ライフサイエンスコーパス: machine

1 some" as a homogeneous, monolithic molecular machine.

2 y of a broadly distributed protein secretion machine.

3 red for the assembly of the entire secretion machine.

4 , which functions as a needle-like molecular machine.

5 free-energy landscape of this conformational machine.

6 translation using this artificial molecular machine.

7 Here, we model this machine.

8 potential applications of the Hallucination Machine.

9 Software has only been tested on Windows machines.

10 wth signals and the assembly of key cellular machines.

11 rce for molecular rotary motors in nanoscale machines.

12 nd were used for a large number of molecular machines.

13 e another step towards autonomously learning machines.

14 kes inspiration from these tiny but powerful machines.

15 can be used to engineer self-organized soft machines.

16 ive systems in areas where humans outperform machines.

17 ircuits versus more general-purpose learning machines.

18 Following similar work on T4aP machines(8,9), here we use electron cryotomography(10) t

19 The AMO WhiteStar Signature Pro machine (Abbott Medical Optics) with the Ellips FX handp

20 ter, principal component, and support vector machine analyses.

21 n model definition shifts from programmer to machine and opens up the model parameter space for inclu

22 ncRNA data sets compared with support vector machine and traditional neural network.

23 modifying a commercial 8-channel pipet using machined and 3D-printed components.

24 is essential for the operation of macroscale machines and microfluidic devices.

25 afe and powerful actuation for myriad common machines and robots.

26 ti-particle trapping and manipulation, laser machining, and laser material processing.

27 ides a basis for constructing efficient nano-machines applied to biological processes.

28 d C2) and outline how they may inspire novel machine architectures.

29 machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and

30 The core elements of this multi-protein machine are the envelope-associated needle complex, the

31 lecular modeling and comparative analysis of machines assembled with protein-tagged components or fro

32 ign and synthesis of shuttles, switches, and machines at the nanoscale.

33 ential for the design of optimized molecular machines based on light-driven rotary motion.

34 atial distribution of the substrates of this machine before secretion.

35 elements of innovative materials, molecular machines, biological probes, and even commercial medicin

36 Here we show that molecular machines can drill through cellular bilayers using their

37 Keratoconus indices measured by all 3 machines can effectively differentiate keratoconus from

38 d cargo adaptor proteins to form a transport machine capable of long-distance processive movement alo

39 ynthetic micro/nanomotors are self-propelled machines capable of converting the supplied fuel into me

40 computational framework uses support vector machines combined with transfer machine learning and fea

41 The ribosome is a complex macromolecular machine composed of RNA and proteins and it is responsib

42 Evolution of a nano-machine consisting of multiple parts, each with a specif

43 The HslUV proteolytic machine consists of HslV, a double-ring self-compartment

44 The combination of large amounts of machine-consumable digital data, increased and cheaper c

45 Biomolecular machines consume free energy to break symmetry and make

46 A BioArtificial Liver machine could temporarily replace the functions of the l

47 AAA+ proteases and remodeling machines couple hydrolysis of ATP to mechanical unfoldin

48 ational changes of a translational molecular machine during its operation.

49 tes dynamic recruitment of specific cellular machines during different stages of transcription.

50 ak (DSB) by a multisubunit helicase-nuclease machine (e.g. RecBCD, AddAB or AdnAB) generates the requ

51 eatures to predictive models (support vector machine, elastic net) and validated those models in an i

52 he flagellar motor is a sophisticated rotary machine facilitating locomotion and signal transduction.

53 del was transferred into a numerical control machine for manufacturing the personalized titanium plat

54 we present the use of ethoscopes, which are machines for high-throughput analysis of behavior in Dro

55 uantum computing algorithms and the physical machines foreseen within the next ten years.

56 rotaxane was designed to perform a molecular machine function of contraction and expansion utilizing

57 Gradient Boosting Machine (GBM) approach was used to explain the non-linea

58 Artificial molecular machines have been developed for tasks that include sequ

59 heir environment, and CRISPR-based molecular machines have been repurposed to enable a genome editing

60 dependence on the cytoskeleton and signaling machines have been studied extensively in cultured cells

61 Type III protein secretion machines have evolved to deliver bacterially encoded eff

62 l information for this vital protein-folding machine in humans.

63 hree-dimensional in situ structure of a T4bP machine in its piliated and non-piliated states.

64 ization of the Salmonella type III secretion machine in live bacteria by 2D and 3D single-molecule sw

65 the molecular architecture of the secretion machine in situ and localized its structural components.

66 hiometry of the different components of this machine in situ, and the spatial distribution of the sub

67 into the Chimera architecture of the D-Wave machine, initiating an approach to reversible classical

68 s on neural plasticity associated with brain-machine interface (BMI) exposure have primarily document

69 ts, the neural control engineering and brain-machine interface studies.

70 A human-machine interface that uses non-invasive, electrophysiol

71 However, the existing machine-interface platforms are obtrusive, uncomfortable

72 ol, it may serve in the development of brain machine interfaces that also use ipsilateral neural acti

73 ty of this internal model estimate for brain-machine interfaces, we performed an offline analysis sho

74 e environment - the requisite of a molecular machine - is more likely than completion in a direction

75 e first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and

76 he authors perform such a simulation using a machine-learned density functional that avoids direct so

77 AD identification problem as an unsupervised machine learning (clustering) problem, and develop a new

78 A machine learning (ML)-enabled image-phenotyping pipeline

79 ost network-based DTI prediction is based on machine learning - methods such as Restricted Boltzmann

80 and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with

81 After applying a machine learning algorithm with input variables that acc

82 profiling of behavior by using a supervised machine learning algorithm, are able to deliver behavior

83 els have been developed, while nonparametric machine learning algorithms are used less often and nati

84 Machine learning algorithms helps to narrow down the sea

85 logous groups identified in OrthoDB to train machine learning algorithms that are able to distinguish

86 Deep learning is a group of machine learning algorithms that use multiple layers of

87 mentation, particularly the use of ensembled machine learning algorithms.

88 those of preceding predictors incorporating machine learning algorithms.

89 functionals for realistic molecular systems.Machine learning allows electronic structure calculation

90 Interestingly, machine learning also reveals that certain compositions

91 Machine learning analyses identified immune response com

92 of nodes in a network have a rich history in machine learning and across domains that analyze structu

93 re classified into three different states by machine learning and all found to be distributed homogen

94 e-scale functional data can be combined with machine learning and clinical knowledge for the developm

95 Machine learning and correlational methods are increasin

96 b based knowledge bases in biology to use in machine learning and data analytics.

97 the iHMM's breadth in applicability outside machine learning and data science warrants a careful exp

98 pport vector machines combined with transfer machine learning and feature selection.

99 rm for the realization of smart memories and machine learning and for operation of the complex algori

100 The development of machine learning and network structure study provides a

101 Our machine learning approach based on a decision tree algor

102 A machine learning approach revealed important differences

103 sion tensor imaging, we used an unsupervised machine learning approach to combine cognitive, diffusio

104 We utilize the randomforest machine learning approach to estimate the surface exposu

105 lt and pheromone stimulation and developed a machine learning approach to explore regulatory associat

106 Here we applied a machine learning approach to identify immune signatures

107 We developed a machine learning approach using support vector machines

108 Using a powerful machine learning approach, a recent study of human genom

109 Here, we describe a machine learning approach, called HIPred, that integrate

110 orm infrared (FT-IR) microscopy coupled with machine learning approaches has been demonstrated to be

111 Statistical and machine learning approaches predict drug-to-target relat

112 Statistical and machine learning approaches were applied to demonstrate

113 d unacetylated proteins and more recently by machine learning approaches.

114 iversity of immune receptors and widely used machine learning approaches.

115 We have provided the first comprehensive machine learning based classification of protein kinase

116 ing supported by evolutionary restraints and machine learning can be used to reliably identify and mo

117 where the c-di-GMP network is analogous to a machine learning classifier.

118 ideration for compatibility with the broader machine learning community by following the design of sc

119 Conclusion An automated machine learning computer system was created to detect,

120 The field of quantum machine learning explores how to devise and implement qu

121 array of clustering methods developed in the machine learning field to the TAD identification problem

122 ent method, which combines RI tomography and machine learning for the first time to our knowledge, co

123 ing transcription factor binding motifs in a machine learning framework, we identify EOR-1 as a uniqu

124 acy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its depende

125 g with advances in deep neural networks with machine learning has provided a unique opportunity to ac

126 Machine learning holds the promise of learning the energ

127 Combining computational biology and machine learning identifies protein properties that hind

128 Machine learning is a means to derive artificial intelli

129 RATIONALE: Machine learning may be useful to characterize cardiovas

130 learning, highlighting that incorporation of machine learning may outperform parametric regression in

131 Deep learning as the cutting-edge machine learning method has the ability to automatically

132 d; we also propose a novel multiple-instance machine learning method that uses sequence composition a

133 We used a cross-validating machine learning method to select predictor variables fr

134 -of-function genetic variation and develop a machine learning method, MutPred-LOF, for the discrimina

135 failure within 30 days were selected using a machine learning methodology.

136 s were evaluated by the performance of three machine learning methods (support vector machines (SVMs)

137 n A, B, Cpi, LASSO, Ridge Regression and two machine learning methods (SVM and Random Forest) were us

138 ow be enabled by the rational combination of Machine Learning methods and materials databases.

139 Using machine learning methods and the best-available data fro

140 Complex machine learning methods are avoided to keep the algorit

141 ve hybrid model (CSHM) and five conventional machine learning methods are used to construct the predi

142 Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art

143 We hypothesize that machine learning methods based on word frequencies can b

144 p survival models and other state of the art machine learning methods for survival analysis, and desc

145 Machine learning methods have shown that local structure

146 a at such scales has brought statistical and machine learning methods into the mainstream.

147 ion models for over 280 kinases by employing Machine Learning methods on an extensive data set of pro

148 ET diagnosis, applying feature selection and machine learning methods to a study sample of 18 patient

149 The average accuracy of machine learning methods was 0.32, compared to 0.31 achi

150 oosing Random Forest over alternative tested Machine Learning methods, and (3) balancing the training

151 of standard-model processes was assisted by machine learning methods.

152 A machine learning model called gradient boosting tree ens

153 This study provides proof of concept that a machine learning model can be applied to predict the ris

154 Purpose To develop a machine learning model that allows high-risk breast lesi

155 Peddy predicts a sample's ancestry using a machine learning model trained on individuals of diverse

156 A random forest machine learning model was developed to identify HRLs at

157 ion, and pCRE combinatorial relationships in machine learning models and found that only consideratio

158 Machine learning models trained by texture features on D

159 Performance comparison among machine learning models with features identified by diff

160 The supervised machine learning neural network developed is able to gen

161 tion, rely on, and are therefore limited by, machine learning of sequence patterns in known experimen

162 Here we demonstrate that systematic machine learning of the wave function can reduce this co

163 nsion could be predicted by using supervised machine learning of three-dimensional patterns of systol

164 Machine learning on carefully-chosen training sequences

165 elopment of intelligent data analysis from a machine learning perspective provides exciting opportuni

166 ntary information from both co-evolution and machine learning predictions.

167 Numerical ecology analyses and a machine learning procedure were used to analyze the data

168 hms that could act as the building blocks of machine learning programs, but the hardware and software

169 A machine learning random forest model was developed with

170 m the University of California, Irvine (UCI) Machine Learning Repository, and a clinical data set bui

171 Using a machine learning system, we succeeded in classifying the

172 e used for a variety of downstream proteomic machine learning tasks.

173 y and poses additional challenges for common machine learning tasks.

174 tempt to address this question by applying a machine learning technique to SP whole genomes.

175 A Bayesian machine learning technique, called Graphical Model-based

176 st the ability of random survival forests, a machine learning technique, to predict 6 cardiovascular

177 We apply machine learning techniques from the natural language pr

178 ing computer power and algorithmic advances, machine learning techniques have become powerful tools f

179 Machine learning techniques provide a vast array of tool

180 We use machine learning techniques to create an accurate pan-ca

181 implement quantum software that could enable machine learning that is faster than that of classical c

182 isting nodule segmentation algorithms employ machine learning that trains a classifier to segment the

183 structural quantity, "softness," designed by machine learning to be maximally predictive of rearrange

184 Our software relies on machine learning to devise robust algorithms, and includ

185 Previous studies have applied machine learning to facilitate processing of mass cytome

186 Using these 113 metabolites, combined with machine learning to segregate mice based on insulin sens

187 ced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of

188 at identifies optimal sorting gates based on machine learning using positive and negative control pop

189 Machine learning with SVM allows automatic and accurate

190 f novel methods (e.g., polygenic approaches, machine learning) that enhance the quality of imaging ge

191 onal techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel

192 feature selection based on reproducibility, machine learning, and correlation analyses were performe

193 lecular modeling, structural bioinformatics, machine learning, and functional annotation filters in o

194 melt curves are identified by Support Vector Machine learning, and individual pathogen loads are quan

195 al features and which exploits random forest machine learning, comparing its performance with a numbe

196 utational methods, developed in the field of machine learning, offer new approaches to leveraging the

197 Using machine learning, we assessed the features that best def

198 Yet, machine learning, which supports this care process has b

199 e extraction, selection, and benchmarking of machine learning-based models, thereby contributing to a

200 eraction of workers via antennation) using a machine learning-based system.

201 l using refractive index (RI) tomography and machine learning.

202 id approach that combines rule induction and machine learning.

203 a using both a heuristic approach as well as machine learning.

204 A machine-learning algorithm called linear discriminant an

205 d crystallization was probed using an active machine-learning algorithm developed by us to explore th

206 out the combined activity of all neurons, a machine-learning algorithm reliably decode the motion di

207 When trained on these features, machine-learning algorithms achieve blind single cell cl

208 ch provide a useful framework for developing machine-learning algorithms for modular and hierarchical

209 bining sequence and energetic patterns using machine-learning algorithms further improves classificat

210 ory psychophysical data set, teams developed machine-learning algorithms to predict sensory attribute

211 Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data

212 performance quality as the state of the art machine-learning applications with multiple tuning param

213 and to identify predictive variables using a machine-learning approach based on random survival fores

214 val forests are a simple and straightforward machine-learning approach for prediction of overall surv

215 A study by Lupolova et al. applied a machine-learning approach to complex pan-genome informat

216 It uses a machine-learning approach to extract discriminant inform

217 The second part of the paper describes a machine-learning approach to the identification and anal

218 ned an identity using a new time-independent machine-learning approach we call Neuron Registration Ve

219 We have combined a machine-learning approach with other strategies to optim

220 given RNA-seq data of any bacterium using a machine-learning approach.

221 Our overall goal is to develop machine-learning approaches based on genomics and other

222 Machine-learning approaches were used to identify releva

223 , better than comparable methods built using machine-learning approaches, highlighting the strength o

224 Using statistical machine-learning approaches, we showed that adding EP as

225 each cluster were estimated separately using machine-learning approaches.

226 State-of-the-art performance comparable to machine-learning based systems was achieved in the three

227 To bridge this gap, machine-learning methods can be trained to use the gene

228 Using classical epidemiology and machine-learning methods in 16,147 children aged 4 years

229 tally reported in both structure types, this machine-learning model correctly identifies, with high c

230 est, using a widely used, purely statistical machine-learning model trained on a standard corpus of t

231 The supervised machine-learning model was first trained with 1037 indiv

232 ical and treatment data and encoding it in a machine-learning readable format, we built a prognosis p

233 aper we describe HistomicsML, an interactive machine-learning system for digital pathology imaging da

234 We used a machine-learning technique on brain imaging data to pred

235 Furthermore, implementation of machine-learning techniques for sorting class averages o

236 n be accurately predicted using data-mining, machine-learning techniques.

237 In healthy humans, machine-learning-based analysis of high-resolution cardi

238 The most prominent machine-learning-selected and -weighted features were pa

239 future generations of programmable molecular machines may have significant roles in chemical synthesi

240 Our support vector machine model could be trained effectively on large publ

241 ackages, available as a downloadable virtual machine or as a Platform-as-a-Service supported by a ded

242 te continous positive airway pressure (CPAP) machines or intraoral devices.

243 ke was assessed through the use of a vending machine paradigm and snack food taste tests (SFTTs).

244 n, outcomes of a first clinical normothermic machine perfusion (NMP) liver trial in the United Kingdo

245 Machine perfusion techniques have decreased delayed graf

246 s underwent continuous hypothermic pulsatile machine perfusion until transplant: 69 with simultaneous

247 sefulness of the Ion Torrent Personal Genome Machine (PGM) in food traceability analyzing candies as

248 rning - methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM).

249 in multiple scientific "dialects", including machine-readable and lay-friendly formats.

250 Methods We downloaded publicly available machine-readable network data and public use files for i

251 eature enrichment and reporting a human- and machine-readable text label.

252 amming gradient of temperature in the coffee machines recently introduced in the market opens new exp

253 novel statistical approach, Bayesian kernel machine regression (BKMR), to study the joint effect of

254 ction of protein-nucleic acid macromolecular machines requires multidimensional molecular and structu

255 t vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are

256 rk, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ad

257 We argue that human-like machines should be designed to make decisions in transpa

258 ylfuran signatures derived from the smoke of machine-smoked cigarettes.

259 tivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresse

260 nation Analysis "PLS-DA" and Support Vectors Machines "SVM"), was able to discriminate the wine vineg

261 We have developed CellSort, a support vector machine (SVM) algorithm that identifies optimal sorting

262 pathological diagnosis using support vector machine (SVM) algorithms.

263 The support vector machine (SVM) method is also presented for comparative p

264 d open questions using linear support vector machine (SVM) resulted in an above-chance-level correct

265 chine learning approach using support vector machines (SVM) for automatic VP placement.

266 (NN), fuzzy models (FM), and support vector machines (SVM) to predict physicochemical composition of

267 d Boltzmann Machines (RBM) or Support Vector Machines (SVM).

268 ristics Curve (AUC) using the Support Vector Machines (SVM).

269 ree machine learning methods (support vector machines (SVMs), multilayer perceptrons (MLP), and C4.5)

270 ultivariate predictive models using learning machine techniques.

271 are then used to construct a support vector machine that can be used for accurate prediction of nove

272 ia coli) using an efficient genomic delivery machine that is driven by elastic energy stored in a con

273 ATPase (V-ATPase) is a complex multisubunit machine that regulates important cellular processes thro

274 B-TolC tripartite complex is a transmembrane machine that spans both plasma membrane and outer membra

275 hed ImmunoGlobulin Galaxy (IGGalaxy) virtual machine that was developed to visualize V(D)J gene usage

276 Moreover, each of the essential OM machines that assemble the barrier requires one or more

277 sion would help move research even closer to machines that can learn and think like humans.

278 retroelements (DGRs) are molecular evolution machines that facilitate microbial adaptation to environ

279 Machines that learn and think like people should simulat

280 Lake et al. proposed a way to build machines that learn as fast as people do.

281 Machines that simultaneously process and store multistat

282 findings pave the way toward supramolecular machines that would photogenerate pulling forces, at the

283 An essential part of this machine, the large terminase protein, processes viral DN

284 d the subcellular distribution of individual machines, the stoichiometry of the different components

285 mbrane protein that functions as a molecular machine to control the cholesterol content of membranes

286 ticated statistical models combine to enable machines to find patterns in data in ways that are not o

287 n speech recognition processes in humans and machines, using novel multivariate techniques to compare

288 Successful operation of the machine via native chemical ligation (NCL) demonstrates

289 As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analys

290 o relevant parameters are calibrated using a machine vision-based method.

291 Recording Capillary Feeder or CAFE (ARC), a machine-vision (automated image tracking)-based system f

292 eceptor being an 'imperfect' photon counting machine, we explain how these constraints give rise to a

293 CPAP machines were allocated to one hospital during each stud

294 nnotated sequences through the deep learning machine, which indicates that deep learning method has t

295 a first step toward the realization of such machines, which will require biological actuators that c

296 gn the next generation of complex biological machines with controllable function, specific life expec

297 ndicating that flux is enhanced in molecular machines with fewer states.

298 ct the conditions under which we can develop machines with general intelligence as well.

299 tures for the design of functional molecular machines with unprecedented properties.

300 nts underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred foo