ライフサイエンスコーパス: supervise

1 ng paradigm (ADP) before and after 1 week of supervised 100 mg daily oral naltrexone.

2 week quarantine at home followed by a second supervised 2-week quarantine at a closed college campus

3 Current supervised AI methods require a curation process for dat

4 Moreover, supervised algorithms often fail to take advantage of la

5 he two groups, which were then classified by supervised algorithms.

6 Currently, two types of supervised analyses are mainly used on MSI data: pixel-w

7 related species, using both unsupervised and supervised analyses, led us to detect lineage-specific v

8 ing Machine (LLM) is an innovative method of supervised analysis capable of constructing models based

9 ng an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample group

10 Supervised analysis of 77 primary tumors with paired met

11 alysis while offering the ability to perform supervised analysis.

12 method is competitive with state-of-the-art supervised and semi-supervised predictive systems.

13 We experimented both with supervised and semi-supervised pretraining, leading to i

14 Non-supervised and supervised statistical analyses revealed

15 ethylation, ATAC-seq, and multi-omic data in supervised and unsupervised analyses, showing that COCOA

16 oncordance with pathology using a variety of supervised and unsupervised clustering methods.

17 eural computations, as well as the basis for supervised and unsupervised learning, is the ability to

18 ed data in model training by performing both supervised and unsupervised learning.

19 n of such a neurosynaptic system, capable of supervised and unsupervised learning.

20 Both supervised and unsupervised machine-learning algorithms

21 oaches can be divided into two major groups: supervised and unsupervised methods.

22 We use methods from supervised and unsupervised ML to efficiently create int

23 In both supervised and unsupervised modes, this allowed us to ac

24 Computationally, we consider supervised and unsupervised statistical approaches to id

25 Untargeted metabolomic profiling followed by supervised and unsupervised statistics allowed understan

26 ide variety of clustering methods, both semi-supervised and unsupervised, have been developed for dat

27 leviate this challenge, we contribute a semi-supervised approach mi-CNN.

28 However, the success of such supervised approach relies heavily on the availability o

29 earning using AuTOml (PLATO), a general semi-supervised approach to improving accuracy of model-based

30 ures with higher average scores from several supervised as well as unsupervised feature ranking algor

31 Two observers supervised automated quantification of T2-weighted high-

32 We propose a semi-supervised Bayesian approach to novelty detection, allow

33 predictive performance competitive with the supervised benchmark methods and provides interpretable

34 erating room and specific physical exercises supervised by a physical therapist.

35 parallel factor analysis (PARAFAC), PARAFAC supervised by linear discriminant analysis, and discrimi

36 statin medications recommended by NPHWs but supervised by physicians; and support from a family memb

37 To supervise cell identity, Hey determines the expression o

38 pendent computational methods are needed for supervised cell type annotation of single-cell RNA seque

39 We present SciBet, a supervised cell type identifier that accurately predicts

40 Passing a supervised challenge, a surrogate for preventing out-of-

41 build models including exploratory analysis, supervised classification and multivariate calibration.

42 the other hand, existing deep learning based supervised classification approaches are highly demandin

43 den shifts, and small sudden spikes, whereas supervised classification had higher accuracy for predic

44 The proposed node embedding followed by a supervised classification improves the predictive perfor

45 s and train CMNs to identify glia cells in a supervised classification paradigm, which are then used

46 body, and subjective experience-and compare supervised classification solutions with those from unsu

47 e decision boundaries for standard nonlinear supervised classification tasks in feature space.

48 We found that semi-supervised classification was better able to detect sudd

49 eggs by Raman spectroscopy and multivariate supervised classification with Partial Least Squares Dis

50 referred to as DeepType, that performs joint supervised classification, unsupervised clustering and d

51 This can be achieved by supervised classification, which takes as input MSI spec

52 ated state of the tissues with random forest supervised classification.

53 hile maintaining manual selection review and supervised classification.

54 Trained on these extracted features only, a supervised classifier, DeepC, can effectively distinguis

55 chnical framework of training the expandable supervised-classifier in order to reveal the single-cell

56 Supervised classifiers trained on histologic rejection s

57 In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to att

58 Lapco provided competency-based supervised clinical training for specialist colorectal s

59 mokers, and COPD patients and employed a non-supervised clustering algorithm to define and detect cha

60 ep learning classification with automated un-supervised clustering for generating training data provi

61 Supervised clustering with markers of boundary cells and

62 umoural progression area was done by using a supervised convolutional neural network.

63 There was significant variation in supervised CR dose by region (P<=0.001), with the Americ

64 Correlates of supervised CR dose included more involvement of physicia

65 This study characterized the dose offered in supervised CR programs and alternative models worldwide

66 Globally, supervised CR programs were a median of 24 sessions (n=6

67 Supervised data analysis involved the use of 3 different

68 om social media data seems to be robust when supervised data-driven methods are used.

69 This combination of unsupervised and supervised data-driven tools provides a framework that c

70 In this paper, we propose a semi-supervised deconvolution method, semi-CAM, that extends

71 Supervised deconvolution methods use cell type-specific

72 Our framework combing supervised deep learning classification with automated u

73 light the current and future applications of supervised deep learning in environmental conservation.

74 We develop a weakly supervised deep learning model for classification of aor

75 cturally annotated protein sequences, a semi-supervised deep learning model that unifies recurrent an

76 The semi-supervised deep learning Pi model and the cosine score a

77 loying age-prediction models developed using supervised deep learning techniques, we found that smoke

78 Specifically, FRA provides a supervised discovery of metabolites that express a stati

79 scriminative fingerprints (Q(2) of 0.784 for supervised discriminant analysis).

80 r role in the study dispensed, prepared, and supervised dosing of the study drugs.

81 ompared assessments made in unsupervised and supervised (eg, in the laboratory or hospital) settings

82 ation of endovascular revascularization plus supervised exercise (n = 106) or supervised exercise onl

83 wk to a no-exercise control group or 1 of 2 supervised exercise groups: 8 kcal/kg of body weight/wk

84 ting LT to a 6-week period of thrice weekly, supervised exercise on a static bike.

85 zation plus supervised exercise (n = 106) or supervised exercise only (n = 106).

86 m follow up after combination therapy versus supervised exercise only, demonstrated no significant di

87 for intermittent claudication, compared with supervised exercise only.

88 ombining endovascular revascularization plus supervised exercise shows promising results, however the

89 Supervised exercise therapy (SET) improves walking abili

90 Supervised exercise therapy is recommended as first-line

91 eak oxygen consumption (VO(2)peak) to 1 of 2 supervised exercise training interventions delivered wit

92 (n = 9) completed 14 weeks of controlled and supervised exercise training.

93 We found that supervised exercise was associated with an increased haz

94 services, which are provided in a medically supervised facility, HBCR relies on remote coaching with

95 trained a deep neural network prototype in a supervised fashion using 884 older children and adult su

96 e been analyzed in a human-expert-intensive, supervised fashion.

97 -based manualised intervention, delivered by supervised graduate psychologists to staff in six intera

98 pose Look4TRs, the first application of self-supervised hidden Markov models to discovering microsate

99 uent relapsing P. vivax, failure rates after supervised high-dose primaquine are significantly lower

100 In contrast, after supervised high-dose primaquine only 1 in 40 relapse.

101 ogrammes: Home-HIT (n = 9), laboratory-based supervised HIT (Lab-HIT; n = 10) or virtually supervised

102 Therefore, we developed a virtually supervised HIT protocol targeting this group that can be

103 uscle microvascular endothelium as virtually supervised home-based MICT and laboratory-based HIT, whi

104 upervised HIT (Lab-HIT; n = 10) or virtually supervised home-based moderate-intensity continuous trai

105 the first to show that 12 weeks of virtually supervised Home-HIT in obese individuals with elevated c

106 irect supervision during exercise, virtually supervised Home-HIT resulted in comparable structural an

107 Census tract-level supervised HVI values were positively associated with in

108 Supervised HVIs may provide marginally more specific ind

109 Unsupervised and supervised HVIs yielded differing spatial vulnerability

110 achine learning algorithm referred to as the supervised inference of feature taxonomy from ensemble r

111 all CRE carriers and events of acquisition, supervised information exchange between healthcare facil

112 However, a fall in FeNO following supervised inhaled corticosteroid dosing could indicate

113 eatments, there is accumulating evidence for supervised injectable opioid treatment (prescribing phar

114 yringe programmes, pre-exposure prophylaxis, supervised injection facilities, and, to a lesser extent

115 participants received on average 12 (SD 11) supervised inpatient sessions using 4 (SD 1) different d

116 cellular fluxes of inorganic phosphate (Pi) supervises its structural roles in bones and teeth, its

117 Vector Machine (SVM) was trained to perform supervised land cover classification at a 1-m resolution

118 A modified variable importance score from a supervised learner is used to detect important variables

119 lly, we used computer simulations to train a supervised learning algorithm to detect the dynamical fo

120 ext]O emissions from the ocean by training a supervised learning algorithm with over 158,000 [Formula

121 mbining SERS spectra encoding technique with supervised learning algorithm, a panel of nucleotide mix

122 ata set and evaluating the performance of 12 supervised learning algorithms on the normalized data se

123 undergone a revolution, catalyzed by better supervised learning algorithms.

124 ARting DIsease Gene AssociatioNs), uses semi-supervised learning and exploits a measure of similarity

125 challenge are computational methods, such as supervised learning and label propagation, that can leve

126 Data were analyzed using unsupervised and supervised learning and other statistical methods.

127 Capture Hi-C, and high-throughput imaging, a supervised learning approach could facilitate the discov

128 In this study, we design a novel supervised learning approach for identifying ChIP-seq pe

129 Supervised learning approaches for TFBS predictions requ

130 y relevant and apply to many applications of supervised learning approaches in bioinformatics.

131 Our method outperforms peer semi-supervised learning approaches, achieving better cross-v

132 ological associations in mouse studies, semi-supervised learning approaches, combining mouse and huma

133 Besides, supervised learning based methods usually need both posi

134 It is unknown how supervised learning broadly performs across different ne

135 work combining a denoising autoencoder and a supervised learning classifier to identify gene signatur

136 a machine-learning-based framework involving supervised learning for diagnostics, risk stratification

137 , we present a comprehensive benchmarking of supervised learning for network-based gene classificatio

138 In the present work, BINNs are trained in a supervised learning framework to approximate in vitro ce

139 eveloped an application of Snorkel, a weakly supervised learning framework, for extracting chemical r

140 semantic features for individual words) and supervised learning from "small data" (to create represe

141 ar machine-learning technique across fields, supervised learning has been applied only in a few netwo

142 argely on deep neural networks trained using supervised learning in tasks such as image classificatio

143 Here, we propose a framework for supervised learning in thin, creased sheets that learn t

144 These results show that supervised learning is an accurate approach for prioriti

145 These results show that although supervised learning is heavily involved in VPL of comple

146 and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmi

147 rained identify the transmission rate (using supervised learning methods).

148 Comparisons between groups, supervised learning methods, and weighted correlation ne

149 nal correlation and the TargetFinder and PEP supervised learning methods.

150 t is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-I

151 data for novel features, select appropriate supervised learning models and optimize their parameters

152 According to supervised learning models that are trained to predict o

153 the corresponding sensor signals learned by supervised learning models.

154 We demonstrate that supervised learning on a gene's full network connectivit

155 We further show that supervised learning on the full network is also superior

156 ile engineering work has mainly proceeded by supervised learning on treebanks of sentences hand label

157 ationships, we have twisted the problem as a supervised learning one and investigated how the compute

158 etaalgorithms that can take advantage of any supervised learning or regression method in machine lear

159 Multiple Instance Learning (MIL), a popular supervised learning paradigm.

160 ry to these methods, we treat filtering as a supervised learning problem, using variant call data wit

161 erimentally derived sequences were used in a supervised learning procedure to train a support vector

162 that climbing fibers operate according to a supervised learning rule to instruct changes in motor ou

163 inantly operates according to an error-based supervised learning rule to refine movements, emerging e

164 alysis to characterize USVs and a principled supervised learning setup.

165 The proposed DRL framework comprises (i) a supervised learning step to predict expert actions, and

166 It is based on a supervised learning strategy that models the residue env

167 ealize high-performance event-driven in-situ supervised learning systems using nanoscale and stochast

168 We combine sparse mixture mapping with supervised learning to achieve bit error rates as low as

169 We then used supervised learning to investigate whether sociodemograp

170 ws a multi-model approach of stochastic semi-supervised learning to rank disease-associated genes thr

171 niques can automatically realize TPU through supervised learning, as called deep learning-based tempo

172 ds beyond the classical view of error-driven supervised learning, as well as open questions that must

173 ailored to specific datasets or are based on supervised learning, which requires comprehensive manual

174 geometry and physicochemical properties for supervised learning.

175 The algorithms solve a problem of supervised learning: the construction of a classifier.

176 t our method outperformed the representative supervised-learning algorithms (including support vector

177 For this purpose, a number of supervised-learning approaches have been proposed to ide

178 To train a conventional supervised-learning model, both reliable positive and ne

179 24-week structured limbs-exercise program (3 supervised limb exercise sessions /week, 60 min /session

180 l 2011 in 19 study areas across Europe, with supervised linear regression (SLR) and random forest (RF

181 M-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifi

182 The supervised machine learning (e.g. deep learning) methods

183 A supervised machine learning (ML) exercise is performed,

184 A supervised machine learning algorithm developed for dete

185 A recently developed semi-supervised machine learning algorithm was used to deline

186 orithms approach the performance of existing supervised machine learning algorithms when tested on a

187 and compare its performance with traditional supervised machine learning algorithms.

188 determine features governing stability using supervised machine learning and Shapley values.

189 risk for Alzheimer's disease), developed by supervised machine learning and validated with data from

190 equencing, and structure-based modeling in a supervised machine learning approach to report the compr

191 In this study, comparative supervised machine learning approaches were applied to t

192 Subsequently, supervised machine learning is applied to transfer the r

193 obiome Interpretable Temporal Rule Engine, a supervised machine learning method for microbiome time-s

194 ecific sites using elastic net regression, a supervised machine learning method.

195 tional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover

196 Standard supervised machine learning methods enabled development

197 a movement imitation task were combined with supervised machine learning methods to classify 22 autis

198 The method trains a system composed of supervised machine learning models for accurate classifi

199 article provides a walkthrough for creating supervised machine learning models with current examples

200 tures were ranked in relation to RFI through supervised machine learning on microbial and metabolite

201 sing convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25%

202 Census: an easily deployed implementation of supervised machine learning that extends convenient 2D '

203 EvalCon uses supervised machine learning to calculate an annotation c

204 l type with unbiased clustering analyses and supervised machine learning to determine how accurately

205 pose a combined approach of unsupervised and supervised machine learning to discriminate the hierarch

206 (DriverML) integrating Rao's score test and supervised machine learning to identify cancer driver ge

207 of assessing probe specificity that employs supervised machine learning to predict probe binding spe

208 strapping, out-of-model outcome measures and supervised machine learning were each used to examine st

209 ramework to create density functionals using supervised machine learning, termed NeuralXC.

210 subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based co

211 method learns a similarity metric through a supervised machine learning-based algorithm such that a

212 eled, which creates barriers to their use in supervised machine learning.

213 cted from the literature and used to train a supervised machine-learning algorithm utilizing boosting

214 We integrated these features with supervised machine-learning algorithms to predict enhanc

215 Here, we develop a supervised machine-learning approach to cluster analysis

216 o that the feature graph can be learned in a supervised manner and specifically constructed for a giv

217 g simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to

218 s better or similar accuracy compared to the supervised method using signature genes, CIBERSORT, and

219 to any CRM prediction method, particularly a supervised method.

220 flowMeans, DEPECHE, and kmeans) and two semi-supervised methods (Automated Cell-type Discovery and Cl

221 of the ten genes that were selected with the supervised methods are involved in metabolism and detoxi

222 Supervised methods can be implemented when training clas

223 ature genes, CIBERSORT, and the marker-based supervised methods semi-NMF and DSA.

224 Supervised methods typically focus on the regions that c

225 ed in cross validation with that of standard supervised methods, namely: decision tree, artificial ne

226 reduction methods on the performance of the supervised ML algorithms.

227 This study presents Vaxign-ML, a supervised ML classification to predict bacterial protec

228 We show that Supervised mode significantly increases statistical powe

229 imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs.

230 Supervised modeling of mass spectrometry imaging (MSI) d

231 By supervised modeling, radiomics features can improve the

232 rather sparse, they are sufficient to train supervised models that capture the complex relationship

233 me prediction are largely based on inductive supervised models that use selected biomarkers to make p

234 es any number of classes through a minimally supervised, modular model-per-class paradigm.

235 Sample t-tests for gene expression data, and supervised multi-variate analysis using orthogonal parti

236 information from scientific literature using supervised natural language processing(3-10), which requ

237 on in the training step by training a weakly supervised neural network using only storage duration ti

238 By properly optimizing supervised neural networks, specifically multilayer perc

239 statistics, whereas choice bias results from supervised or reinforcement learning.

240 been long-standing debates regarding whether supervised or unsupervised learning mechanisms are invol

241 Supervised oral fluid and nasal swab specimens performed

242 o intramuscular adrenaline treatment, during supervised oral food challenges.

243 Principal component analysis (PCA) and supervised partial least squares-discriminant analysis (

244 Supervised pattern learning revealed that hierarchical f

245 ccessfully classified and identified through supervised pattern recognition methods such as soft inde

246 stograms (obtained from digital images), and supervised pattern recognition techniques to classify re

247 of mortality occurring on extreme heat days; supervised PCA could not be applied to block group data.

248 Supervised PCA explained 62% of variance in the input va

249 The supervised PCA identified a subset of analytes whose abu

250 actors for heat-related health outcomes; b) "supervised"-PCA applied only to variables significantly

251 hoc grouping, so analysis can proceed from a supervised perspective.

252 we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline.

253 lers have emerged as a viable means to relay supervised physical therapy to patients' homes, thereby

254 In the case of supervised prediction methods-when training data compose

255 develop a cross-validation scheme to enable supervised prediction.

256 ve with state-of-the-art supervised and semi-supervised predictive systems.

257 ry x-ray severity (PXS) score), using weakly-supervised pretraining on ~160,000 anterior-posterior im

258 e experimented both with supervised and semi-supervised pretraining, leading to interesting insights

259 d primaquine (1.0 mg/kg per day), 14 days of supervised primaquine (0.5 mg/kg per day), or placebo.

260 ndomly assigned (2:2:1) to receive 7 days of supervised primaquine (1.0 mg/kg per day), 14 days of su

261 Supervised procedure-specific training enabled successfu

262 e results to those predicted from an optimal supervised process, we studied a noise-matched optimal l

263 To test whether both reflect supervised processes, we designed feedback protocols aim

264 en the time of arrival and the second day of supervised quarantine and on days 7 and 14.

265 e results for SARS-CoV-2 at the beginning of supervised quarantine, and less than 2% of recruits with

266 mprove the efficiency of data analysis, both supervised (Random Forest) and unsupervised (cluster lar

267 knowledge of user intent and can outperform supervised recalibration.

268 PET data were analyzed using cerebellum or a supervised reference region for the Alzheimer and multip

269 weighted co-expression network analysis and supervised regulatory network learning.

270 nt margin, but neither unsupervised nor semi-supervised representation learning techniques yielded co

271 All groups completed 8 wk of supervised RT and consumed supplemental whey protein.

272 and without clinician supervision, clinician-supervised self-collected mid-turbinate (nasal) swab spe

273 s classification of cancer driver genes in a supervised setting.

274 Combining the computational potential of supervised SNNs with the parallel compute power of in-me

275 ed Cut for Binning (SolidBin), based on semi-supervised spectral clustering.

276 eveloped a novel contig binning method, Semi-supervised Spectral Normalized Cut for Binning (SolidBin

277 Non-supervised and supervised statistical analyses revealed a singular impr

278 r weighting censored observations and a semi-supervised SVM with local invariances.

279 Yet CNN, being a supervised technique, requires a large labeled dataset t

280 the author contributions: "C.W. and A.J.R.H. supervised the work presented in Figs.

281 1, 2, 5, 6; P.Z. and C.S. supervised the work presented in Figs.

282 We found that the transcription factor Hey supervises the identity of differentiated enterocytes (E

283 Compared with usual care, supervised therapy did not improve asthma control among

284 We conducted a cluster randomized trial of supervised therapy in 20 elementary schools with a dispr

285 We sought to evaluate the effectiveness of supervised therapy in a unique setting and population.

286 tched, and randomized to receive 9 months of supervised therapy with mometasone furoate or usual care

287 a once-daily inhaled corticosteroid regimen (supervised therapy) can improve medication adherence and

288 However, the extrinsic signals supervising these phenotypic transitions remain elusive.

289 igher risk for reoperation when performed by supervised trainees [57 to 72 minutes: hazard ratio (HR)

290 We found that semi-supervised training of a neural network identified signi

291 Aortic stiffness is reduced through supervised training programs, but these are not easily g

292 h and combines it with expression data using supervised training.

293 Then a kernel-based semi-supervised transductive algorithm is applied to the grap

294 odulation and eligibility traces, while true supervised types go even further by adding objectives an

295 School-supervised use of a once-daily inhaled corticosteroid re

296 sed to train higher-layer weights in a usual supervised way so that the performance of the full netwo

297 the second, a binary judgment, learned in a supervised way, is applied to the combined percept.SIGNI

298 ied to the combined percept, is learned in a supervised way.

299 ancestry who participated in an intensively supervised weight loss program with replication of promi

300 Current standards of care for medically supervised withdrawal include treatment with mu-opioid r