ライフサイエンスコーパス: pipeline

1 lling, and 98% concordant using an automated pipeline.

2 an automated machine-learning-based analysis pipeline.

3 ndent fully automatic open-source end-to-end pipeline.

4 not provide a complete quality control (QC) pipeline.

5 perform many steps in a long bioinformatics pipeline.

6 wly developed field-deployable bioinformatic pipeline.

7 ed using an automated and publicly available pipeline.

8 gRNA, is a unique advantage of our in-silico pipeline.

9 biases with the corresponding MPS sequencing pipeline.

10 opore sequencing with a novel bioinformatics pipeline.

11 n our specifically-designed machine learning pipeline.

12 new approaches entering the drug development pipeline.

13 validate (n = 22), and test (n = 28) the CNN pipeline.

14 d onto the market as well as in the clinical pipeline.

15 rst-in-class peptide therapeutics are in the pipeline.

16 ng GP to select the best subset in the final pipeline.

17 A-gene sequencing (MiSeq-Illumina) and QIIME pipeline.

18 gle-cell analysis algorithms into a flexible pipeline.

19 perience and ownership of the entire project pipeline.

20 of the stages across two invocations of the pipeline.

21 ial for revolutionizing the drug development pipeline.

22 incorporated demonstrating each step of the pipeline.

23 on the state-of-the-art viral identification pipelines.

24 can be easily integrated into bioinformatics pipelines.

25 rpret and communicate results from different pipelines.

26 eat samples in East African forensic science pipelines.

27 e accelerate high-throughput DNA engineering pipelines.

28 e plugs that can clog, and sometimes rupture pipelines.

29 ESI deconvolution in automated data analysis pipelines.

30 drugs) and (ix) build custom compound mining pipelines.

31 with identical sequencing and bioinformatics pipelines.

32 can be easily integrated into presented WGBS pipelines.

33 ological anxiety to improve drug development pipelines.

34 viously neglected by conventional annotation pipelines.

35 zed with specifically designed bioinformatic pipelines.

36 g Louisiana-Mississippi and Texas-New Mexico pipelines.

37 shorter for the CPU-based and GPU-based CNN pipelines (216.6 seconds +/- 40.5 and 204.9 seconds +/-

38 n and the implementation of image processing pipelines able to deal with diverse motion types, and 3D

39 Our pipeline accurately processed images of diverse origin,

40 ports automated deployment of user-validated pipelines across the entire data capital.

41 addresses different stages of the discovery pipeline, all of them share three common features: a tri

42 tion strategies coupled to new data analysis pipelines allowed the mapping of specific DNA damage for

43 egrated into GenBank's submission processing pipeline allowing for viral submissions passing all test

44 The pipeline also includes motion correction using the MRI n

45 e operator dependent steps in a reproducible pipeline and allowed for automated estimation of atrial

46 iant calling were computed using an in-house pipeline and compared to the reference MiniSeq data.

47 , we provide FREYA, a robust data processing pipeline and statistical analyses framework.

48 by the drying up of the antibiotic discovery pipeline and the resulting unchecked spread of resistant

49 approaches are not practical for underground pipelines and their deployment can be complicated for th

50 for easy integration into existing analysis pipelines and to generate high quality figures and repor

51 on of unconventional oils using transmission pipelines and train rails.

52 Due to this potential for expanding target pipelines and treating a larger number of human diseases

53 in biorefineries is captured, transported by pipeline, and injected into saline aquifers.

54 ive Cancer Network, Mayo Clinic K2R Research Pipeline, and Mayo Clinic Center for Individualized Medi

55 ed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across x

56 We develop a pipeline, Antibody Sequence Analysis Pipeline using Stat

57 Improved lead compounds from the adjuvant pipeline are under development and are explored for thei

58 s is written in R, Bash and uses a Snakemake pipeline as a workflow management system.

59 released the developed paradigm and analysis pipeline as open-source software to facilitate replicati

60 hat are now commercially available or in the pipeline as rapid diagnostic tools.

61 , for automating and improving data analysis pipelines associated with large-scale fitness screens, i

62 Ts: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quantitation via whole-s

63 Here we present ImageAEOT, a computational pipeline based on autoencoders and optimal transport for

64 istically rigorous phylogenetic footprinting pipeline based on precomputed orthologs to predict the d

65 imulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec,

66 Manatee also goes beyond common pipelines by identifying and quantifying expression from

67 The pipeline can also detect single nucleotide variants and

68 s an example of how the antibiotic discovery pipeline can be populated with more promising candidates

69 This pipeline can be used for the identification of genes wit

70 Automated machine learning pipelines can perform equivalent to or better than hand-

71 A bioinformatics pipeline, Cenote-Taker, was developed to automatically a

72 ts (SparkINFERNO), a scalable bioinformatics pipeline characterizing non-coding genome-wide associati

73 The innovative potential of the preclinical pipeline compared with the clinical pipeline is encourag

74 We found that RNA-seq pipeline components jointly and significantly impacted t

75 ribed, including potential treatments in the pipeline, cost-effective participant recruitment and sel

76 We then established a scalable pipeline coupled with the SONICC and TEM techniques to s

77 eployment can be complicated for the case of pipelines covered by insulation.

78 A computational pathology pipeline (CRImage) was used to classify cells in the H&E

79 used to evaluate the proposed reconstruction pipeline derived from an open-source three-dimensional C

80 We anticipate that the benefits and pipeline described in our study can be applied to optimi

81 introduce MetaLAFFA, a functional annotation pipeline designed to take unfiltered shotgun metagenomic

82 All pipelines detected amino acid variants (AAVs) at full ra

83 Here we report a robust WGS and analysis pipeline (DigiPico/MutLX) that virtually eliminates all

84 represented as expression trees and optimal pipelines discovered using a stochastic search method ca

85 rmacokinetics were desirable for discovering pipeline drug candidates.

86 ains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the

87 The proposed pipeline employs WGCNA, a software widely used to perfor

88 The pipeline encompasses the following steps: (1) conformati

89 The pipeline extracts feature fingerprints from sequences.

90 As a proof-of-concept, we show that an OCS pipeline focused on genes coding for transcription facto

91 We developed an image analysis pipeline for 3D imaging of GEMs in the context of large,

92 C, a scalable, portable and cloud compatible pipeline for analyzing Nanopore sequencing data.

93 d, we have applied a quantitative proteomics pipeline for analyzing the secretome of primary human um

94 we developed a fast, simple, and open source pipeline for assembly and verification of plasmid sequen

95 Therefore, we develop an analytical pipeline for automatic assessment of Ca(2+) transient ab

96 ly developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265

97 s this issue, we established an experimental pipeline for comprehensive profiling of small exRNAs iso

98 problems in the initial steps of the common pipeline for data analysis in metabolomics.

99 ent GeoBoost2, a natural language-processing pipeline for extracting the location of infected hosts f

100 hypercluster, a python package and SnakeMake pipeline for flexible and parallelized clustering evalua

101 affinity results and propose a new analysis pipeline for future HTP measurements of domain-peptide i

102 eated as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users.

103 Here we present CheckV, an automated pipeline for identifying closed viral genomes, estimatin

104 ent by the community, we develop a benchmark pipeline for inference of cell-type proportions and impl

105 By developing a pipeline for integrating multi-omics data, we identify 7

106 to infer networks and its accessibility as a pipeline for non-bioinformaticians to analyze transcript

107 quencing, but until now, a rigorous analytic pipeline for nuclear RNA-seq has been lacking.

108 Here we present Padhoc, a pipeline for pathway ad hoc reconstruction.

109 In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment o

110 We also developed an image processing pipeline for segmentation and classification of morpholo

111 le for genetic code expansion and provides a pipeline for the discovery of additional orthogonal pair

112 Here, we evaluate a two-step pipeline for the imputation of common variants in ancien

113 lico trials innovations represent a powerful pipeline for the prediction of the effects of specific t

114 reover, our results also validate a scalable pipeline for the rapid characterization of cancer-associ

115 ted GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.

116 Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues in

117 encing and software platforms and annotation pipelines for a new genome project can be daunting becau

118 ve created a need for improved data analysis pipelines for deconvolution of electrospray (ESI) mass s

119 We evaluate sixteen pipelines for reconstructing the evolutionary histories

120 Many existing pipelines for scRNA-seq data apply pre-processing steps

121 Standard bioinformatics pipelines for the analysis of bacterial transcriptomic d

122 ese three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reli

123 te cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with mult

124 uently overlooked, because genome annotation pipelines generally ignore small open reading frames, wh

125 ions found using AMLD, combined with utility pipeline GIS information, to allow us to estimate activi

126 esults show that signatures derived from our pipeline gives a substantially more reliable and informa

127 Although some current compounds in the pipeline have exhibited increased susceptibility rates i

128 Many NGS HIVDR data analysis pipelines have been independently developed, each with v

129 ing reference genome using a modified tuxedo pipeline (hisat 2 + cufflinks package) and infer GRNs fr

130 To assess the performance of the developed pipeline, IDIFs extracted by both CT-based attenuation c

131 tients (n = 98) were selected for a WES-only pipeline if the history was atypical for genes within th

132 ate pipeline validation and catalyze further pipeline improvement by the community, we develop a benc

133 further drop-off in the physician scientist pipeline in a field that has a perpetual need for resear

134 s reveal the usefulness of our computational pipeline in supporting the selection of candidates for c

135 rocess using GPCRs as a focus to improve the pipeline in two critical ways: Firstly, we use a blended

136 The proposed integrated reconstruction pipeline including a CNN architecture is capable of rapi

137 n processed using each of the five different pipelines including HyDRA, MiCall, PASeq, Hivmmer and DE

138 Here, we present a genome recovery pipeline incorporating iterative subtractive binning, an

139 cognition with human-level accuracy, and the pipeline informs on a series of quantitative parameters

140 The combined burst acquisition rate of the pipeline is 3 Gpixel per sec and the net rate is 600 Mpi

141 The pipeline is available as a macro for the open-source ima

142 clinical pipeline compared with the clinical pipeline is encouraging but fragile.

143 The performance of the proposed pipeline is evaluated using similarity between the signa

144 From the user's perspective, the entire pipeline is invoked by adding two simple lines to their

145 challenging, and the current pharmaceutical pipeline is nearly empty.

146 The pipeline is scalable, modular and flexible.

147 ive information about the global preclinical pipeline is unavailable.

148 methods, the major advantage of the proposed pipeline lies in the uniform structure prediction and re

149 crobiome and metabolome integrative analysis pipeline (M2IA) has been developed to meet the urgent ne

150 equency, we find a clear interaction between pipeline material and age with the leakiness of all mate

151 cations of REA across the precision medicine pipeline may contribute to inconsistencies in data colle

152 Thus, the new pipeline may significantly boost the performance of L100

153 The classical drug development pipeline necessitates studies using animal models of hum

154 providers, and are essential in developing a pipeline of academic A/I specialists.

155 sistant (MDR) Gram-negative pathogens as the pipeline of antibiotics is essentially empty.

156 The new Brian2GeNN software uses a pipeline of code generation to translate Brian scripts i

157 ntrols, and processed using the longitudinal pipeline of Freesurfer v.5.3.0.

158 Shortcomings in the current pipeline of infectious disease physician scientists are

159 The pipeline of new cardiovascular drugs is relatively limit

160 We critically examine the pipeline of SLE drugs, including past failures and their

161 equivalent to or better than hand-optimized pipeline on an external validation test non-invasively p

162 n the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the d

163 Here, we apply the tree-based pipeline optimization tool (TPOT) to predict angiographi

164 into existing high-performance data analysis pipelines or as a Python package to implement new tests

165 rivers and is universally accessible without pipelines or dams.

166 es that are not attributable to distribution pipelines or other NG infrastructure suggest many small

167 sets differ in coverage, and when sequencing pipelines other than GATK are used.

168 Our Snakemake pipeline performs sample demultiplexing, overlap paired-

169 , we deploy NanoSPC to our scalable pathogen pipeline platform, enabling elastic computing for high t

170 We developed Pan Resistome Analysis Pipeline (PRAP) for the rapid identification of antibiot

171 Most notably, the pipeline predicts TKR with AUC 0.943 +/- 0.057 (p < 0.05

172 onstrated high-throughput material discovery pipeline presents a paradigm for facile and accelerated

173 tegrating these computational modules into a pipeline (ProNorM), we mitigate variation among instrume

174 Our pipeline provides a fully automatic estimation of fibros

175 The pipeline provides a roadmap for rapid antibody-discovery

176 The complete veSEQ-HIV pipeline provides viral load estimates and quantitative

177 Our pipeline reliably identified pathogenic bacteria (that i

178 The pipeline relies on a volumetric MRI scan that serves as

179 Current popular variant calling pipelines rely on the mapping coordinates of each input

180 lgorithm, genist, or a regression tree-based pipeline, rtp-star.

181 reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation.

182 We provide some guidelines for TPOT-based ML pipeline selection and optimization-based on various cli

183 The software pipeline SHOGUN profiles known taxonomic and gene abunda

184 Pipelines show consistent types of biases, with those in

185 Here, our pipeline shows accelerated mapping of PRRs.

186 icrobiome discovery (CSMD), a bioinformatics pipeline specifically developed to generate accurate spe

187 Here, we describe standardized computational pipelines specifically tailored to the analysis of mouse

188 base in each task and file associated with a pipeline stage, JUDI simplifies plug-and-play of the pip

189 stage, JUDI simplifies plug-and-play of the pipeline stages.

190 Although some steps in this pipeline still require manual intervention, complete aut

191 rt into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Pro

192 This pipeline takes advantage of "anatometabolic" information

193 Generally each stage in a pipeline takes considerable computing resources and seve

194 pecies of nonmodel treefrogs and developed a pipeline that allowed us to assemble their complete amin

195 transposon-site sequencing with an analysis pipeline that allows statistical comparisons between dif

196 We present a novel data integration pipeline that analyses GWAS results in the light of expe

197 Here we introduce PEPPAN, a novel pipeline that can reliably construct pangenomes from tho

198 Using the pipeline that can semiautomatically process data from di

199 This report demonstrates a pipeline that effectively filters small-molecule RyR1 mo

200 pplied the method to our production analysis pipeline that establishes genotype-phenotype association

201 We have developed a computational pipeline that extracts protein-adenine complexes from th

202 Here we introduce RepeatModeler2, a pipeline that greatly facilitates this process.

203 However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis, such as o

204 In this study, we develop a pipeline that integrates dimensionality reduction and st

205 e established a TE expression quantification pipeline that is compatible with scRNA-seq data generate

206 We also developed a comparative analysis pipeline that minimizes biases attributable to sequence

207 -cost, high-throughput prokaryotic scRNA-seq pipeline that overcomes these technical obstacles.

208 We have developed a pipeline that performs maximum likelihood analyses, a k-

209 chnology, Engineering and Mathematics (STEM) pipeline that perpetuate racial disparities in academia.

210 s can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequenci

211 them according to the steps of a seven-step pipeline that they employ.

212 We present a deep learning pipeline that uses all uncurated chart, lab, and output

213 vious single pipeline with multiple analysis pipelines that are tailored according to the input data,

214 In addition, we describe research pipelines that broaden evidence-based approaches and the

215 As a result, TPOT-generated ML pipelines that outperformed grid search optimized models

216 correct mapping coordinates, variant calling pipelines that rely on mapping coordinates can exhibit r

217 ite of tools, interfaces and data processing pipelines that transforms NCBI Gene Expression Omnibus (

218 omatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PC

219 e present study, we developed a high-content pipeline, the large-area spatial transcriptomic (LaST) m

220 Application of our pipeline to 2,815 human-gut associated bacteria showed h

221 s study, we applied a machine learning-based pipeline to a dataset consisting of genetic features, cl

222 We used the same computational pipeline to analyze publicly available expression data f

223 ction, and machine learning can be used in a pipeline to automatically detect high-risk phenotypes of

224 study, we develop a bioinformatics analysis pipeline to build a predictive gene expression model (GE

225 mportance of a fruitful antibody development pipeline to combat the potential escape plans of SARS-Co

226 Here, using an evolutionary genomics pipeline to compare 208 complete genomes, we analyze the

227 developed using Pydpiper image registration pipeline to create an average brain image of 41 four-mon

228 Here, we describe an integrated pipeline to define the in vivo function of non-conserved

229 e evaluated using a multifaceted phenotyping pipeline to define their unique disease profiles followi

230 quenced and analyzed through a bioinformatic pipeline to detect indels, frameshifts, or hypermutation

231 Here, we implemented a computational pipeline to determine the correlation of expression betw

232 interactive visualization and open analysis pipeline to facilitate use of our dataset for vaccine de

233 Using fMRI, we propose an analytical pipeline to identify abnormal thalamocortical network dy

234 tibody sequences, we created a bioinformatic pipeline to isolate key determinants of polyreactivity.

235 Its open architecture enables any tool or pipeline to output or convert results into mirGFF3.

236 In this study, we propose a new pipeline to perform gene co-expression network analysis.

237 We developed an R-scripted computational pipeline to perform reanalysis and functional analysis o

238 ere, we present an open-source computational pipeline to produce 3D consistent histology reconstructi

239 0 billion reads) and a uniform bioinformatic pipeline to produce comprehensive sRNA locus annotations

240 We present a systematic pipeline to produce first-approximation estimates for mo

241 We used 1 postprocessing pipeline to retrospectively analyze T1-weighted magnetic

242 We developed a computational pipeline to study plasmodesmata distributions and detect

243 We describe a fully automated processing pipeline to support the noninvasive absolute quantificat

244 RGS-box." Here, we developed an experimental pipeline to systematically assess the mutational landsca

245 We developed an image- and CRISPR/Cas9-based pipeline to systematically characterize candidate genes

246 rest and individual cells, and we apply this pipeline to the regenerating humerus.

247 ycle generative adversarial network into our pipeline to transform SS into DS WSIs.

248 We developed image-processing pipelines to analyse patterns in root trajectories and a

249 hat can be easily incorporated into existing pipelines to better understand the aging process.

250 management, while SPI-Birds creates tailored pipelines to convert each unique data format into a stan

251 PR can be easily integrated into the current pipelines to facilitate scRNAseq data analysis.

252 ated semiautomated and flexible ImageJ2/Fiji pipelines to quantify kinetochore misalignment at metaph

253 lear RNA-seq data analysis and develop a new pipeline, Tuxedo-ch, which outperforms other approaches.

254 fication of corrosion, cracks and defects in pipelines used for transporting oil and gas can reduce t

255 The R-based pipeline uses Fluorescence Minus One (FMO) controls or d

256 To accomplish this, we developed a discovery pipeline using nematode, zebrafish, and mammalian cell m

257 Here we developed an imaging pipeline using plus-end tip tracking and intravital micr

258 We hypothesized that an automated pipeline using radiomics and machine learning could iden

259 velop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning

260 s can objectively characterize their imaging pipeline using suitable reference standards, which are s

261 GWAS and post-GWAS analysis in an automated pipeline using the command-line interface.

262 We have built a parallel imaging pipeline using transmission electron microscopes that sc

263 PEPPAN with four state-of-the-art pangenome pipelines using both empirical and simulated data sets.

264 The new approach performed better than pipelines using commonly used metrics such as F1-score i

265 e compared the performance of five NGS HIVDR pipelines using proficiency panel samples from NIAID Vir

266 Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improv

267 The best-performing hand-optimized pipeline was a Bayesian classifier with Fischer Score fe

268 Our stringent analysis pipeline was able to detect 18 unique variants (8 de nov

269 a fully automated deep learning BC analysis pipeline was applied to a cross-sectional population coh

270 The automatic pipeline was composed of five steps with a DenseNet arch

271 ddress this challenge, a biomarker discovery pipeline was developed to integrate gene expression prof

272 The practical usefulness of the pipeline was examined in three large-scale benchmark exp

273 The pipeline was first tested in a large-scale retrospective

274 A review of the clinical antibacterial drug pipeline was recently published, but comprehensive infor

275 This pipeline was then fed into an interactive data resource.

276 A segmentation pipeline was used to accurately identify true and false

277 iously validated natural language processing pipeline was used to identify laterality of cataract sur

278 Using an unbiased proteomics pipeline, we determined the composition of centromeric c

279 Using a novel robust analysis pipeline, we found broad regions with elevated probabili

280 kdown, combined with an image-based analysis pipeline, we have determined the phenotypic signature of

281 To accelerate the cardiac drug discovery pipeline, we set out to develop a platform that would be

282 combining both manual curation and automatic pipelines, we present a genome-wide annotation of the ps

283 of the gas-solid mixture in the flow passage pipeline were studied by numerical simulation.

284 The proposed pipelines were applied to 448 individuals from the ROSMA

285 the effects of preprocessing, three distinct pipelines were used: (1) regression of white matter (WM)

286 t is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq da

287 We developed a computational pipeline, where the inputs of a cryo-EM map, the corresp

288 MetaLAFFA is implemented as a Snakemake pipeline, which enables convenient integration with dist

289 We demonstrate that our pipeline, which relies on de novo assembly, can also be

290 a novel hierarchical virtual-screening (VS) pipeline, which starts with low-resolution protein struc

291 freedom to interface with the data analysis pipeline while maintaining a user-friendly environment a

292 e introduce a new automated image processing pipeline whose main novelties include an innovative modu

293 precision weights in a general linear model pipeline with continuous autoregressive structure to acc

294 end-to-end metagenomic functional annotation pipeline with distributed computing compatibility and fl

295 We integrated an optimized bioinformatics pipeline with high-resolution mycobiota sequencing and c

296 ss correlation coefficients of the automatic pipeline with manually generated results were excellent

297 (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailo

298 detailed experimental protocol and analysis pipeline with which to perform DISCOVER-seq.

299 a comparative analysis of TPOT-generated ML pipelines with selected ML classifiers, optimized with a

300 phics to their data by following recommended pipelines written in reproducible code in the user manua