ライフサイエンスコーパス: learning

1 visuospatial discriminations (reward-guided learning).

2 al inference dictate that uncertainty shapes learning.

3 ontrol false discoveries for class-imbalance learning.

4 tably retained even more than 200 days after learning.

5 or file handling, visualization, and machine learning.

6 on to image classification and reinforcement learning.

7 s >2000 behavioral features based on machine learning.

8 ely untested method of delivering simulation learning.

9 ategies in favor of low-level implicit motor learning.

10 s variability but variability can facilitate learning.

11 tegrated predictions that may improve future learning.

12 her, when and how to try is central to human learning.

13 n the projection neurons mediates appetitive learning.

14 d joint effects of these factors upon reward learning.

15 ikely contribute to independent processes of learning.

16 plays a key role in motor control and motor learning.

17 with sensory confidence and guide subsequent learning.

18 lasticity and in response to object location learning.

19 ich limits our chances to improve intergroup learning.

20 enhancement, which has value for educational learning.

21 es of well-choreographed walks or flights of learning [1-4].

22 ppocampus subserves place and MB learning by learning a "successor representation" of relational stru

23 A crucial aspect when learning a language is discovering the rules that govern

24 category emerges in the OT within minutes of learning a novel odor-reward association, whereas the pP

25 present in infancy and aid understanding and learning about the social environment.

26 Here, we report the development of a deep learning algorithm for the prediction of local gene expr

27 tion (TMLE) paired with the ensemble machine learning algorithm Super Learner, and compared these wit

28 eveloped a new systems-biology-informed deep learning algorithm that incorporates the system of ordin

29 dvances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-yea

30 A deep learning algorithm was used to segment all baseline OCT

31 rk presents a system equipped with a machine learning algorithm, capable of continuously monitoring r

32 ction from first principles for each machine-learning algorithm, we observe that there is typically a

33 In this study, by using machine learning algorithms and the functional connections of th

34 Machine learning algorithms are then typically employed to make

35 As deep learning algorithms drive the progress in protein struct

36 everal hierarchical extensions of well-known learning algorithms have been proposed.

37 t significantly outperformed shallow machine-learning algorithms in benchmark comparisons, showing ne

38 with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy

39 ne interactions for the construction of deep-learning algorithms that benefit from expert knowledge a

40 training and a testing set and used machine-learning algorithms to classify participants based on ps

41 work is to evaluate the efficacy of machine learning algorithms to cluster the patients into discrim

42 SR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text,

43 Machine learning algorithms trained to predict the regulatory ac

44 rvival hazard ratio was presented by machine-learning algorithms using survival statistics and was co

45 Machine learning algorithms were developed to identify a combina

46 t decisions were modeled using reinforcement learning algorithms.

47 his was more evident for some of the machine learning algorithms.

48 Reinforcement learning allows organisms to predict future outcomes and

49 tics, in vivo electrophysiology, and machine learning analysis, we find that a subset of neurons in t

50 he rich and complex components that comprise learning and decision-making.

51 use of nonlinear kernel methods for machine learning and dimension reduction of high-dimensional dat

52 lementary roles in the implicit, unconscious learning and exploitation of spatial statistical regular

53 nvestigate more abstract, higher-order human learning and knowledge generalization.

54 mpal neurogenesis plays an important role in learning and memory function throughout life.

55 Whereas DG's function in spatial learning and memory has been extensively investigated, i

56 itical role in many processes fundamental to learning and memory in health and are implicated in Alzh

57 tes feeding-relevant biological signals with learning and memory processes to regulate feeding.

58 ritical for neurological function, including learning and memory.

59 conditioning to facilitate associative fear learning and memory.

60 Physical exercise is a powerful modulator of learning and memory.

61 lays a causal role in the acquisition of new learning and not just in the extinction or reversal of p

62 Statistical learning and probabilistic prediction are fundamental pr

63 studied at this merging superhighway of deep learning and protein structure prediction.

64 opment of force accuracy was associated with learning and reduced falls.

65 nd Attachment, Reward Responsiveness, Reward Learning and Reward Valuation constructs.

66 Here, we investigate how reinforcement learning and selective attention interact during learnin

67 and structural changes in key regions of the learning and sensory systems associated with anesthesia-

68 which dissociable neural systems support the learning and transfer of abstract control structures.SIG

69 (B) agonist baclofen impaired motor sequence learning and visuomotor learning in 20 young healthy par

70 as the testing effect or as retrieval-based learning) and consider directions for future research.

71 rojecting neurons disrupted Pavlovian reward learning, and activation of these cells promoted the acq

72 learning have only considered unintentional learning, and therefore the interaction between intentio

73 educational manipulation to improve memory, learning, and transfer of theory-rich content.

74 sing multiscale mechanistic docking, machine learning, and X-ray crystallography, pointing the way fo

75 Here, we developed a machine-learning approach to identify small molecules that broad

76 ovarian tumour cohort and develop a machine learning approach to molecularly classify and characteri

77 Here, we introduce a bespoke machine-learning approach, hierarchical statistical mechanical m

78 Using a machine learning approach, we built gene expression-based models

79 We apply machine learning approaches to a comprehensive vascular plant da

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

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

82 With the development of deep learning approaches, the problem of catheter assessment

83 ew discovery may be advanced through machine learning approaches.

84 Effort-related decision-making and reward learning are both dopamine-dependent, but preclinical re

85 ries, including network analysis and machine learning, are well placed for analysing these data but m

86 ical projection neurons, validating transfer learning as a tool to adapt the model to novel categorie

87 xtinction or reversal of previously acquired learning, as previously thought.

88 ories (A items) during overlapping pair (BC) learning, as well as learning-related representational c

89 as the application of multitask and transfer learning, as well as the use of biologically motivated,

90 peline using Statistical testing and Machine Learning (ASAP-SML), to identify features that distingui

91 S implements model-free response learning by learning associations between actions and egocentric rep

92 EMG showed that learning augments the muscular response evoked by motone

93 her with a "logical" statistical and machine learning-based approach, identified a number of molecula

94 uence data, we developed MapPred, a new deep learning-based contact prediction method.

95 The highest-performing model used a machine learning-based genetic algorithm, with an overall receiv

96 A specific class of deep learning-based methods allows for the prediction of regu

97 acked is a major bottleneck in standard deep learning-based models.

98 sponse, we employed high-performance machine learning-based NER tools for concept recognition and tra

99 We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates

100 Practical workshops were useful for learning basic concepts about ultrasound imaging, allowi

101 hibited selectivity for stimulus rank during learning, but not before or after.

102 ing circuits may be influenced early in song learning by a cortical region (NIf) at the interface bet

103 in which hippocampus subserves place and MB learning by learning a "successor representation" of rel

104 n states; DLS implements model-free response learning by learning associations between actions and eg

105 ing may also underlie its contribution to MB learning by representing relational structure in a cogni

106 In this Review, we discuss how machine learning can aid early diagnosis and interpretation of m

107 , radiomics features extraction, and machine learning can be used in a pipeline to automatically dete

108 stead, novel tools from the field of machine learning can potentially solve some of our challenges on

109 Thus, deep learning can reveal the regulatory syntax predictive of

110 traction and, integrated with CNN-based deep learning, can boost the predictive accuracy.

111 Although social learning capabilities are taxonomically widespread, demo

112 On the basis of machine learning, CEFCIG reveals unique histone codes for transc

113 Finally, machine learning classification algorithms applied to group lass

114 or quantitatively with the Gene Oracle deep learning classifier.

115 at abstraction varies at different levels of learning, cognitive development, or cognitive ability.

116 The IED task measures visual discrimination learning, cognitive flexibility and specifically attenti

117 of the multistakeholder Prior Authorization Learning Collaborative of the Value in Healthcare Initia

118 re of themodel used is set purely by machine learning considerations with little consideration of rep

119 Deep learning-corrected PET (PET(DL)) images were quantitativ

120 tional lecturing with evidence-based, active-learning course designs across the STEM disciplines and

121 odel was used to identify the periods of the learning curve.

122 e used to recruit general, mental health and learning disability nurses, at different levels of senio

123 tilation scans from free-breathing MRI (deep learning [DL] ventilation MRI)-derived specific ventilat

124 rgo consolidation one day after the original learning episode.

125 Learning experience weakens the gap junctions and induce

126 e behavioral and neural data from a task-set learning experiment using a network model.

127 icks and problems when using them in machine learning for excited states of molecules.

128 ter than the models trained with single-task learning for predicting patients' individual symptom tra

129 eless data transfer electronics, and machine learning for predictive data analysis.

130 ical data analysis and interpretable machine learning for quantifying both agent-level features and g

131 A popular deep learning framework (Keras) is applied to the problem of

132 The proposed deep learning framework is effective for such task.

133 We extend the classic transfer learning framework through ensemble and demonstrate its

134 Here we present an End-to-End deep learning framework, circDeep, to classify circular RNA f

135 While learning from complications is useful, M&M does not meet

136 m observing outgroups.SIGNIFICANCE STATEMENT Learning from observing others is an efficient way to ac

137 enerates predictive or descriptive models by learning from training data rather than by being rigidly

138 ning and selective attention interact during learning from trial and error across age groups.

139 sing feature selection algorithm and machine learning, from which the accuracy of detection of positi

140 While deep learning has been applied to cell segmentation problems

141 als (whether wild or captive) rely on social learning has proved remarkably challenging.

142 Recently, deep learning has unlocked unprecedented success in various d

143 ral circuits underlying more direct forms of learning have been well established over the last centur

144 ing the impact of valenced feedback on skill learning have only considered unintentional learning, an

145 h-care system through the establishment of a learning health system built on digital data and innovat

146 One is model-free learning, i.e., simple reinforcement of rewarded actions

147 ypes, including escape behavior, associative learning, immunity and longevity.

148 y systems associated with anesthesia-induced learning impairment.

149 aired motor sequence learning and visuomotor learning in 20 young healthy participants of both sexes.

150 f neonatal anesthesia exposure on behavioral learning in adolescent subjects, and a variety of MRI te

151 humans exhibit two distinct strategies when learning in complex environments.

152 sion weighting of prediction errors benefits learning in health and is impaired in psychosis.

153 st, we asked if well-known features of motor learning in lab-based experiments generalize to a real-w

154 properties of CA1 interneurons 3 h following learning, in a form of oxidative eustress.

155 Machine learning is a branch of computer science and statistics

156 how that using prior knowledge to facilitate learning is accompanied by the evolution of a neural sch

157 This form of social learning is argued to reflect novel forms of social hier

158 However, it remains unclear whether learning is facilitated by non-rapid eye movement (NREM)

159 In real-world settings, learning is often characterised as intentional: learners

160 erated MR image reconstruction using machine learning is presented.

161 during the learning process, and the goal of learning is readily dissociable from the awareness of wh

162 e of success, and when success-biased social learning is unavailable.

163 key predictions that prestige-biased social learning is used when it constitutes an indirect cue of

164 (~20 Hz) at triplet transitions that indexes learning: it emerges with increased pattern repetitions;

165 Among other advances, deep-learning machine learning methods, including convolution

166 hat the contribution of hippocampus to place learning may also underlie its contribution to MB learni

167 uggests that a targeted modulation of reward learning may be a viable approach for novel intervention

168 work was used to elucidate the reinforcement-learning mechanisms that change in adolescence and into

169 the foundation to examine observational fear learning mechanisms that might contribute to fear and an

170 omputational modeling revealed that dominant learning mechanisms underpinning flexible behavior diffe

171 and fundamental changes in behaviors such as learning, memory, and social interactions.

172 MENT The dentate gyrus (DG) is important for learning, memory, pattern separation, and spatial naviga

173 on, time-memory for food sources, sleep, and learning/memory processes.

174 ons: General Ability, Speed/Flexibility, and Learning/Memory.

175 Here, we present a machine-learning method comparing projected typhoon tracks with

176 A deep learning method for localization and quantification of f

177 d.Objectives: To develop and validate a deep learning method to improve the management of IPNs.Method

178 nsional (3D) U-Net and a coarse-to-fine deep learning method.

179 d biological interpretation across 8 machine learning methods and 4 different types of metagenomic da

180 Bayesian machine learning methods can be used for prospective prediction

181 nimal evidence that state-of-the-art machine learning methods can forecast the occurrence of a global

182 multivariate statistical models and machine learning methods for the classification of 6 types based

183 Modern machine learning methods may be used to probe relationships alon

184 rediction enables the development of machine-learning methods to predict the likely biological activi

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

186 ral Imaging (NIR-HSI), together with machine learning methods, is valuable to improve the efficiency

187 th the scripts for training and testing deep learning methods.

188 performance and outcomes of chemical machine learning methods.

189 CCS prediction using machine learning (ML) has recently shown promise in the field, b

190 Application of machine learning (ML) methods for the determination of the gas a

191 Machine learning (ML) provides powerful dimensionality reduction

192 The deep learning model allowed for fully automatic and robust se

193 rams per liter using a random forest machine-learning model based on 11 geospatial environmental para

194 The first deep learning model DeepMS on WGS somatic mutational profiles

195 shift calculation protocols using a machine learning model in conjunction with standard DFT methods.

196 The machine learning model pretrained on Fourier spectrum features a

197 We have trained a machine learning model to analyze the correlation between SARS-C

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

199 We propose a deep learning model, so-called CLPred, which uses a bidirecti

200 ues of choices, estimated by a reinforcement learning model, were regressed against BOLD signal.

201 The resulting machine learning models and segregation database are key to unlo

202 enetic and non-genetic factors, four machine learning models have close prediction results for the ph

203 Various deep learning models have gained success in image analysis in

204 e highlight specific achievements of machine learning models in the field of computational chemistry

205 s, BioConceptVec, via four different machine learning models on ~30 million PubMed abstracts.

206 nt participants and symptoms, our multi-task learning models perform statistically significantly bett

207 We used four machine learning models to produce flood susceptibility maps.

208 Reinforcement learning models treat the basal ganglia (BG) as an actor

209 Deep learning models were compared with mean RNFL thickness f

210 Deep learning models were trained to use SD OCT retinal nerve

211 walkthrough for creating supervised machine learning models with current examples from the literatur

212 election can improve the accuracy of machine-learning models, but appropriate steps must be taken to

213 The training performance of all machine learning models, including six other algorithms, was eva

214 d the selected variables to multiple machine learning models.

215 Future research should focus on learning more about the use of existing tools rather tha

216 umption through its effects on reinforcement learning, motivation, and hedonic experience.

217 s of age by using the Mullen Scales of Early Learning (MSEL).

218 ter is devoted to the application of machine learning, namely, convolutional neural networks to solve

219 Although applications of deep learning networks to real-world problems have become ubi

220 provides evidence that human motor sequence learning occurs outside of M1.

221 tor Octbeta1R drives aversive and appetitive learning: Octbeta1R in the mushroom body alphabeta neuro

222 firmation bias may be adaptive for efficient learning of action-outcome contingencies, above and beyo

223 90-93% through the sparse regression machine learning of patterns.

224 d activity and thus tracked specifically the learning of reward predictive visual features.

225 Here, we conduct machine learning of serum metabolic patterns to detect early-sta

226 Statistical learning of transition patterns between sounds-a strikin

227 e behavior in ASD was driven by less optimal learning on average within each age group.

228 tly, it is highly correlated with behavioral learning outcomes.

229 Both before and after learning, participants were also scanned while viewing i

230 Rather than impaired threat learning, pathological anxiety involves heightened skin

231 I characterize the typical learning performance in terms of the power spectrum of r

232 ressure values were calculated from the deep learning-predicted tonometer and mire diameters using th

233 l: learners are aware of the goal during the learning process, and the goal of learning is readily di

234 uide each other's attention, prediction, and learning processes towards salient information, at the t

235 The machine learning produced highly accurate and robust classificat

236 We found that memory reactivation during learning promoted formation of differentiated representa

237 We found that contextual fear learning recruits a population of young ABNs that are re

238 Learning reduces variability but variability can facilit

239 and parietal areas, without any evidence of learning-related activation increases.

240 ed in this respect, how the thalamus encodes learning-related information is still largely unknown.

241 g overlapping pair (BC) learning, as well as learning-related representational change for indirectly

242 ce of this outgroup deficit in observational learning remained unknown, which limits our chances to i

243 -quality case reports, and a specific set of learning resources.

244 where subjects solve multiple reinforcement learning (RL) problems differing in structural or sensor

245 oscientific implications: deep reinforcement learning (RL).

246 ar sensitization in the stress-enhanced fear learning (SEFL) model of PTSD, as well as associated cha

247 These findings imply that intergroup learning should rely on observing outcomes, rather than

248 ing a multifaceted role in socially adaptive learning.SIGNIFICANCE STATEMENT Adaptively navigating so

249 rsed to negative valence in a Pavlovian fear learning situation, where CeA ChR2 pairing increases def

250 Within-session learning slope may be a useful marker beyond performance

251 ation, and whether facilitation results from learning-specific processing.

252 When exposed to a subthreshold learning stimulus, juv ELE and juv-adol ELE formed lasti

253 Previous deep learning studies on optical coherence tomography (OCT) m

254 gical workflow recognition and report a deep learning system, that not only detects surgical phases,

255 to overcome is the amount of data needed by learning systems of this type.

256 the changes that occur in the brain as this learning takes place are poorly understood.

257 , performed worse on the Rey Auditory Verbal Learning Task (p < 0.05), and had a markedly lower IQ (p

258 Here we developed a reversal learning task for head-fixed mice, monitored the activit

259 s performed a two-armed bandit reinforcement learning task.

260 peed in a two-stage sequential reinforcement-learning task.

261 DR-A is well-suited for unsupervised learning tasks for the scRNA-seq data, where labels for

262 rning tasks, i.e. model-free vs. model-based learning tasks, and their possible differential effects

263 We compared different motor-learning tasks, i.e. model-free vs. model-based learning

264 ecreased exponentially across trials in both learning tasks, optimal target selection (task 1) and op

265 ysiological plasticity and to distinct motor learning tasks, which suggests they represent separate c

266 We tested several recent deep learning techniques including wide residual networks, dr

267 generate classification models using machine learning technology.

268 density functionals using supervised machine learning, termed NeuralXC.

269 ersarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is bette

270 Instead of learning the inverse mapping from a streaking trace to a

271 Hippocampal theta thus supports learning through two interleaved processes: strengthenin

272 mbine sparse mixture mapping with supervised learning to achieve bit error rates as low as 0.11% for

273 nd metabolomic platform that employs machine learning to automate the selective discovery and isolati

274 f label-free imaging flow cytometry and deep learning to characterize RBC lesions.

275 direct long-read RNA sequencing and machine learning to detect secondary structures in cellular RNAs

276 titative signal can be combined with machine learning to enable microscopy in diverse fields from can

277 We applied machine learning to explain how specific interactions controlled

278 rovides encouragement for the use of machine learning to extract meaningful structural information fr

279 ion-seeking, attention, decision-making, and learning to help us survive in an uncertain world.

280 prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic

281 MARS uses deep learning to learn a cell embedding function as well as a

282 ally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruc

283 Here, we used EEG and machine learning to study how the brain processes auditory, visu

284 Finally, we use machine learning to test whether pairwise trophic interactions c

285 t application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Techn

286 ty suggests a unified role for confidence in learning under different types of uncertainty across mam

287 findings from this and non-human studies on learning under perceptual uncertainty suggests a unified

288 Learning valence-based responses to favorable and unfavo

289 We begin by learning vector representations of tasks.

290 We first demonstrate that learning via goal-directed behaviour indeed constrains m

291 of evidence indicates that visual perceptual learning (VPL) is enhanced by reward provided during tra

292 In contrast, reversal learning was impaired by D2R antagonism, but not D1R ant

293 tion, distributed leadership, and collective learning was used to facilitate adoption.

294 re we asked whether, in the context of motor learning where errors decrease across trials, people tak

295 al processing is affected by fear extinction learning (where no US is presented), and how fear and ex

296 om body alphabeta neurons processes aversive learning, whereas Octbeta1R in the projection neurons me

297 warded actions, and the other is model-based learning, which considers the structure of the environme

298 paminergic reward-prediction errors underlie learning, which renders stimuli 'wanted'.

299 y of these thalamic neurons increased during learning, with the learned values stably retained even m

300 iated with methamphetamine (METH) days after learning, without retrieval.