ライフサイエンスコーパス: supervise

1 HWs should be compensated, trained, and well supervised.

2 latively rare when CHWs are trained and well supervised.

3 data during training, making our method semi-supervised.

4 For instance, the supervised 10-feature model derived from animal species

5 ountries, associate clinicians were commonly supervised (100% [four countries] for surgery and 90% [2

6 Five d/wk of aerobic exercise (3 d/wk supervised, 2 d/wk unsupervised) for 30 min/session (mod

7 provements in diet, exercise, and weight and supervised a review on the economic efficiency of these

8 y as fat (<10% saturated fat)] combined with supervised aerobic and resistance exercise (60 min; 3 d/

9 Progressive supervised AEx training (60-75% VO2max intensity) was gi

10 Here, we present a novel functional supervised algorithm, called Functional Epigenetic Modul

11 lished in 1985 as a governmental agency that supervises all national transplant activities in the KSA

12 Although originally developed for supervised analyses, i.e., to test the association betwe

13 However, in a supervised analysis of ASXL1 mutated versus wild-type ca

14 Supervised analysis reveals a significant number of pseu

15 screens have based on a single parameter or supervised analysis with predefined Golgi structural cla

16 ntified and explicitly controlled for in any supervised analysis.

17 oftware, Atlas-IT, was developed to inspect, supervise and analyze the atlas.

18 In total, 11 different supervised and semi-supervised classifiers were trained

19 It leverages supervised and semi-supervised feature-based classifiers

20 Through supervised and unsupervised analyses, we demonstrate syn

21 ata, and PARTIE provides mechanisms for both supervised and unsupervised classification.

22 teaching have been considered in research on supervised and unsupervised learning.

23 enges in the application of supervised, semi-supervised and unsupervised machine learning methods, as

24 By combining supervised and unsupervised methods, it reliably detects

25 es an efficient mapping solution with a semi-supervised anomaly detection scheme to filter out false

26 On the other hand, the supervised approach of LDA allowed a clear grouping of t

27 e unsupervised clustering was confirmed by a supervised approach with the use of a top-scoring-pair c

28 ng categorises patients into four subgroups, supervised approaches confirm the need of both endoscopi

29 omain optical coherence tomography (SD-OCT), supervised automated segmentation of individual retinal

30 Integrating supervised behavioral classification with multivariate a

31 s lack experienced faculty who can train and supervise bioinformatics students.

32 on moral philosophy that will accompany and supervise biomedical research.

33 s) using a hierarchical clustering algorithm supervised by a new classification method, FunFHMMer.

34 participation in physical activity classes, supervised by a physiotherapist.

35 data, we found that 68.3% of all concessions supervised by authorities were suspected of major violat

36 Firefighters and station officers supervised by each manager were included in the study vi

37 e was change in sickness absence among those supervised by each of the managers.

38 ere assigned to small, homogeneous caseloads supervised by experts.

39 parallel factor analysis (PARAFAC), PARAFAC supervised by linear discriminant analysis (LDA), and di

40 y provided by community health workers, when supervised by specialists, in low-income and middle-inco

41 The supervising cardiologist (S.C.) made a management plan b

42 involved in the control of gene expression, supervising cell fate decisions and differentiation.

43 ) mid infrared spectroscopy and multivariate supervised classification (partial least squares discrim

44 ge processing application that uses a robust supervised classification algorithm (Support Vector Mach

45 individuals was compared with that of three supervised classification algorithms: Random Forest Clas

46 approaches based on several state-of-the-art supervised classification and feature selection methods.

47 Supervised classification based on support vector machin

48 We proposed a novel supervised classification method for microbial community

49 tients (Cohort #1, n = 70), we constructed a supervised classification model involving the most predi

50 A supervised classification model specified apoptotic and

51 Finally, we developed a supervised classification model, which achieved 84% and

52 Supervised classification PLS-DA models were built with

53 The method of supervised classification PLS-DA was employed to classif

54 alling is challenging, we applied a range of supervised classification techniques, including Support

55 ween CP and AgP and subsequently carried out supervised classification using machine-learning algorit

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

57 e preprocessing, endmember extraction, and a supervised classification), we extensively analyzed the

58 A supervised classifier was implemented to automatically d

59 The supervised classifier was then used, to our knowledge fo

60 rk describes development and applications of supervised classifiers on DFIR imaging data, comparing c

61 In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulti

62 data exploration is not possible after such supervised "cleaning", because effects beyond those stip

63 ovided locally in private offices or faculty-supervised clinics without modification.

64 Supervised cluster analysis was used to generate paramet

65 h efforts in different brain areas have used supervised clustering methods based on standard paramete

66 Semi-supervised clustering of discovery (n=168) and validatio

67 Semi-supervised clustering, based on KRAS(G12D) mutant expres

68 Registered nurses supervised community health workers and offered mobile c

69 sher-ratio (F-ratio) analysis applied to the supervised comparison of sample classes algorithmically

70 ustry self-regulatory bodies are tasked with supervising compliance with marketing rules.

71 f reports have demonstrated the potential of supervised computational methods to deconvolute tissue h

72 ults of non-human species, though only under supervised (conditioning) procedures [3].

73 Gene set testing is typically performed in a supervised context to quantify the association between g

74 atment with AL unless it was combined with a supervised course of PQ.

75 Supervised daily supplementation with 60 mg of elemental

76 Various supervised data reduction algorithms were tested and com

77 We then construct a supervised deep learning architecture comprised of an em

78 The PLS1-DA algorithm was used as supervised discriminant analysis to identify the differe

79 ut with overlapping zones; consequently, two supervised discriminant techniques, i.e. partial least s

80 1 nonsmoking minors aged 14 to 17 years made supervised e-cigarette purchase attempts from 98 Interne

81 V(O2peak) = 3.5 +/- 0.5 l min(-1)) underwent supervised ET (6 weeks, 3-4 sessions per week).

82 ance measure for segmenting all cells in the supervised evaluation.

83 Endovascular revascularization plus supervised exercise (combination therapy) was associated

84 revascularization (selective stenting) plus supervised exercise (n = 106) or supervised exercise onl

85 However, supervised exercise (SE) is known to provide comparable

86 12, and December 22, 2016, to 1 of 4 groups: supervised exercise + GM-CSF (exercise + GM-CSF) (n = 53

87 rcise + GM-CSF (exercise + GM-CSF) (n = 53), supervised exercise + placebo (exercise alone) (n = 53),

88 obese and overweight women before and after supervised exercise 5 d/wk for 16 wk combined with modes

89 Supervised exercise consisted of treadmill exercise 3 ti

90 pants opted for a 16 week moderate intensity supervised exercise intervention, while seven participan

91 Supervised exercise is recommended as a first-line treat

92 erapy of endovascular revascularization plus supervised exercise may be more promising but few data c

93 nting) plus supervised exercise (n = 106) or supervised exercise only (n = 106).

94 an improvement of 1237 m) compared with the supervised exercise only group (from 285 m to 1240 m for

95 related quality-of-life scores compared with supervised exercise only.

96 evascularization plus supervised exercise or supervised exercise only.

97 randomized to either 6 weeks of preoperative supervised exercise or standard treatment using sealed e

98 o either endovascular revascularization plus supervised exercise or supervised exercise only.

99 Supervised exercise program and advice about self-manage

100 A supervised exercise program and advice did not confer ad

101 ssess the impact of a preoperative medically supervised exercise program on outcomes after elective a

102 e findings do not support the routine use of supervised exercise programs after removal of immobiliza

103 f endovascular revascularization followed by supervised exercise resulted in significantly greater im

104 were randomized to a group that received 1 h supervised exercise training (n=10) or one that received

105 A period of preoperative supervised exercise training reduces postoperative cardi

106 ganisms using Markov Random Fields in a semi-supervised fashion.

107 n of predictive performance is required when supervised feature selection has been used.

108 Supervised feature selection of a SNP subset in the G-BL

109 It leverages supervised and semi-supervised feature-based classifiers, including our new

110 ral blood of a cohort of subjects undergoing supervised food challenge for FPIES.

111 We propose a supervised framework, pMim, built upon concepts of signi

112 moderate physical activity (PA; 80% aerobic; supervised/free gym membership).

113 ality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic f

114 This work focuses on supervised generative binary classification models, spec

115 nts exercised below prescribed target during supervised HIIT and 80% above target in MCT.

116 tive trajectories of the same profile during supervised immunosuppression minimization diagnosed reje

117 Primaquine is efficacious when supervised in clinical trials, but its effectiveness in

118 ed by the graded exposure, either at home or supervised in hospital depending on severity, using a mi

119 vided (needle and syringe exchange programs, supervised injection, and available medically assisted t

120 Unsupervised or improperly supervised insertions of the central catheters by traine

121 Hence, we devised a novel, supervised, integrative learning framework to perform wh

122 ents were not statistically different during supervised intervention or at follow-up at 52 weeks (HII

123 esent multicenter trial compared 12 weeks of supervised interventions of HIIT, MCT, or a recommendati

124 4 on 10-point Likert scale) when compared to supervised introduction (median 3.8, P < 0.0001) or home

125 l of performance is close to that of lightly supervised IS methods and has proven sufficient to aid a

126 Our study innovates a semi-supervised iterative pattern learning approach that is u

127 es from the raw atom distribution, driven by supervised labels.

128 Once found, this supervised learner is employed in an inverse optimizatio

129 undamental idea of our approach is to find a supervised learner that links the scattering pattern of

130 To increase the predictive power of the supervised learner, the scattering patterns are filtered

131 We evaluate the performance of 11 well known supervised learners and show that a multiple-classifier

132 In addition, a supervised learning algorithm based on the "random fores

133 arget genes to putative enhancers via a semi-supervised learning algorithm that predicts gene express

134 dual animals in a social context, and used a supervised learning algorithm to classify several well-d

135 ror prone, which is problematic for training supervised learning algorithms (the so-called garbage in

136 ) and built models using these features with supervised learning algorithms.

137 hylation and TF binding, we proposed a novel supervised learning approach to predict TF-DNA interacti

138 rcome these limitations, we herein propose a supervised learning approach, termed snvForest, to prior

139 Using a supervised learning approach, we discovered a DNAm signa

140 ing of genes and gene ontology terms using a supervised learning approach.

141 ustering and then characterize them by using supervised learning approaches.

142 In particular, supervised learning based models for TF-DNA interactions

143 rogeneity in the data, we introduce a novel, supervised learning footprinter called Detecting Footpri

144 an be successfully implemented within a semi-supervised learning framework that exploits the intrinsi

145 tworks are trained by a novel combination of supervised learning from human expert games, and reinfor

146 These neural networks were trained by supervised learning from human expert moves, and by rein

147 The supervised learning further improves contact prediction

148 ition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and

149 ab) is an open source package for performing supervised learning in structured prediction tasks.

150 rm a hyperdimensional feature space in which supervised learning is performed for cell classification

151 (Bayesian Analysis of Gene EssentiaLity), a supervised learning method for analyzing gene knockout s

152 coupling data, but not quite as well as the supervised learning methods of MetaPSICOV and CoinDCA-NN

153 Our GGL method can also integrate supervised learning methods to further improve accuracy.

154 icance of candidate clusters is tested using supervised learning methods, and a multiple testing corr

155 tion via the use of multiple kernel learning supervised learning methods.

156 These findings support a supervised learning model in which activation of the US

157 y improved the predictive performance of the supervised learning models.

158 ich provides an error signal as in classical supervised learning paradigms.

159 This is because standard supervised learning requires 'ground-truth' functional a

160 Here, we present a supervised learning scheme for the feedforward and recur

161 response Gaussian processes are used for the supervised learning task and a simple setup is introduce

162 field that requires the development of novel supervised learning techniques for addressing challenges

163 osis and prognosis model, incorporating semi-supervised learning techniques to improve their accuraci

164 We combine the model with supervised learning techniques to risk-stratify patients

165 Variable selection and supervised learning techniques were used to select relev

166 uires computational features trained through supervised learning to emphasize the behaviorally import

167 ntegrates joint multi-family EC analysis and supervised learning to improve accuracy on proteins with

168 Ultimately, supervised learning using a subset of biomarker and EMR

169 riate and multivariate statistical analysis, supervised learning, correlation networks, clustering an

170 ation algorithms, including graph-based semi-supervised learning, graph sharpening integration, compo

171 When trained with supervised learning, we demonstrate that a DNC can succe

172 ives a winner-take-all circuit that supports supervised learning.

173 was 99.1% similar to those assigned based on supervised learning.

174 blematic for existing methods of regularized supervised learning.

175 roblem-solving therapy delivered by trained, supervised LHWs plus an optional 6-session peer support

176 Here, we show that supervised machine learning (SML) can be used to build r

177 racking and profiling of behavior by using a supervised machine learning algorithm, are able to deliv

178 The system uses supervised machine learning and rich features largely ba

179 A supervised machine learning approach for automatic predi

180 , builds on these features and adds enhanced supervised machine learning capabilities (Classifier), a

181 provides functionality for unsupervised and supervised machine learning for data exploration and pro

182 e its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably

183 Supervised machine learning is a powerful and widely use

184 Supervised machine learning is commonly applied in genom

185 Supervised machine learning is widely applied to transcr

186 w that deep convolutional neural networks, a supervised machine learning method, can solve this chall

187 Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed,

188 n this study, we apply state-of-the-art semi-supervised machine learning methods to the Alzheimer's d

189 The supervised machine learning model, recursive-support vec

190 Supervised machine learning models were constructed base

191 The supervised machine learning neural network developed is

192 ary hypertension could be predicted by using supervised machine learning of three-dimensional pattern

193 duced pluripotent stem cell (iPSC) lines for supervised machine learning pattern recognition to accur

194 he authors used support vector regression, a supervised machine learning technique, to address this q

195 We applied unsupervised and supervised machine learning techniques to the copy-numbe

196 of the mitotic cell cycle phases by applying supervised machine learning to morphological features ex

197 We apply supervised machine learning to predict peptide S/N based

198 e needs interpretable classification models (supervised machine learning) and features with rich biol

199 By using advanced data mining, supervised machine learning, and network analysis, this

200 and their locations in the text required for supervised machine learning.

201 ified as either adverse or non-adverse using supervised machine learning.

202 mental data to train a variant classifier by supervised machine learning.

203 mily evolutionary coupling (EC) analysis and supervised machine learning.

204 First, we used a supervised machine-learning algorithm to predict a NUE g

205 s of TF-miRNA interactions, we extended semi-supervised machine-learning approaches to integrate a la

206 The supervised machine-learning model was first trained with

207 combining different ranking approaches in a supervised manner via using LTR.

208 able to combine various ranking methods in a supervised manner via using the Learning to Rank (LTR) a

209 el where a decision boundary is learned in a supervised manner.

210 trometry (PS-MS) combined with a chemometric supervised method (partial least square discriminant ana

211 Our supervised method for thin lung tissue classified NETs w

212 Reference-based algorithms are supervised methods that determine the underlying composi

213 n of DNA methylation data has led to several supervised methods, but these methods do not easily perm

214 t out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional rep

215 Three supervised ML models were developed utilising endoscopic

216 accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automati

217 The created supervised model yielded correct cross-validation result

218 nts were randomized to either a 6-month semi-supervised moderate exercise protocol (EX, n = 66) or a

219 olving 21 post-menopausal women, 16 weeks of supervised moderate intensity exercise training was foun

220 A supervised, moderate- to high-intensity, combined resist

221 To investigate this, we develop supervised, multi-task, convolutional neural network mod

222 A supervised multiclass pathway activity inference method

223 Unsupervised and supervised multivariate analyses were sequentially perfo

224 tical software packages for unsupervised and supervised multivariate analysis.

225 nding data was subjected to unsupervised and supervised multivariate statistical analyses.

226 e target data to train, validate, and test a supervised neural network.

227 r ratio (F-ratio) analysis and facilitates a supervised nontargeted analysis based upon the experimen

228 We develop semi-supervised normalization pipelines and perform experimen

229 ormalism underlying our approach facilitates supervising of multi-read allocation with a variety of d

230 owever, all existing approaches, even weakly supervised ones, require several hundreds of hand-annota

231 feature extraction and classification of the supervised or unsupervised type.

232 ndividual ions as an additional parameter in supervised orthogonal projections to latent structures d

233 upervised (Principal Component Analysis) and supervised (Partial Least Squares Discriminant Analysis)

234 Semi-supervised pattern recognition has been proposed to auto

235 ata obtained with NIRS were analysed using a supervised pattern recognition method, Discriminat Parti

236 Once the analytical data were collected, supervised pattern recognition techniques such as linear

237 In a second step, several supervised pattern recognition techniques were used to c

238 by principal component analysis (PCA), three supervised pattern recognition techniques, Partial Least

239 Applying supervised pattern recognition, we found highly signific

240 pocaloric normoproteic diet and 60 min/wk of supervised physical activity).

241 Care delivered in the presence of senior supervising physicians was more comprehensive than care

242 duce the influence of subjective judgment of supervising physicians.

243 When input synapses are subject to supervised plasticity, however, dense wiring becomes adv

244 ffing PPE, and a system of trained observers supervising PPE donning and doffing was implemented.

245 on increasing the quantity and diversity of supervised practice and improving the teenagers' prelice

246 nary evidence suggests that the TDP improves supervised practice and the driving performance of preli

247 dies have failed to show an effect of parent-supervised practice driving on the driving performance o

248 examined mean differences in the quantity of supervised practice, differences in the overall proporti

249 ensing programs have provisions that require supervised practice.

250 ncrease the quantity and diversity of parent-supervised practice.

251 In supervised prediction applications, our multitask approa

252 ), alone or in combination with 14 d of semi-supervised primaquine (PQ) (3.5 mg/kg total).

253 nts in the discovery set were subjected to a supervised principal component (SPC) analysis to predict

254 MA for a previously validated CCRCC-specific supervised principal component (SPC) risk score prognost

255 hypomethylation with activation of oncogenes.Supervised principal component analysis (SPCA) is a popu

256 Supervised principal components analysis was used to ide

257 Supervised principal components and lasso penalized Cox

258 nt, hospital, and procedure covariates using supervised principal components regression.

259 -living weight-loss patterns to in-residence supervised programs, we applied the following 4 differen

260 Experiments demonstrate that the new semi-supervised protocol can result in improved accuracy and

261 We developed a new semi-supervised protocol that can use unlabeled cancer protei

262 anual-based coping intervention delivered by supervised psychology graduates, or treatment as usual (

263 A supervised random forest algorithm with a 10-fold cross-

264 intensity maxima and classifying them with a supervised random forest classifier.

265 and a moderate- to high-intensity, combined supervised resistance and aerobic exercise program (OnTr

266 During the supervised scenarios, lower team performance checklist s

267 We excluded studies that did not include supervised schizonticidal treatment without primaquine.

268 er, with a rich feature approach for NER and supervised semantic indexing for normalization.

269 d recurrent challenges in the application of supervised, semi-supervised and unsupervised machine lea

270 anpower and/or hypervigilance on the part of supervising senior residents or attending physicians, th

271 AT consisted of three supervised sessions per week at 20 to 45 minutes per ses

272 ontrol intervention (PR comprising 8 wk, two supervised sessions per week) or the trial intervention

273 This scenario is different from the standard supervised setting, where each classifier's accuracy can

274 overtreated, in turn suggesting the need for supervised step-down.

275 ites were monitored to develop LUR models by supervised stepwise selection of GIS predictors.

276 150 minutes per week of aerobic exercise and supervised strength training twice per week) or usual ca

277 Our approach is based on supervised structural feature extraction using deep lear

278 h unsupervised (hierarchical clustering) and supervised (support vector machine) analyses of these di

279 We introduce an approach that uses supervised temporal topic models to transform large corp

280 Psychiatrists supervised the team and conducted interactive video psyc

281 he data sets for chemometric assessment with supervised tools.

282 USTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq re

283 m, where an oracle or expert is employed for supervised training in lieu of direct observations, whic

284 To determine whether GM-CSF combined with supervised treadmill exercise improves 6-minute walk dis

285 Among patients with PAD, supervised treadmill exercise significantly improved 6-m

286 either when used alone or when combined with supervised treadmill exercise.

287 chizontocidal efficacy and safety of a 3-day supervised treatment of the fixed-dose combination artes

288 Supervised visits in major teaching EDs compared with at

289 In a sample of US EDs, supervised visits were associated with a greater likelih

290 pared with the 25,808 attending-only visits, supervised visits were significantly associated with mor

291 Association of supervised visits with hospital admission, advanced imag

292 eaching ED, minor teaching ED (half or fewer supervised visits), and major teaching ED (more than hal

293 sits), and major teaching ED (more than half supervised visits).

294 teaching EDs, a weighted estimate of 9% were supervised visits, compared with 82% of visits to the 34

295 Supervised visits, defined as visits involving both resi

296 ediatric EDs in the sample, 3374 visits were supervised visits.

297 y the proportion of sampled visits that were supervised visits: nonteaching ED, minor teaching ED (ha

298 10 men who had undertaken 20 weeks of fully supervised, whole-body resistance exercise training (RET

299 Supervised withdrawal (ie, detoxification) from opioids

300 low abuse potential, is effective for use in supervised withdrawal settings.