ライフサイエンスコーパス: genomic data

1 etect disease susceptibility loci for clinic genomic data.

2 nternational policy and practice for sharing genomic data.

3 he footprints of sweepstakes reproduction in genomic data.

4 rue multilocus analysis for high-dimensional genomic data.

5 the available proteomic, transcriptomic and genomic data.

6 lacement that integrated epidemiological and genomic data.

7 sed approach that integrates metabolomic and genomic data.

8 pcoming studies or sanity checks on existing genomic data.

9 context of outbreak reconstruction based on genomic data.

10 nd facilitate discovery from high throughput genomic data.

11 rity-related problems revolving around human genomic data.

12 to accommodate constantly growing microbial genomic data.

13 l modeling for high-dimensional clinical and genomic data.

14 ial correlation networks for high-throughput genomic data.

15 detect informative patterns from large-scale genomic data.

16 s is hampered by the limited availability of genomic data.

17 analysis of gene expression and multi-modal genomic data.

18 ding predictive models based on multi-source genomic data.

19 rt curation and quality control of the input genomic data.

20 ncerns raised by readily accessible pathogen genomic data.

21 h are hampered by the scarcity of functional genomic data.

22 er allele specific differences in functional genomic data.

23 ful efforts to generate avian haemosporidian genomic data.

24 logy has led to an explosive accumulation of genomic data.

25 e sequencing has led to high availability of genomic data.

26 ecifically tailored to the analysis of mouse genomic data.

27 ed in the model compared to models with only genomic data.

28 iques of genetic research and the sharing of genomic data.

29 (DP) while sharing summary statistics about genomic data.

30 ronment that promotes unrestricted access to genomic data.

31 ons to enhance the research ecosystem around genomic data.

32 s on unmixing tumor subpopulations from bulk genomic data.

33 ng a promising way to take full advantage of genomic data.

34 application for interactive visualization of genomic data.

35 application to connect life scientists with genomics data.

36 both mechanisms has been confirmed in cancer genomics data.

37 al quality measure when analyzing functional genomics data.

38 integration of these models with functional genomics data.

39 to store, update, explore, and analyze phage genomics data.

40 estimating their parameters from large-scale genomic data, a framework for an appropriate null model

41 show that the adversary can infer sensitive genomic data about a user from the differentially privat

42 pport the rapid implementation of widespread genomic data access.

43 Analyzing genomic data across populations is central to understand

44 es provide a consistent set of interfaces to genomic data across the tree of life, including referenc

45 Such time series genomic data allow for more accurate estimation of popul

46 Viral genomic data allowed us to reconstruct mumps transmissio

47 Using the viral genomic data alone there was insufficient resolution to

48 capable of identifying virus hosts based on genomic data alone would aid evaluation of their medical

49 ed information than clinical-pathological or genomic data alone.

50 the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust t

51 taxa included and the choice and quality of genomic data analysed.

52 The feed turns genomic data analysis into a collaborative work between

53 s known to have effects on various stages of genomic data analysis pipelines.

54 Our TCGA cancer genomic data analysis revealed that amplification or mRN

55 ped Epiviz as an integrative and interactive genomic data analysis tool that incorporates visualizati

56 ed fundamental operation for highly scalable genomic data analysis.

57 tightly integrated to facilitate interactive genomic data analysis.

58 re the proposed system advances the field of genomic data analysis: (i) takes the first step of proac

59 n recent years due to increasingly available genomic data and advances in statistical modelling.

60 ic lethality screens, integration of clinico-genomic data and computational modelling.

61 Each image is created by stacking aligned genomic data and encoding distinct alleles into separate

62 rganisms likely to express e-pili from (meta)genomic data and for the construction of microbial strai

63 has proved challenging due to the paucity of genomic data and genetic tools available for corals.

64 Using genomic data and quantitative PCR, we show that SCN4A is

65 Indigenous communities around the sharing of genomic data and suggests principles and actions that ge

66 n a statistically rigorous manner, alongside genomic data and temporal data.

67 (2) unequal generation of health-associated genomic data and their prediction accuracies.

68 The application of genomic data and well-developed data mining technologies

69 drug-response data, multidimensional cancer genomics data and genome-wide association study data for

70 Finally, by integrating genomics data and pathway analysis, we find that the dif

71 tworks to extract features from clinical and genomic data, and convolutional neural networks to extra

72 numbers of CHD candidates, based on patient genomic data, and for building upon existing genetic net

73 analysis for 3 sets of prostate cancer (PCa) genomic data, and found that BMI1 and androgen receptor

74 ntial increases in the availability of virus genomic data, and ongoing advances in phylogenomic metho

75 ntly when tested on simulated and real human genomic data, and thus can be practically used for priva

76 erformed a meta-analysis of pooled published genomics data, andwe present a comprehensive literature

77 ank of iPSC-derived neurons and accompanying genomic data are available to accelerate ASD research.

78 These genomic data are consistent with 16th century written re

79 dition, concerns about the privacy of banked genomic data are exacerbated by recent projects that dem

80 Most cancer genomic data are generated from bulk samples composed of

81 Web-based visualization tools for genomic data are increasingly taking advantage of modern

82 ver, outside a few model organism databases, genomic data are limited in their scientific impact beca

83 ., provide public access to large amounts of genomic data as flat files.

84 In addition, using our genomic data as well as publicly available resources, we

85 current best practices for various types of genomic data, as well as opportunities to promote ethica

86 se, which means that the capacity to perform genomic data assembly and analysis has not expanded as w

87 In recent years, the accelerated pace of genomic data availability has been accompanied by the ap

88 However, given the lack of genomic data available for ADHD, these theories have not

89 g technology have led to a rapid rise in the genomic data available for plants, driving new insights

90 al increase in the amount of disease-related genomic data available in public databases.

91 g sense of the growing amount of genetic and genomic data available.

92 eases, may necessitate exchange of sensitive genomic data between multiple institutions.

93 e increasing amount and complexity of bovine genomics data, BGD continues to advance its practices in

94 Parental lineages and genomic data both revealed demographic patterns in Franc

95 for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of th

96 However, genomic data can be poorly informative of transmission e

97 An integration of field-based phenotypic and genomic data can potentially increase the genetic gain i

98 review the genomic landscape of cancer, how genomic data can provide much more than a sum of its par

99 es and clinical measurements, in addition to genomic data, can identify relevant biomarkers of cardio

100 Utilizing genomic data collected from 11 315 subjects of 33 distin

101 Here we took advantage of several large genomic data collections (Genotype-Tissue Expression, Th

102 The Genomic Data Commons (GDC) Data Portal is a platform tha

103 oma patients using open-access data from the Genomic Data Commons.

104 Accordingly, genomic data comparing isolates carried by mothers and t

105 ich can bring efficacy in secure and private genomic data computation.

106 genetic elements." There is an abundance of genomic data consistent with the hypothesis that CRISPR

107 hod to bovine, codfish, and human population genomic data containing panels of multiple populations r

108 While genomic data continues to rocket, clinical application a

109 enes to form feed-forward loops (FFLs) using genomic data covering mouse embryonic days E10.5 to E14.

110 egion (Discoglossus and Pelodytes), by using genomic data (ddRAD).

111 The explosion in population genomic data demands ever more complex modes of analysis

112 Our experiments on synthetic and empirical genomic data demonstrate that our parallelized methods o

113 Conventional methods to analyze genomic data do not make use of the interplay between mu

114 Genomics data due to its sparse, high-dimensional and no

115 e frequency changes observable from temporal genomic data (e.g., evolve-and-resequence studies).

116 Genomic data encode past evolutionary events and have th

117 The volume of functional genomic data focusing on post-transcriptional regulation

118 Here we present genomic data for 48 ancient individuals from Chukotka, E

119 ing increase in availability of high-density genomic data for a diverse array of bacteria, developmen

120 from the UK Biobank to collect patient-level genomic data for cases and controls of European ancestry

121 We implemented methods to process genomic data for deep learning in a user-friendly progra

122 three major methods together with functional genomic data for new genes.

123 in the interpretation of array and NGS-based genomic data for precision medicine.

124 convolutional neural networks on population genomic data for the detection and quantification of nat

125 tebook based tool for evaluating and sharing genomic data for variant analysis and quality control of

126 cogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, a

127 in-ligand docking to augment sparse chemical genomics data for the machine learning model of genome-s

128 Here we use genomic data from 256 terminals to estimate evolutionary

129 We analyzed genomic data from 341 patients with CCA and identified N

130 Here, we show that multi-sample genomic data from a single time point of normal and canc

131 gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outs

132 Acquisition of genomic data from diverse Wolbachia clades will aid in t

133 Our new genomic data from Iberian Early and Middle Neolithic ind

134 Here we leveraged genomic data from one of the last remaining putative rep

135 Using phenotypic and genomic data from over 100,000 UK Biobank participants,

136 ing data from The Cancer Imaging Archive and genomic data from The Cancer Genome Atlas from 110 patie

137 , no cyanases were detected in thaumarchaeal genomic data from the Gulf of Mexico.

138 oad taxon sampling including newly sequenced genomic data from the monoplacophoran Laevipilina antarc

139 Genomic data from these pathogens have extended earlier

140 dividuals with DS with or without AVSD, with genomic data from whole exome sequencing, whole genome s

141 s of available ChIP-chip and ChIP-sequencing genomic data from yeast, we investigated whether the RNA

142 Through rigorous, integrative analysis of genomics data from a range of solid tumors, we show many

143 e interpretation of the extensive functional genomics data from HepG2 requires an understanding of th

144 cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENC

145 ation sequencing generating large amounts of genomic data, gene expression signatures are becoming cr

146 Visualization of multiple genomic data generally requires the use of public or com

147 The amount of genomic data generated globally is seeing explosive grow

148 To maximize the value of the genomic data generated, these data will need to be share

149 With the emerging of high-dimensional genomic data, genetic analysis such as genome-wide assoc

150 Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites h

151 As the volume of available genomic data grows, developing efficient and scalable me

152 In recent years, the ability to generate genomic data has increased dramatically along with the d

153 Population genomic data has revealed patterns of genetic variation

154 anscriptional regulatory networks (TRN) from genomics data has always represented a computational cha

155 fforts to increase the reusability of public genomics data has been to focus on the inclusion of qual

156 Functional genomics data has the potential to increase GWAS power b

157 Recent analyses of population genomic data have fitted models where both these process

158 to detect signals of natural selection from genomic data have traditionally emphasized the use of si

159 Recent genomic data highlighted that DCC is the third most freq

160 Joint modeling of clinical and genomic data highlights the interactions between tumor a

161 Regardless of these risks, our genomic data hold much importance in analyzing the well-

162 Genomic data hold salient information about the characte

163 The sharing of genomic data holds great promise in advancing precision

164 the risks versus the benefits of generating genomic data in deciding whether to undergo exome sequen

165 ublications) heterogeneous and multi-species genomic data in human, mouse and rat brains.

166 owever, extracting features from single cell genomic data in order to infer their evolutionary trajec

167 candidate regulatory genes using integrated genomic data in plants.

168 f omics technologies have generated abundant genomic data in public repositories and effective analyt

169 Marked variation exists in the use of genomic data in tumour diagnosis, and optimal integratio

170 and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS

171 iction accuracy compared to models with only genomic data included when heading date was used as a co

172 ovides a wide array of zebrafish genetic and genomic data, including genes, alleles, transgenic lines

173 anning from the political (the regulation of genomic data, increased philanthropic funding and malici

174 Genomic data indicate active promoters in the genome-wid

175 learning approach suitable for multi-layered genomic data integration, given its robustness to noisy

176 rning methods that can take these aspects of genomic data into account.

177 rove the efficiency and speed of translating genomic data into clinically effective therapies and how

178 We integrate genomic data into networks as node-level attributes (anc

179 However, translating genomic data into personalized treatment regimens has be

180 des a novel approach to integrate regulatory genomic data into predictive machine learning models of

181 ing unprecedented opportunities to integrate genomic data into the clinical diagnosis and management

182 tients and physicians to rapidly incorporate genomic data into treatment decisions without increasing

183 ediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative.

184 Prioritization of variants in personal genomic data is a major challenge.

185 with modern machine-learning and large-scale genomic data is a powerful paradigm for the study of bot

186 Genomic data is frequently stored as segments or interva

187 Nowadays, the amount of genomic data is massive and substantial efforts and new

188 Genomic data is now abundant in healthcare, and the newl

189 explosion and ease of access to large-scale genomics data is intriguing.

190 The volume of phage genomics data is rapidly increasing, driven in part by t

191 be extended to integrate additional types of genomics data, leading to further improvements in its pe

192 The density of simulated genomic data mimics roughly 1.2 million SNP markers in t

193 h makes it possible to run efficiently large genomic data mining analyses.

194 not strongly differentiated, vast amounts of genomic data now make it possible to study subtle patter

195 show that the adversary can use the inferred genomic data obtained from the attribute inference attac

196 n more broadly, we analyzed the clinical and genomic data of 1,662 advanced cancer patients treated w

197 -SNE, UMAP, and PCA-UMAP) to biobank-derived genomic data of a Japanese population (n = 169,719).

198 and western Himalayas based on mitochondrial genomic data of Cytochrome b (380 bps) and D-loop (1000

199 gy analysis of transcriptomic, proteomic and genomic data of large data sets of healthy controls and

200 ans found South of the Sahara desert, Recent genomic data of Taforalt individuals in Eastern Morocco

201 polymorphisms (SNP) markers identified from genomic data of the two Caribbean Acropora species as we

202 prospectively adapt to the physiological and genomic data of woody plants.

203 istant TGCT, and combine this with published genomic data on an additional 624 TGCTs.

204 omputational work required-such as accessing genomic data on large scales, integrating genomes from d

205 s and publicly releases clinically annotated genomic data on tumor and germline specimens on an ongoi

206 itat often cannot be reliably predicted from genomic data or from physiology studies of isolates.

207 Meltos to either estimate VAFs directly from genomic data or to use copy number corrected estimates.

208 By analyzing functional genomic data, our results indicate that 180 genes (66.7%

209 he efficiency and scalability to process big genomic data, particularly when annotating whole-genome

210 naive methods using both simulated and real genomic data, particularly when input data had realistic

211 s dramatically increased the availability of genomic data, phased genome assembly and structural vari

212 However, the sheer volume and complexity of genomic data presents a challenge to interpreting enhanc

213 Genomic data provide an opportunity to isolate drivers o

214 Single-cell genomic data provide an opportunity to quantify mosaicis

215 Network-based analyses of high-throughput genomics data provide a holistic, systems-level understa

216 The explosive growth of genomic data provides an opportunity to make increased u

217 The recent accumulation of cancer genomic data provides an opportunity to understand how a

218 that demonstrate the ability to re-identify genomic data, raising the specter of discriminatory or o

219 Genomic data repositories like The Cancer Genome Atlas,

220 ures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of h

221 coding was carefully tailored for processing genomic data, respecting the qualitative differences in

222 Our genomic data reveal a surprisingly dynamic history of co

223 Population genomic data revealed no signature of higher transpositi

224 Our population genomic data revealed strong genetic structure for B. ox

225 cence, 3D ATAC-PALM connected microscopy and genomic data, revealing spatially segregated accessible

226 to reidentify an individual from a specific genomic data set.

227 he original metagenomic data with additional genomic data sets and found that SSaDV was 1 of 10 denso

228 riorities, which is particularly relevant as genomic data sets become increasingly accessible.

229 Analysis of genomic data sets can provide high-resolution estimates

230 Previously published comparative functional genomic data sets from primates using frozen tissue samp

231 Here, we reanalyzed public genomic data sets from The Cancer Genome Atlas (TCGA) an

232 ingle nucleotide substitution rates in large genomic data sets from untreated cancers.

233 roduce new statistical methods for analyzing genomic data sets that measure many effects in many cond

234 Integration of various genomic data sets with the traditionally used phenotypic

235 of transcriptomic, miRNomic, proteomic, and genomic data sets.

236 evolutionary information in the era of large genomic data sets.

237 res large scale electronic health record and genomic data sets.

238 -to-use computational system for analysis of genomics data sets, designed to accelerate biomedical di

239 Genomic data sharing accelerates research.

240 nt general privacy-protection techniques for genomic data sharing and their potential applications in

241 Widely accepted 'genomic data sharing beacon' protocol provides a standar

242 Since genomic data sharing is often infeasible due to privacy

243 Based on the Genomic Data Sharing Policy issued in August 2007, the N

244 very large public survey on attitudes toward genomic data sharing.

245 future research opportunities for advancing genomic data sharing.

246 of these multiple mumps virus lineages, the genomic data show that one lineage has dominated in the

247 ernel models that combined physiological and genomic data showed 35 to 169% increase in prediction ac

248 it favoured early during domestication, with genomic data showing how bitterness loss was achieved co

249 bination of long-term field observations and genomic data shows that the reduction of gene flow for Z

250 It allows any combination of clinical and genomic data streams to be searched using an evolutionar

251 Employing population and cancer genomics data, structural analyses, molecular dynamics s

252 atform for genomics, but its host of complex genomic data structures places a cognitive burden on the

253 The estimation of in-situ replication from genomic data suggest that (U)Petromonas tenebris lineage

254 el organism research has generated extensive genomic data that can provide insight into the neurobiol

255 rence and cannot capture latent structure of genomic data that corresponds to lineages.

256 us consortia have collected a vast volume of genomic data that enable us to investigate the role that

257 Integrative analysis of genomic data that includes statistical methods in combin

258 quencing produces an extraordinary amount of genomic data that is organized into a number of high-dim

259 s compared to those not treated, and provide genomics data that support the idea of direct clonal tra

260 response to the ever-expanding generation of genomic data, the International Organization for Standar

261 ition to the considerable correlation in the genomic data, there is a need for machine learning metho

262 With the rapidly growing genomic data these innovative capabilities offer a platf

263 oencoder method which transforms single cell genomic data to a reduced dimension feature space that i

264 y 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy

265 with analyses of Q. suber transcriptomic and genomic data to evaluate potential redundancies in cork

266 yzes high-throughput chromatin accessibility genomic data to identify cell-type-specific accessible r

267 d combined these data with transcriptome and genomic data to identify mechanisms that control gene ex

268 netic analyses in individuals with available genomic data to identify variants associated with inheri

269 We used genomic data to infer the geographic origin of transloca

270 ith geographic distance, enabling the use of genomic data to localize a sample's birth coordinates wi

271 Submission of genomic data to NCBI GenBank is a requirement prior to p

272 1B) is an important consideration when using genomic data to predict susceptibility to TZP.

273 lizes the widely available re-sequencing and genomics data to create more realistic simulations and t

274 idy" data principles, we create a grammar of genomic data transformation, defining verbs for performi

275 CBNA integrates different genomic data types, including gene expression, gene netw

276 , while also offering visualization of other genomic data types.

277 more prognostic of survival time than other genomics data types; and gene modules/signatures are at

278 age, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation and fact

279 ble for interactive data query or as part of genomic data visualization platforms such as genome brow

280 0073); and (iv) classification incorporating genomic data was highly predictive of recurrence (OR 13.

281 In a subset of cases with extensive genomic data, we found no significant association betwee

282 Toward predicting bleaching response from genomic data, we generated a chromosome-scale genome ass

283 By linking the resistance phenotypes to genomic data, we reveal the interplay of genetic lineage

284 integrating multiple sources of genetic and genomic data, we show that putative G-quadruplex forming

285 ing these measurements with population-scale genomic data, we show that the response of a model repli

286 Genomic data were analyzed using multi- and pangenomic t

287 The genomic data were divided into a training set of N = 835

288 ecame 'personalized' when cell line-specific genomic data were included into simulations, again valid

289 undreds of bacterial species using published genomic data, which suggest that the great majority of T

290 hown great utility in tackling this flood of genomic data, while using minimal compute resources.

291 Here, we combine genomic data with comprehensive epidemiological data on

292 By coupling our genomic data with domestic and international travel patt

293 increasing integration of archaeological and genomic data with insights from herbarium collections, t

294 plasms and assess whether the integration of genomic data with morphologic diagnosis improves classif

295 The interpretation of accumulating genomic data with respect to tumor evolution and cancer

296 es from multiple types of highly-dimensional genomic data with very different signal properties opens

297 integrate multiple types of high-dimensional genomics data with clinical data for predicting survival

298 ion in the portal is quantified by combining genomics data with rich proteomic annotations.

299 Integrating high-throughput functional genomics data with this information can help identifying

300 atforms for managing, analyzing, and sharing genomic data, with an emphasis on data commons, but also