ライフサイエンスコーパス: embedding

1 environments, we applied hierarchical delay embedding.

2 nsional proteome organization after hydrogel embedding.

3 on into one coherent model via cross-network embedding.

4 as well as structural and functional network embedding.

5 abeled samples in test data through instance embedding.

6 y map, and t-distributed stochastic neighbor embedding.

7 protein motif discovery and protein sequence embedding.

8 trained to predict the confounder from that embedding.

9 s of syntactic information to the input word embeddings.

10 uential training samples with splittings and embeddings.

11 revalence curves and low-dimensional disease embeddings.

12 signals to generate biologically informative embeddings.

13 s of comparing the resulting low-dimensional embeddings.

14 achine learning models are used to train the embeddings.

15 encode graph structure into low-dimensional embeddings.

16 fficiently encoded as information-dense word embeddings(11-13) (vector representations of words) with

17 PhEMD is a general method for embedding a 'manifold of manifolds', in which each datap

18 By embedding a classical metapopulation model within a netw

19 Here, we exploit strong coupling by embedding a fullerene-free organic solar cell (OSC) phot

20 akes advantage of a curation effort aimed at embedding a large fraction of the gene products that are

21 -perfect reflection of low-energy photons by embedding a layer of air (an air bridge) within a thin-f

22 ure valley coherence of valley-polaritons by embedding a monolayer of tungsten diselenide in a monoli

23 This is achieved by embedding a patient's lesion into an atlas of functional

24 Embedding a Rh cyclopentadienyl (Cp*) catalyst in the ac

25 ted, both numerically and experimentally, by embedding a total-internal-reflection design in a thin-p

26 Embedding a toxin gene in a gene of interest restricted

27 r and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines

28 tibility, reducing energy re-quirements, and embedding active control in the devices.

29 nded stability in acid can be constructed by embedding active sites within an acid-stable metal-oxide

30 Our methodology is embedding-agnostic and allows for the measurement of wav

31 We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iter

32 e microbiome-level properties by applying an embedding algorithm to quantify taxon co-occurrence patt

33 alyses and t-distributed stochastic neighbor-embedding algorithms.

34 iota and the dietary behavioral patterns, by embedding also the related social aspects, allows improv

35 roplet size is examined and it is found that embedding an elastomer with a polydisperse distribution

36 l polarization detector is functionalized by embedding an imprinted bilayer wire-grid polarizer withi

37 l sections of the mouse penis after paraffin embedding and antibody staining against Protein-Gene-Pro

38 optimal-transport problem for probabilistic embedding and derive an efficient iterative algorithm to

39 s, we demonstrate, via t-Stochastic Neighbor Embedding and k-means cluster analysis of surface marker

40 ence spectroscopy (muXRF), after epoxy resin embedding and preparing thin sections.

41 A new tissue sample embedding and processing method is presented that provid

42 reconstruction method based on methacrylate embedding and serial-sectioning, where 2-D images of imm

43 is estimated and optimized through spectral embedding and stochastic gradient descent.

44 n, and the technical difficulties faced when embedding and submitting a problem to the quantum anneal

45 ia, quick specimen mounting without hydrogel embedding and their applicability for multiple-view imag

46 GNNs, SkipGNN learns biologically meaningful embeddings and performs especially well on noisy, incomp

47 onclude with prospects and future outlook on embedding, and our view on the use of universal test cas

48 orization, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projec

49 in the user query as well as a novel neural embedding approach that enables the retrieval of semanti

50 We explore using word and sentence embedding approaches for nucleotide sequences since they

51 In addition, we conclude that the embeddings are beneficial in protein classification task

52 e the model's likelihood: In this procedure, embeddings are iteratively adapted by solving sparse, di

53 Moreover, because the embeddings are trained in an unsupervised manner, unlabe

54 In addition, embeddings are versatile features that can be used for m

55 metamaterials that can be easily realized by embedding arrays of periodic cuts into an elastic sheet.

56 We refer to these new embeddings as SeqVec (Sequence-to-Vector) and demonstrat

57 scaling and multiview minimum curvilinearity embedding, as well as a multiview spectral clustering me

58 ectral embedding (LSE) or adjacency spectral embedding (ASE).

59 he diffusion state distance, we design a new embedding-based link prediction method called global and

60 concept recognition and trained our concept embeddings, BioConceptVec, via four different machine le

61 oximation and Projection, the model produces embeddings by balancing two goals-pulling nearby example

62 ent protein sequences as continuous vectors (embeddings) by using the language model ELMo taken from

63 We show that the new method of embedding can marginally outperform ProtVec in enzyme pr

64 sing any biological features and the learned embeddings can be treated as complementary representatio

65 licit insertion of chemical knowledge, these embeddings capture complex materials science concepts su

66 This was achieved here by embedding carbon nanodots within crystalline particles o

67 such as DNA methylation underlie biological embedding, causal data are lacking.

68 gned) local-electric fields (LEFs), i.e., by embedding charges or dipoles into molecules.

69 ng that the Eckart embedding is not the best embedding choice.

70 A variety of different embedding choices are considered, together with a hierar

71 The embedding combines a representation of the mutation scor

72 Analysis by t-stochastic neighbor embedding confirmed phenotypically distinct tTreg and pT

73 First, the sequence-embedding convolutional neural network generalizes the e

74 ediction model by integrating a new sequence-embedding convolutional neural network model over a ther

75 erarchical clustering on the low-dimensional embedding corresponds well with previously defined and c

76 However, normalization of the embedding could suffer from over-correction and alter tr

77 pixel size was obtained after developing an embedding/cryosectioning protocol.

78 Embedding cubane [M(4) (OH)(4) ] (M=Ni, Co) clusters wit

79 In study 3, we used word embeddings derived from a large corpus of online text to

80 network is also superior to learning on node embeddings (derived using node2vec), an increasingly pop

81 We found that the embedding dimension is a major factor in controlling the

82 able, and might allow for a reduction in the embedding dimension without performance loss, which is c

83 r ratios of their stimuli dimension to their embedding dimension, which is consistent with greater ef

84 mation across research tools and systems and embedding DMPs in existing workflows.

85 to the common view, we argue that such graph embeddings do not capture salient properties of complex

86 By embedding dynamic ESI within framework that admits data

87 This work involves first encoding ("embedding") each sequence into a dense, low-dimensional,

88 es, split for formalin-fixation and paraffin-embedding (FFPE) and RNAlater preservation (RNAlater).

89 pervised machine learning to create chemical embeddings, finding that the chemicals identified by Foo

90 The proposed node embedding followed by a supervised classification improv

91 entifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to predict s

92 lustering, t-distributed stochastic neighbor embedding for visualization and Laplace score for priori

93 Using sample embeddings for body site classification resulted in negl

94 e been developed to estimate low-dimensional embeddings for filtered and normalized single-cell data.

95 on, but it can also learn higher-dimensional embeddings for other uses.

96 OSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using thre

97 model can produce two- and three-dimensional embeddings for visualization, but it can also learn high

98 Model, to learn vector representations (i.e. embeddings) for all drugs and targets in the created kno

99 s SENSE (SiamEse Neural network for Sequence Embedding), for efficient and accurate alignment-free se

100 atient-specific mutational features using an embedding framework for larger sequence context.

101 rameter q, we are able to produce a range of embeddings from local (q = 1) to global (q -> 0).

102 In addition, the learned embeddings from MutSpace reflect intrinsic patterns of b

103 ptimal architecture parameters using feature embeddings from state-of-the-art image classification ne

104 MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the

105 e a deep neural network to learn an explicit embedding function based on a small training dataset to

106 nd indistinguishability by deterministically embedding GaAs quantum dots in broadband photonic nanost

107 ltiple attempts, existing biomedical concept embeddings generally suffer from suboptimal NER tools, s

108 ow-dimensional image measurements such as an embedding generated by a neural network.

109 Although SeqVec embeddings generated the best predictions from single se

110 called global and local integrated diffusion embedding (GLIDE).

111 odel for drug combination design, by jointly embedding graph-structured domain knowledge and iterativ

112 Distances between sequence embeddings had similar qualities to distances between al

113 It is widely acknowledged that our social embedding has a substantial impact on what information w

114 The field of embedding has grown increasingly broad with many approac

115 an unbiased hierarchical stochastic neighbor embedding (HSNE) analysis of the phenotype of peripheral

116 ements, and present a method for learning an embedding in [Formula: see text] that is isometric to th

117 ort of social experience-mediated biological embedding in adulthood, even in the conventionally memor

118 stem cells (mESCs) form aggregates that upon embedding in an extracellular matrix compound induce the

119 Finally, we demonstrate that embedding in Matrigel induces gastruloids to generate so

120 itrogen atoms or the physical environment by embedding in mesoporous scaffolds, the thermodynamics ca

121 This enables the use of our embeddings in a wide variety of downstream data analysis

122 in, better performance than existing concept embeddings in identifying similar and related concepts.

123 show that a linear transformation of learned embeddings in these models captures parse tree distances

124 cal properties of collagen fibrils and their embedding interfibrillar matrix in native (unfixed), hyd

125 present study investigated this question by embedding irrelevant distractors (flanker arrows) within

126 The peptide embedding is learned by pre-training on natural ligands,

127 lusions are found, including that the Eckart embedding is not the best embedding choice.

128 Our embedding is obtained using LOCA, which is an algorithm

129 Although usage of embeddings is well described in the bioinformatics liter

130 vertices of a graph based on their spectral embedding-is commonly approached via K-means (or, more g

131 We employed the embedding layer and the multi-scale convolutional networ

132 ent to learning a path entropy, and that its embedding layer, instead of representing contextual mean

133 il to transfer to different domains, i.e. an embedding learned from one dataset with a specific confo

134 how that by considering edge-types into node embedding learning in heterogeneous graphs, edge2vec sig

135 Graph embedding learning that aims to automatically learn low-

136 well as hazardous chemicals, dehydration, or embedding, limiting their scalability and utility.

137 ring composed with either Laplacian spectral embedding (LSE) or adjacency spectral embedding (ASE).

138 algorithm, which we named 'feature-augmented embedding machine' (FEM), first learns the structure of

139 DNAm deep learning method that can construct embeddings, make predictions, generate new data, and unc

140 preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that P

141 n for a few domains and coarse causal models embedding markers indicating that these details are avai

142 Recently, learning this embedding matrix directly from the data as part of the c

143 epresented as a continuous vector through an embedding matrix.

144 e between hub genes, but that GLIDE's global embedding measure adds value in the rest of the network.

145 are achieved using a Fourier-transform setup embedding metasurfaces able to manipulate, simultaneousl

146 sed on the multi-graph unsupervised Gaussian embedding method (MG2G).

147 A deep learning based network embedding method is utilized to automatically learn feat

148 to represent bacteriocins, and apply a word embedding method that accounts for amino acid order in p

149 n a given set of genes using a novel network embedding method.

150 ystematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural net

151 otein function predictions, the recent graph embedding methods achieve competitive performance withou

152 al results demonstrate that the recent graph embedding methods achieve promising results and deserve

153 We select 11 representative graph embedding methods and conduct a systematic comparison on

154 eral guidelines for properly selecting graph embedding methods and setting their hyper-parameters for

155 To date, most recent graph embedding methods are evaluated on social and informatio

156 Quantum mechanical embedding methods hold the promise to transform not just

157 fusion MRI connectome dataset: The different embedding methods yield different clustering results, wi

158 zation (which can be seen as a type of graph embedding methods) have shown promising results, and hen

159 ate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-e

160 classification, inference methods and graph embedding methods.

161 We demonstrate this behavior by embedding microgel NPs in agarose gels.

162 r an inertial microfluidic sorting device by embedding microsquares to construct periodic contraction

163 ical expressions in a lexicon using a phrase embedding model, lexical similarity-based natural langua

164 We propose a specific knowledge graph embedding model, TriModel, to learn vector representatio

165 apply machine learning to generate a spatial embedding (multidimensional ordination) of the temporal

166 distances between alignment identities, and embedding multiple sequences can be thought of as genera

167 multiview t-distributed stochastic neighbour embedding, multiview multidimensional scaling and multiv

168 To develop concept embeddings, named-entity recognition (NER) tools are fir

169 phase separation can be highly suppressed by embedding nanocrystals of mixed-halide perovskites in an

170 ion can yield element-specific morphology of embedding nanostructures in ultrathin films.

171 g unsupervised training, we infer morphology embeddings (Neuron2vec) of neuron reconstructions and tr

172 Biological embedding occurs when life experience alters biological

173 Information which obtains a low-dimensional embedding of a statistical manifold (SM).

174 s incompatible drifts, allowing for a stable embedding of a two-dimensional variable (position) in a

175 atalysis: (1) cavity promoted reactions, (2) embedding of active sites in the structure of the cage,

176 Little support was found for the biological embedding of adolescents' perceptions of familial social

177 tochondria play a key role in the biological embedding of adversity.

178 on of trees, hyperbolic geometry enables the embedding of complex hierarchical data in only two dimen

179 as a biomarker to index the neurobiological embedding of early adversity, which in turn may impact c

180 Instead, here we propose extracting cellular embedding of environmental factors from gene expression

181 and their potential roles in the biological embedding of experience.

182 methodology is based on the low temperature embedding of fresh frozen specimens into a hydrogel matr

183 ly performed via 2D static images, since the embedding of interactive 3D structures in webpages is no

184 erving visualization with nonlinear manifold embedding of normalized spectral data.

185 algorithm to sample rooted networks and the embedding of phylogenetic trees within networks.

186 a manifold learning technique, to obtain an embedding of positions and equipment coordinates in a sp

187 rates with the reassortment network and the embedding of segments in that network from full-genome s

188 y of the neural code depends on the relative embedding of signal and noise in the activity of neural

189 Such embedding of supranano liquid metal in perovskite films

190 -bioprint collagen using freeform reversible embedding of suspended hydrogels (FRESH) to engineer com

191 ng economy principle imposed by the physical embedding of the cerebral cortex.

192 by those weak learners, the low-dimensional embedding of the data is estimated and optimized through

193 Yet little is known about spatial embedding of the detailed whole-brain connectivity and i

194 anical stimuli were found to alter the lipid embedding of the integrin beta3 transmembrane domain (TM

195 In addition, we generated a numerical embedding of the interaction network of protein-coding g

196 to oxidation in air arise from the complete embedding of the metal core into the carbon shell togeth

197 ne protein (MP) 3D structure followed by the embedding of the MP into the lipid bilayer for visualiza

198 is to construct a low-dimensional Euclidean embedding of the vertices of the network, where proximit

199 ind that quick learners display more compact embedding of their neural responses, and hence have high

200 Here we use word embeddings of protein sequences to represent bacteriocin

201 es are limited to directly concatenating the embeddings of syntactic information to the input word em

202 rning is on par with classical encodings for embeddings of the same dimension even when limited train

203 ient descent model is employed to learn node embedding on a heterogeneous graph via the trained trans

204 rmation for a target protein, SeqVec created embeddings on average in 0.03 s.

205 articular we introduce a tractable model for embedding one network (A) into another (B), focusing on

206 We engineered synthetic promoters de novo by embedding operator sites with varying affinities and rad

207 tional hypothesis that K has at least 1 real embedding or S contains a finite place we can get a prod

208 followed by a relatively fast heating of the embedding organic matrix, occurring on the 100-ps timesc

209 red to the local average interstellar medium embedding our SS for the past few million years.

210 ing on binding data from hundreds of TFs and embedding over 1 M DNA sequences, BindSpace achieves sta

211 We find that embedding Pd in Cu systems strongly enhances the selecti

212 The focus will be on (1) embedding percolation networks of one-dimensional conduc

213 In t-distributed stochastic neighbor embedding plots from blood and tumor samples, the few ga

214 ess all these important tasks using a single embedding - Poincare maps produce state-of-the-art two-d

215 We show that sequence embeddings preserve relevant information about the seque

216 d, the representation of sequences with word embeddings preserving sequence order information can be

217 These results show that naturally embedding primes within a person's speech and gestures e

218 ling method for vertex ordering in our graph embedding process.

219 We demonstrate the isometric embedding properties of LOCA in various model settings a

220 o create functional protein representations, embedding proteins from different species in the same ve

221 anipulating the social presence in VR (i.e., embedding recording devices and humanoid avatars within

222 Given that formalin fixation and paraffin embedding remains the most common preparation method for

223 at create self-sustaining reaction networks, embedding replication and catalysis, is cited as a poten

224 Embedding resins are also composed by light atoms, thus

225 ation process was optimized, which comprises embedding samples of Biceps femoris, cryo-sectioning, gl

226 approach that makes traditional histological embedding/sectioning/staining feasible for large 3D cell

227 Together, our results show that embedding sequences results in meaningful representation

228 The embedding shell and the occluded Asp act as an integral

229 (aqueous hyperhydration) and PACT (hydrogel embedding) showed higher clearing efficiency in white ma

230 fecting the fluorescence of red-emitting QDs embedding silica nanospheres.

231 nstrate how our technique can be extended to embedding silver nanoparticles in buried microfluidic ch

232 The method also provides a route for embedding small 3D objects inside these devices.

233 Lastly, the k-mer embedding space captured distinct k-mer profiles that ma

234 tances and pairwise distances defined in the embedding space is minimized.

235 Specifically, the sequence embedding space resolved differences among phyla, as wel

236 raining dataset to project sequences into an embedding space so that the mean square error between al

237 robabilistically defining a cell type in the embedding space.

238 on as well as a set of landmarks in the cell embedding space.

239 rpret the effect of the normalization on the embedding space.

240 We used one-dimensional embedding spaces to perform the group-level analysis.

241 e Graphlet Laplacians to generalize spectral embedding, spectral clustering and network diffusion.

242 Embedding spheroids in Matrigel and continuing 3D growth

243 ltrathin sections on silicon wafers for post-embedding staining and volumetric correlative light and

244 Embedding such associative memories into the network rev

245 gle between its normal and the normal of the embedding surface.

246 sis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and others.

247 aging: t-distributed stochastic neighborhood embedding (t-SNE) and uniform manifold approximation and

248 t-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-

249 ds such as t-distributed stochastic neighbor embedding (t-SNE) or generative topographic mapping (GTM

250 ion method t-Distributed Stochastic Neighbor Embedding (t-SNE) to plot the distribution of compounds

251 (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE)) are provided for gene expression expl

252 sis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and the Barnes-Hut (BH) approximation

253 utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly ca

254 veys, and then leverage an existing sentence embedding technique to embed all sequences belonging to

255 , we empirically study a number of different embedding techniques based on dot product, and show that

256 implications, this establishes that popular embedding techniques such as singular value decompositio

257 The model computes a low-dimensional embedding that aims to preserve neighborhood relationshi

258 networks: (i) an autoencoder to generate an embedding that can reconstruct original measurements, an

259 We develop a novel node embedding that enables classification of cancer driver g

260 d by a fairly low-dimensional, interpretable embedding that generalizes to external behaviour.

261 a novel procedure to directly estimate t-SNE embeddings that are not driven by batch effects.

262 d show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as co

263 We jointly train the networks to generate embeddings that can encode as much information as possib

264 ets, we show that our model can (i) generate embeddings that do not encode confounder information, (i

265 s of variations, called confounders, produce embeddings that fail to transfer to different domains, i

266 We mathematically prove that any embedding (that uses dot products to measure similarity)

267 Medicinal chemists are accountable for embedding the appropriate drug target profile into the m

268 in mouthwash products and antiseptic creams, embedding the drug between alginate and poly-beta-amino-

269 great interest as they are often crucial for embedding the molecular switch into a system of interest

270 The results revealed that embedding the polyelectrolyte in a conical pore leads to

271 Like t-distributed Stochastic Neighborhood Embedding, the model can produce two- and three-dimensio

272 elementary matrices if K has at least 1 real embedding, the product of at most 6 elementary matrices

273 l populations and social networks, a spatial embedding-the branching random walk (BRW)-is required.

274 teractive, vector-graphic sequence logos and embedding them in web applications.

275 mparisons of the many available (and future) embedding theories.

276 analysis (DDA), a nonlinear method based on embedding theory from theoretical physics, was applied t

277 at uses deep learning methodology along with embedding to identify succinylation sites in proteins ba

278 tional approach based on stochastic neighbor embedding to nonlinearly align 3D MSI to MRI data, ident

279 efficient bioinformatics approach using word embedding to summarize drug information from more than 2

280 Our ability to connect biological embedding to the epigenetic landscape in its complexity

281 extend k-mer based protein vector (ProtVec) embedding to variablelength protein embedding using PPE

282 ng the publicly available biomedical concept embeddings to date.

283 tly learning convolutional filters and k-mer embeddings to represent RNA sequence contexts.

284 To develop approaches for embedding trials into routine delivery of maintenance he

285 reduction by t-neighbor stochastic neighbor embedding (tSNE) or linear discriminant analysis (LDA).

286 strate more accurate unsupervised behavioral embedding using 3D joint angles rather than commonly use

287 ProtVec) embedding to variablelength protein embedding using PPE sub-sequences.

288 onal space, adds new data points to existing embeddings using a parametric mapping function, and scal

289 ReSimNet learns the embedding vector of a chemical compound in a transcripti

290 em to Linking Set Association (LSA), concept embeddings vector cosine, Linking Term Count (LTC), and

291 ference between the cosine similarity of the embedding vectors of the two compounds and the CMap scor

292 The uPAD was fabricated by designing and embedding wax channels onto the cellulose-based filter p

293 Applying Graphlet Laplacian-based spectral embedding, we visually demonstrate that Graphlet Laplaci

294 By providing a set of pre-trained embeddings, we allow any V4 16S amplicon study to apply

295 The result is intensive embedding, which not only is isometric (preserving local

296 ated by visual inspection of low-dimensional embeddings, which are inherently imprecise.

297 Concept embeddings-which involve the learning of vector represen

298 ly removes multiple batch effects from t-SNE embeddings, while retaining fundamental information on c

299 ted from the membrane lipidic environment by embedding within a 26 strand beta-barrel formed by MtrB.

300 to using OTU abundance data, and clustering embeddings yielded high fidelity species clusters.