remodeling machines.

10 itical for the design of synthetic molecular machines.

11 d hardness categories using a support vector machine algorithm.

12 is performed using nonlinear support vector machine and kernel Fisher discriminant analysis.

13 ery paves the way to elucidate how polar Tfp machines are regulated to coordinate multicellular movem

14 ing, reconstruction and design of biological machine assembly lines.

15 d be built into the microsensor and could be machined at the same time as the sensors themselves, in

16 and methylation types using a support vector machine-based network.

17 en neglected by research comparing human and machine behavior, and that it should be essential to any

18 They function as molecular machines by coupling ATP binding, hydrolysis, and phosph

19 tly improve the efficiency of bionic medical machines by giving them different sensitivities to exter

20 me from a national network of influenza test machines called the Influenza Test System (ITS).

21 cteriophage genomes and a standalone virtual machine containing all the required software and learnin

22 Support vector machine decoding of alpha power patterns revealed that l

23 s and thus provides a valuable component for machine-driven medicinal chemistry design workflows.

24 believed that conserved multisubunit protein machines extract these lipids after their synthesis is c

25 growing body of evidence that AAA+ molecular machines generate translocating forces by a common mecha

26 soil properties and the long-term effects of machine-grading and subsequent restoration of ski runs s

27 The standard actions applied (machine-grading, storage and re-use of topsoil, hydrosee

28 of similar structures for the same molecular machine illustrate the limits of inferring biochemical m

29 -known concept in computer science-committee machines-in the context of memristor-based neural networ

30 markers need to be quantitative and user and machine independent.

31 It highlights the importance of pathologist/machine interactions for the construction of deep-learni

32 o understand the mechanisms supporting human-machine interactions is becoming increasingly pressing.

33 e results have strong implications for Brain Machine Interface applications and for study of populati

34 behavior and have been used to control Brain Machine Interfaces with varying degrees of success.

35 ding electrodes are regularly used for Brain Machine Interfaces, but the information content varies d

36 ning, offers multi-class and versatile human-machine interfaces.

37 and scientists as a means to optimise human-machine interfaces.

38 algorithm is molecule generation, where the machine is required to design high-quality, drug-like mo

39 of expert knowledge and its integration into machine-learned models.

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

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

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

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

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

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

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

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

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

49 Machine learning (ML) may guide interventions to reduce

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

51 Machine learning (ML) provides powerful dimensionality r

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

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

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

55 The machine learning algorithm classified with 81% overall a

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

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

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

59 es and optimising a multivariate statistical machine learning algorithm.

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

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

62 Machine learning algorithms are then typically employed

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

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

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

66 Machine learning algorithms trained to predict the regul

67 Multiple machine learning algorithms were applied to predict assa

68 Machine learning algorithms were developed to identify a

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

70 Machine learning analysis demonstrates that baseline pla

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

72 Advances in machine learning and contactless sensors have given rise

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

74 Applications of machine learning and graph theory techniques to neurosci

75 Machine learning and high-throughput computational scree

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

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

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

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

80 A novel machine learning approach generated severity estimates t

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

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

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

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

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

86 90%, the precision for LASSO was 65% and the machine learning approach was 74%, while the specificity

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

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

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

90 We apply machine learning approaches to a comprehensive vascular

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

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

93 Machine learning approaches to modeling of epidemiologic

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

95 Machine learning augments burn sepsis prediction.

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

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

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

99 Machine learning can be used to improve surgical risk pr

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

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

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

103 Finally, machine learning classification algorithms applied to gr

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

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

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

107 Machine learning feature selection methods are needed to

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

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

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

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

112 We used machine learning for processing laboratory findings of 1

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

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

115 Although machine learning has enabled unbiased analysis of behavi

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

117 Machine learning identified different cognitive profiles

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

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

120 Machine learning is a branch of computer science and sta

121 Machine learning is a powerful tool for creating computa

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

123 The machine learning medial prefrontal cortex-posteromedial

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

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

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

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

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

129 Bayesian machine learning methods can be used for prospective pre

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

131 Standard supervised machine learning methods enabled development of discrimi

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

133 Various machine learning methods have been proposed, including a

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

135 Modern machine learning methods may be used to probe relationsh

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

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

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

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

140 The machine learning model group selected for further studie

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

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

143 The machine learning model pretrained on Fourier spectrum fe

144 The machine learning model that considered both radiomic and

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

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

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

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

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

150 The resulting machine learning models and segregation database are key

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

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

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

154 Machine learning models have the potential to address th

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

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

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

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

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

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

161 We used four machine learning models to produce flood susceptibility

162 Using machine learning models trained on data from the simulat

163 Machine learning models using transporter gene frequenci

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

165 Machine learning models utilizing volatile organic compo

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

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

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

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

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

171 eepHE significantly outperforms the compared machine learning models.

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

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

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

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

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

177 The machine learning produced highly accurate and robust cla

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

179 n UKBiobank brain images against established machine learning references.

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

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

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

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

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

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

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

187 on computing correlation functions or using machine learning techniques.

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

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

190 and to generate classification models using machine learning technology.

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

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

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

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

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

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

197 We applied machine learning to explain how specific interactions co

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

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

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

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

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

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

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

205 e probabilistically estimated using advanced Machine Learning tools.

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

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

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

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

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

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

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

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

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

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

216 Combined with machine learning, morphometric markers form intuitive vi

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

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

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

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

221 Using machine learning, we extract detailed behavioral statist

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

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

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

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

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

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

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

229 Machine learning-driven, physics-based simulations provi

230 of MeONP-induced inflammatory potential via machine learning.

231 ewpoints and leveraging recent advances from machine learning.

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

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

234 as well as opportunities and challenges for machine learning.

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

236 collects >2000 behavioral features based on machine learning.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

257 0 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmen

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

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

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

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

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

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

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

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

266 2000 combinations with less than 10 mg, and machine-learning-enabled autonomous experimentation iden

267 eveloped a two-layer Light Gradient Boosting Machine (LightGBM) ensemble model that integrates severa

268 Despite using additional energy for machine manufacturing and fuel consumption, the mechaniz

269 The gradient boosting machine model for bacteremia had significantly higher ar

270 We use forecasts made in the absence of machine models as prior beliefs to quantify the weights

271 he logistic regression and gradient boosting machine models were trained on the earliest 80% of hospi

272 the method, random forest and support vector machine models were trained on within-participant single

273 ether supplemental oxygen during hypothermic machine perfusion (HMP) could improve the outcome of kid

274 considered a limit of hypothermic oxygenated machine perfusion (HOPE).

275 The benefits of cold pulsatile machine perfusion (MP) for the storage and transportatio

276 Machine perfusion (MP) is at the forefront of innovation

277 Normothermic machine perfusion (NMP) bears the potential for signific

278 Hypothermic machine perfusion is systematically used.

279 60 min of warm ischemia, before hypothermic machine perfusion.

280 ations of mass spectrometry, particularly as machine precision continues to improve.

281 XGBoost, k-nearest neighbor, support vector machine, random forest, logistic regression and Naive Ba

282 rent challenge at the forefront of molecular machine research.

283 d skin and nasal samples from workers in the machine shop area were enriched with Pseudomonas, the do

284 Here, a 5-axis MiRA6 CNC milling machine, specifically designed for the jewelry industry,

285 Four independent support vector machine (SVM) models performed to ensure the BDNF level

286 random forest (RF), nonlinear support vector machine (SVM), and deep neural network (DNN) learning ar

287 Support vector machines (SVM) models can be successfully applied in thi

288 scriminant Analysis (QDA) and Support Vector Machines (SVM), analyzed in the biofingerprint region be

289 s (RFs), elastic net (ELNET), support vector machines (SVMs) and boosted trees in combination with po

290 (250 to >750 kDa), conserved macromolecular machines that are essential for soluble N-ethylmaleimide

291 sire to synthesize surface-rolling molecular machines that can be translated and rotated with extreme

292 aches for the design of autonomous molecular machines that emulate nature's finest examples.

293 sted physical system (a digital memcomputing machine) that, when its non-linear ordinary differential

294 creased throughput by reducing the necessary machine time.

295 -FLAIR) imaging with a 3T magnetic resonance machine to study cerebral glymphatics and meningeal lymp

296 his accuracy exceeded that of support vector machines, traditional linear discriminant, and k-nearest

297 Taking a cue from recent advances in machine translation, we train a recurrent neural network

298 Here, we use a machine-vision-based single-particle analysis (SPA) meth

299 SibeliaZ runs in under 16 hours on a single machine, while other tools did not run to completion for

300 est (0.782), XGBoost (0.781), support vector machine with linear kernel (0.780), and [Formula: see te