ライフサイエンスコーパス: transformer

1 -Transformer), and Bayesian Optimization (BO-Transformer).

2 between the fiber and the imprinted optical transformer.

3 ncoder, hidden space embedding, and temporal Transformer.

4 he sex differentiation pathway downstream of transformer.

5 1.0% unassisted and 92.3% assisted by DeepDR-Transformer.

6 appropriate expression of the female form of transformer.

7 lled by the somatic sex differentiation gene transformer.

8 lus influenzae Rd is a gram-negative natural transformer.

9 ocusing on convolutional neural networks and transformers.

10 same computational principle as that used in transformers.

11 Do humans learn like transformers?

12 gets of the nematode global sexual regulator Transformer 1 (TRA-1), a transcription factor acting at

13 la regulatory proteins Transformer (Tra) and Transformer 2 (Tra2) recruit different members of the SR

14 4; a member of the SR protein family), human transformer 2 beta (hTra2 beta; an exonic splicing enhan

15 sing and is dependent on the function of the transformer 2 gene.

16 ne depends upon the constitutively expressed transformer-2 (tra-2) gene, but does not seem to require

17 The Drosophila sex determination gene transformer-2 (tra-2) is a splicing regulator that affec

18 Among the best studied of these is the transformer-2 (TRA-2) protein which, in combination with

19 ex determination genes transformer (tra) and transformer-2 (tra-2) switch fru splicing from the male-

20 oint mutation in the sex-determination gene, transformer-2 (tra-2), using CRISPR/Cas9 (clustered regu

21 The splicing factor Transformer-2 (Tra2) is expressed almost ubiquitously in

22 In the male germline of Drosophila the transformer-2 protein is required for differential splic

23 characterized one of the CPT targets, Tra2 (Transformer-2).

24 contributes 25% to emissions, soils 31%, and transformers 21%.

25 Transformer 2beta1 (Tra2beta1) is a splicing effector pr

26 A multi-scale sparse temporal autoencoder Transformer (3D-CNN-MSTA-Transformer) is proposed for se

27 to the chart review, Generative Pre-trained Transformer 4 achieved 87.8%-98.8% accuracies for identi

28 duce AOVIFT (adaptive optical vision Fourier transformer)-a machine learning-based aberration sensing

29 ins and small molecules, we introduce Ligand-Transformer, a deep learning method based on the transfo

30 Furthermore, we use the Transformer, a state-of-the-art machine learning model,

31 is at least partially controlled by somatic transformer activity.

32 Here we introduce GET (general expression transformer), an interpretable foundation model designed

33 o other deep-learning architectures based on Transformer and ConvMixer in three different case studie

34 ng each of the two backbone types with seven transformer and convolution block types.

35 The sex determination genes transformer and doublesex ensure that hub formation occu

36 eed industry, are sometimes adulterated with transformer and mineral oil as a means of illegally incr

37 and prediction statistics were obtained for transformer and mineral oils.

38 Both transformer and recurrent-based foundation models genera

39 machine learning system leveraging a vision transformer and supervised contrastive learning for the

40 et, and two attention-based backbones Vision Transformer and Swin Transformer to localize the importa

41 Code for the gait transformer and the trained weights are available at .

42 ets compared to current benchmarks like Swin-Transformer and ViT.

43 ybrid model consisting of double encoders of transformers and a deep neural network to learn the rela

44 Transformers and building sealants represent the largest

45 cussing the key parameters for the design of transformers and generators as well as the development o

46 The chemistry achieved by transformers and generators is (ideally) independent of

47 odules, distributing them in two subclasses: transformers and generators, which can be described resp

48 ave encoded DNA sequences into vectors using Transformers and have shown promising results in tasks i

49 inated biphenyls (PCBs), once used widely in transformers and other applications, and 1,1-dichloro-2,

50 we design and test an ensemble of our Vision Transformers and the ConvNeXt, outperforming the state-o

51 tering, available in two versions: one using transformers and the other convolutional neural networks

52 etermine differences in rCBV values between "transformers" and "nontransformers" at defined time poin

53 dom Search (RS-Transformer), Grid Search (GS-Transformer), and Bayesian Optimization (BO-Transformer)

54 hanism based on RT-DETR (Real-Time Detection Transformer), and thus the method is coded as HSA-RTDETR

55 are extracted using Adaptive Causal Decision Transformers, and the DCCMSA-DCKTKT model parameters are

56 Employing the transformer architecture alongside dilated convolutions,

57 By incorporating a scalable transformer architecture and a diffusion module for buil

58 lly, we demonstrated that TPN2.0 employing a transformer architecture enabled guideline-adhering, phy

59 end pipeline that leverages state of the art transformer architecture in conjunction with QA approach

60 Additionally, we propose a novel WeedSwin Transformer architecture specifically designed to addres

61 quencing model called Cascadia, which uses a transformer architecture to handle the more complex data

62 find the highest performance by combining a transformer architecture with a new modified chaos game

63 a generative adversarial network based on a transformer architecture with self-attention on coarse-g

64 tationS), a deep learning model based on the Transformer architecture, designed to predict chromatin

65 Based on a variant of the transformer architecture, the model learns to call CNVs

66 sformer, a deep learning method based on the transformer architecture.

67 we modify the GPT(6) (generative pretrained transformer) architecture to model the progression and c

68 eneration process and incorporating advanced transformer architectures and general knowledge, our app

69 ectral encoder integrating convolutional and transformer architectures to project raw MS2 spectra int

70 Because Transformers are so successful across a wide variety of

71 identified the sex-specific splicing factor transformer as a functionally significant target of miR-

72 at includes a Dense Associative Memory and a Transformer as two limiting cases.

73 rward-propagated trajectories of the trained transformer, at densities not encountered during trainin

74 rther propose a novel bi-branch masked graph transformer autoencoder (BatmanNet) to learn molecular r

75 By leveraging a shared transformer backbone, MR-IPT effectively learns universa

76 AMPDeep fine-tunes a large transformer based model on a small amount of peptides an

77 0 to 3-4), as were PVH-Rank predictions by a transformer-based AI model ["PVPI"].

78 Furthermore, we apply this transformer-based approach to three additional inference

79 ings underscored the superior performance of transformer-based architectures compared to traditional

80 Five transformer-based architectures-Swin Transformer, ViT, P

81 Additionally, the AFB uses Swin Transformer-based attention and deformable convolution-b

82 of 0.9697, outperforming GRU, LSTM, RNN, and transformer-based baselines.

83 Finally, we employ a supervised transformer-based classifier to perform the HSI classifi

84 First, using BEHRT (a transformer-based deep learning architecture), the embed

85 Here, we develop a new transformer-based deep learning model called UNADON, whi

86 Conclusion A transformer-based deep learning model increased cardiac

87 CelloType leverages transformer-based deep learning techniques for improved

88 ich integrate features learned from advanced transformer-based deep neural network with cell-specific

89 By employing a vision transformer-based encoder, it discerns latent image-gene

90 RNAs and human language, we present LAMAR, a transformer-based foundation LAnguage Model for RNA Regu

91 n performance (average AUROC decay of 3% for transformer-based foundation model vs. 7% for count-LR a

92 our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accura

93 Here, we introduce L-MAP, a transformer-based large language model that is trained o

94 s have existed for decades, the emergence of transformer-based large language models (LLMs) has capti

95 sage passing baselines and recently proposed transformer-based methods on more than 70 node and graph

96 -IPT outperforms both CNN-based and existing transformer-based methods, achieving superior quality ac

97 Additionally, we developed a transformer-based model (MolBART) to predict all end poi

98 developed T-cell receptor cross (TCRoss), a transformer-based model for large-scale learning.

99 Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) prese

100 opment of an interpretable and generalizable transformer-based model that accurately predicts cancer

101 A transformer-based model was developed and validated usin

102 Deep learning transformer-based models using longitudinal electronic h

103 Customized transformer-based models were also trained to perform st

104 Compared with standard and transformer-based models, the model attains better resul

105 of diverse programs using specially trained transformer-based networks and then filtering and cluste

106 A novel transformer-based neural network architecture was traine

107 Here, the transformer-based neural network model was first pre-tra

108 Here we develop a recurrent, transformer-based neural network that learns to decode t

109 Here, we develop a new transformer-based pipeline for end-to-end biomarker pred

110 DetectYSF uses Transformer-based PLMs (e.g., BERT, RoBERTa) as its back

111 Experimental outcomes revealed that the Swin Transformer-based YOLOv10 model delivered the best overa

112 A previously trained, transformer-based, deep learning model automatically cat

113 directional encoder representations from the transformers (BERT) approach, we pre-train Ped-BERT via

114 s bidirectional encoder representations from transformers (BERT) pretrained on 1.14 million scientifi

115 o bidirectional encoder representations from transformers (BERT) with different subsets of reports (f

116 g bidirectional encoder representations from transformers (BERT), is introduced to explore AI trends

117 a bidirectional encoder representations from transformers (BERT), the robustly optimized BERT approac

118 ucleotide (FAD/FADH2) redox center acts as a transformer by accepting two electrons from soluble nico

119 xpression of downstream target genes such as transformer by regulating RNA splicing.

120 xpression of downstream target genes such as transformer , by regulating pre-mRNA splicing and mRNA t

121 entum transformation metasurface (i.e., meta-transformer), by fully synergizing intrinsic properties

122 ith contemporary models such as LSTM and the Transformer, C-FGM clearly achieved a higher accuracy (M

123 les efficient photon transport, the photonic transformer can operate with a near-unity conversion eff

124 g models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular

125 Furthermore, transformer capacity requirement analysis reveals that o

126 Expression of a female transformer cDNA under the control of a dsf enhancer in

127 revealed that rapaprotin acts as a molecular transformer, changing from an inactive cyclic form into

128 Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initi

129 especially OpenAI Chat Generative Pretrained Transformer (ChatGPT), a large language model.

130 AI) platforms, such as Generative Pretrained Transformer (ChatGPT), have achieved a high degree of po

131 d limitations of Chat Generative Pre-trained Transformer (ChatGPT): its ability to (i) define a core

132 nels to reduce computational costs; The MSTA Transformer classification module consists of four parts

133 tronics, including a single rf amplifier and transformer coil.

134 The LM meta-transformer converts red, green and blue illuminations t

135 Built on a transformer decoder and trained with causal masking, Tok

136 sion Transformer (ViT), Data-efficient Image Transformer (DeiT), ImageIntern, and Swin Transformer (v

137 Among these, the Swin Transformer demonstrated the highest performance, achiev

138 CHC profile that differs from doublesex- and transformer-dependent CHC dimorphism.

139 We also demonstrate that transformer-dependent interactions influence whether XX

140 gerprints, including autoencoder embeddings, transformer embeddings, and topological Laplacians.

141 a novel deep learning model consisting of a transformer encoder layer, a convolutional network backb

142 pathology slides by combining a pre-trained transformer encoder with a transformer network for patch

143 th the existing deep learning models, the MR-Transformer exhibited state-of-the-art performance in pr

144 ore, eliminating the expression of Fru(M) by transformer expression in OCT/tyramine neurons changes t

145 Using the GAL4/UAS system to manipulate transformer expression, we feminized or masculinized dif

146 s takeout are feminized by expression of the Transformer-F protein, male courtship behavior is dramat

147 wer cables, magnetic energy-storage devices, transformers, fault current limiters and motors, largely

148 Bidirectional encoder representations from transformers for biomedical text mining (BioBERT) for sp

149 Based on the transformer framework, the model decomposes the traditio

150 For comparison, ClarityNet, a transformer-free variant of the architecture, is present

151 ap, this paper introduces the Focused Reward Transformer (FRT) .

152 1-3p, that has predicted target sites in the transformer gene (Bdtra) required for female sex determi

153 Transgenic expression of transformer gene during development transforms chromosom

154 rtion of cassette exons from the C. capitata transformer gene into a heterologous tetracycline-repres

155 cally spliced intron from the C. hominivorax transformer gene within the Lshid gene ensures that only

156 By targeting the medfly transformer gene, we also demonstrate how CRISPR-Cas9 ge

157 By manipulating either the fruitless or transformer genes in the brains of male or female flies,

158 creating a customized generative pretrained transformer (GPT) within the OpenAI GPT Store and search

159 avinci-003 variant of Generative Pre-trained Transformer (GPT)-3) on a range of analogical tasks, inc

160 odels (LLMs), such as generative pre-trained Transformers (GPTs), across various tasks, they often pe

161 ough conventional methods: Random Search (RS-Transformer), Grid Search (GS-Transformer), and Bayesian

162 ortable X-ray generator, replacing the heavy transformer had been necessary to generate power with a

163 Transformers had a slightly (but not statistically signi

164 phase combinations, such ultra-compact meta-transformer has potential in information storage, nanoph

165 ong the invasion chronosequence of an exotic transformer, Heracleum mantegazzianum, which was replica

166 sNet outperforms VGG16, ResNet50, and Vision Transformer in accuracy, precision, recall, and F1-score

167 Conversely, knockdown of transformer in chromosomal females eliminates the female

168 s an increasing role compared to traditional transformer in some specific power supply systems.

169 basic fibroblast growth factor, a candidate transformer in Xenopus, caused malformation but not hind

170 as ChatGPT (OpenAI), a generative pretrained transformer, in radiology.

171 ther germline gene, ovo, whose regulation is transformer-independent.

172 rging locations can be supported with modest transformer infrastructure (25 to 50 kVA).

173 Availability: The implementation of GRN-transformer is available at

174 Each transformer is used to make predictions of its correspon

175 f the key sex-determination gene tra-2 (tra, transformer) is controlled by a 28-nucleotide repeat ele

176 emporal autoencoder Transformer (3D-CNN-MSTA-Transformer) is proposed for sentiment classification of

177 th gene, fit (female-specific independent of transformer), is not controlled by tra and dsx, suggesti

178 ATGO lies in the utilization of pre-trained transformer language models which can extract discrimina

179 and a single negatively acting receptor, Eye Transformer/Latran (Et/Lat).

180 ing model, DeepECtransformer, which utilizes transformer layers as a neural network architecture to p

181 on a pretrained Local Feature Matching with Transformer (LoFTR) model to verify each component's pre

182 ement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreti

183 We instead propose a 'cochlear transformer' mechanism to interpret cochlear performance

184 Experimental results using the proposed transformer methodology on audio clips of gunshots show

185 long short-term memory (BiLSTM) model and a Transformer model and can analyze POD5 and FAST5 signal

186 GAI uses machine learning and transformer model architectures to generate useful text,

187 mpared to baseline models, the proposed LSTM-Transformer model for Caltech data shows a significant i

188 /threonine kinases to develop an explainable Transformer model for kinase-peptide interaction predict

189 sting sets, the results demonstrate that the Transformer model optimized by MOEBS significantly outpe

190 anning the years 2020-2050 using the Stacked Transformer model show a progressive decrease in the LAI

191 Here, we introduce SEQUOIA, a linearized transformer model that predicts cancer transcriptomic pr

192 ical representations from a generative music transformer model to predict human brain activity.

193 Here, we introduce RiboTIE, a transformer model-based approach designed to enhance the

194 e Bidirectional Encoder Representations from Transformers model is demonstrated on an analog inferenc

195 e-Bidirectional Encoder Representations from Transformers model was used to encode unstructured text

196 e bidirectional encoder representations from transformers model, our model detects nested entities an

197 Additionally, it evaluates Transformer models optimized through conventional method

198 node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types.

199 Application of fine-tuned NLP transformer models to routine ED visit data to automate

200 ntion-based convolutional neural network and Transformer models, DeepPhosPPI accurately predicts phos

201 ges unsupervised pre-training features and a Transformer module to learn both compound and protein ch

202 correlations, derived from an adaptive graph Transformer module.

203 ing a pre-trained transformer encoder with a transformer network for patch aggregation.

204 Gated transformer network models trained with OCT data may be

205 Gated transformer network performance was compared with non-de

206 Gated transformer network performance was worse for eyes with

207 e graph, (2) utilizing a heterogeneous graph transformer network, and (3) computing relationship scor

208 After the development of the famous "Transformer" network architecture and the meteoric rise

209 Gated transformer networks trained and optimized with the othe

210 Combining the advantages of NAS and Transformer networks, local and global information is ca

211 ng technology recently developed for spatial transformer networks, we propose ZephIR, an image regist

212 sanovo, a machine learning model that uses a transformer neural network architecture to translate the

213 Further leveraging transformer neural networks for the design of phase-only

214 Here, we demonstrate that Transformer Neural Networks learn atom-mapping informati

215 nd biosynthetic reactions through end-to-end transformer neural networks.

216 Here, we introduce TECSAS (Transformer of Epigenetics to Chromatin Structural Annot

217 Synechococcus and Aphanizomenon as the main transformers of mercury.

218 BVB's and soya oil samples adulterated with transformer oil and mineral oil were characterised using

219 amples collected from a site contaminated by transformer oil.

220 By pre-training the transformer on extensive synthetic materials data and fi

221 ct layer outperforms existing deep MPNNs and transformers on QM9.

222 phy (UV-NIL) to fabricate functional optical transformers onto the core of an optical fiber in a sing

223 Transformer parental RNAi could be used to produce all m

224 The imprinted optical transformer probe was used in an actual NSOM measurement

225 Here, we present Protein Set Transformer (PST), a protein-based genome language model

226 The OAM meta-transformer reconstructs different topologically charged

227 ChatGPT's (Chat Generative Pre-Trained Transformer) remarkable capacity to generate human-like

228 alysis reveals that only 1.8% of residential transformers require upgrades, while over 80% of commerc

229 than more resource-intensive models such as transformers, resulting in faster inference times.

230 ive to that of the germline (by manipulating transformer) reveal a dominant role for the soma in regu

231 al experimental data to fine-tune regression transformer (RT) models for generative molecular design

232 architecture, is presented to highlight the transformer's impact.

233 In contrast, transformers showed a continuous increase in rCBV up to

234 nsplantations show that a spatially distinct transformer signal emanates from tissues other than the

235 Transformer splicing regulatory proteins determine the s

236 equential, opening up the opportunity to use transformers, state-of-the-art deep learning architectur

237 AS-OCT images of 5746 eyes and (2) a Vision Transformer supervised model coupled to the autoencoder

238 we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised l

239 Even though higher macro-F1 is achieved by transformer systems, based on the findings, a meticulous

240 one female-specific isoforms of T. castaneum transformer (Tctra) were identified.

241 vein using the linear variable differential transformer technique.

242 ime points were also significantly higher in transformers than in nontransformers.

243 t Coulomb drag is analogous to an electrical transformer that functions at zero frequency.

244 around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings.

245 ere, we present Graphormer-RT, a novel graph transformer that performs the first single-model method-

246 methods, such as CNN, LSTM, BiLSTM, GRU, and Transformer, the hyperparameter optimization of these mo

247 By applying the vision transformer to a lung-tumour dataset, we identified and

248 gy to encode protein features by pre-trained transformer to address the issue of scarce labelled data

249 odify a recently proposed architecture named Transformer to capture the interactions between the two

250 Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cr

251 -based backbones Vision Transformer and Swin Transformer to localize the important regions of insect

252 metry measurements is to design an impedance transformer to match the impedance of the load to the ch

253 nostic model, HERPAI, was developed based on Transformer to predict risk of invasive disease-free sur

254 operating in the nonlinear regime acts as a transformer to simultaneously boost the energy and brigh

255 novel sequences we use self-attention-based transformers to capture global patterns in sequences.

256 we designed new architectures for these two Transformers to enable Grad-CAM to be applicable in such

257 ls that represent residues as frames and use transformers to learn relative positions.

258 BetaAlign draws on NLP techniques and trains transformers to map a set of unaligned biological sequen

259 ly, WaveCastNet also generalizes better than transformers to rare and critical seismic scenarios, suc

260 odule) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes managem

261 ntegrates convolutional neural networks with transformer, to predict effects of genetic variations on

262 They found that lipoxygenases are like Transformer toys, being converted from one enzyme type t

263 We show that the sex determination gene transformer (tra) acts in the developing nervous system,

264 phila suppressor-of-white-apricot (SWAP) and Transformer (Tra) alternative splicing factors, we found

265 the role of the sexual differentiation genes transformer (tra) and doublesex (dsx) in regulating the

266 ed and include the hermaphroditic (her), the transformer (tra) and feminization (fem) mutations.

267 nown sex determination pathway, specifically transformer (tra) and its downstream target doublesex (d

268 show that the Drosophila regulatory proteins Transformer (Tra) and Transformer 2 (Tra2) recruit diffe

269 that the Drosophila sex determination genes transformer (tra) and transformer-2 (tra-2) switch fru s

270 entify a key role for sex determination gene transformer (tra) in regulating the male-female differen

271 ) within the regulated 3' splice site of the transformer (tra) pre-mRNA.

272 RA-2) protein which, in combination with the transformer (TRA) protein, directs sex-specific splicing

273 tein to continually instruct the target gene transformer (tra) to make its feminizing product, TRA-F.

274 ethal (Sxl) acting on its switch-gene target transformer (tra) to produce Tra(F) protein.

275 he alternative splicing of Sex-lethal (Sxl), transformer (tra), and Ultrabithorax (Ubx).

276 e sex determination pathway through the gene transformer (tra), the expression of roX1 is independent

277 npf (ms-npf) expression is controlled by the transformer (tra)-dependent sex-determination pathway.

278 morphism by controlling only the switch gene transformer (tra).

279 ination gene hierarchy, Sex-lethal (Sxl) and transformer (tra).

280 lf-supervised representations using a Vision Transformer, trained on 1.7 M histology images across 23

281 tide repeat elements, which are required for transformer/transformer-2-mediated splicing of the femal

282 ile BinaryET is trained from scratch using a transformer-type architecture.

283 t was highest for EfficientNet V2-S and Swin Transformer V2-B at 94.63% and 94.34%, respectively.

284 guage processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate mean

285 ge Transformer (DeiT), ImageIntern, and Swin Transformer (versions v1 and v2).

286 rained foundational model combining a vision transformer (ViT) image encoder with PubMedBERT text enc

287 By leveraging satellite imagery and Vision Transformer (ViT) models, SolarScope achieves an Area un

288 ntNetB6, EfficientNetB7, DenseNet201, Vision Transformer (ViT), Data-efficient Image Transformer (Dei

289 A Visual Transformer (ViT)-based DL model that integrates informa

290 including ResNet-50, ResNet-101, and Vision Transformer (ViT).

291 n medical and veterinary diagnostics, Vision Transformers (ViT) remain relatively unexplored in this

292 Five transformer-based architectures-Swin Transformer, ViT, PVT, MobileViT, and Axial Transformer-

293 hybrid network of EfficientNet-B4 and Swin-B transformer was developed to classify patients according

294 Transformer, ViT, PVT, MobileViT, and Axial Transformer-were systematically evaluated, resulting in

295 First, we propose Blend-Transformer, which introduces Group External Attention (

296 dsx splicing depends on transformer, which is also alternatively spliced such th

297 erforms LSTM whose average MAPE is 4.34% and Transformer whose average MAPE is 5.42%).

298 driven tool leveraging generative pretrained transformers with retrieval-augmented generation technol

299 latory inputs from the soma (as regulated by transformer) with those from the germline (involving ovo

300 compared with a reference multistage fusion transformer without these novel strategies.