ライフサイエンスコーパス: omics

1 oth models rest on large-scale genome-wide "-omics'.

2 ewater treatment plant using integrated meta-omics.

3 reproducibility and integration with other 'omics.

4 rue IDs based on data relationships of multi-omics.

5 ional and mechanistic interpretations across omics.

6 ires, biological samples, imaging data, and -omics.

7 us and high-dimensional (p>>n) data, such as OMICS.

8 pound will be of big help for LC-MS/MS-based omics.

9 We perform meta-omics analyses (metagenomics, metatranscriptomics, metap

10 Novel microbiological techniques and omics analyses have led to the development of several te

11 calls for integrative multi-omics and inter-omics analyses with approaches in 'systems genetics and

12 ic genome-wide association studies and multi-omics analyses, respectively.

13 Using this system and quantitative omics analyses, we demonstrate that acetyl-CoA depletion

14 ely used to describe the results of various -omics analyses.

15 coupled with the increased availability of 'omics' analyses and high-throughput screening technologi

16 Here, single-cell multi-omics analysis demonstrates a shared mechanism with lymp

17 Here, using multi-omics analysis for ovarian cancer, we identified a novel

18 Recent omics analysis has led to the elucidation of hemp candid

19 Integrated-omics analysis identified biologically meaningful RV bro

20 Further in-depth multi-omics analysis identified sphingomyelins as key secreted

21 Data mining of the present multi-omics analysis identifies two compound classes that anta

22 t potential for high-throughput quantitative omics analysis of ultralow-volume samples.

23 hat provides an interactive visualization of omics analysis outputs and efficient data management.

24 Integrated omics analysis showed significant associations between P

25 report more than 1000 seasonal variations in omics analytes and clinical measures.

26 This multi-omics and cell-based profiling showed an immediate redir

27 A model including all omics and clinical variables yielded a cross-validated r

28 However, the combination of omics and clinical variables yielded the highest accurac

29 Omics and drug-targeting studies revealed that PI3Ks act

30 These data show how multi-omics and imaging can identify critical features of RS a

31 gration and application of single-cell multi-omics and imaging, artificial intelligence and patient-d

32 o the status quo calls for integrative multi-omics and inter-omics analyses with approaches in 'syste

33 Herein, the unbiased omics and other approaches were used to study 5XFAD mice

34 cotoxicology, evolution, genome editing and 'omics', and human disease modelling.

35 method (AW-Fisher), initially developed for omics applications but applicable for general meta-analy

36 Thus, the multilayer omics approach identifies protein networks during AD pro

37 An integrated omics approach of patient-derived genomics and transcrip

38 rcadian period is heritable and used a multi-omics approach to investigate underlying mechanisms.

39 imated branches of this tree, we use a multi-omics approach to quantify age-related somatic changes a

40 We have investigated, using a multi-omics approach, adaptation of A. niger colonies to spati

41 Using a multi-omics approach, we find no evidence ALKBH7 functions as

42 By using a multi-omics approach, we found that Ezh2 is required for the d

43 g classic microbial physiology with a 'multi-omics' approach consisting of transposon-directed insert

44 In this Review, we discuss omics approaches and discoveries with the potential to e

45 understanding of FLS biology and highlights omics approaches and studies that hold promise for ident

46 We then investigated results of different omics approaches for both bedside diagnosis of immune dy

47 While high-throughput omics approaches have been increasingly utilized to reve

48 So far, the omics approaches have provided mechanistic understanding

49 sed to benefit from these integrative, multi-omics approaches since the kidney biopsy, blood and urin

50 ular precision medicine that use single-cell omics approaches to characterize cell-specific responses

51 Using omics approaches to monitor complex environmental mixtur

52 ecent application of quantitative/integrated omics approaches to the physiological analysis and metab

53 n excellent target for hypothesis-generating omics approaches, as the disease group is mechanisticall

54 We discuss the application of -omics approaches, including transcriptomics, epigenomics

55 tem is now tractable using single-cell multi-omics approaches.

56 e developed ORSO (Online Resource for Social Omics) as an easy-to-use web application to connect life

57 e-genome sequencing, methylation, and other 'omics assays.

58 To date, however, an integrated omics assessment of atherosclerotic lesions in individua

59 We further used a multi-omics assessment to determine potential off-targets on t

60 d and this information, in conjunction with 'omics'-based strategies, is used to determine subunit st

61 de an exciting translational avenue to merge omics-based drug discovery platforms with patient-specif

62 ) expanding cultivation attempts to validate omics-based metabolic models of yet-uncultured organisms

63 Omics-based methods may provide new markers associated t

64 upon evidence of these compounds from recent omics-based studies of cnidarian-dinoflagellate symbiosi

65 Omics-based technologies now allow for analysis of multi

66 Using an integrative omics, biochemical and imaging approach, we dissected th

67 ed with prospective biospecimen collections, omics biomarker analyses and a molecular pathological ep

68 ity of Bacteria and Archaea; however, while 'omics can be used to infer physiological or ecological r

69 lti-omic casual analysis to infer multilevel omics causal networks for the discovery of common paths

70 were connected to both AD and T2DM in multi-omics causal networks.

71 etween diseases, with applications for multi-omics characterization of disease relationships.

72 Multi-omics clustering revealed four subgroups defined by key

73 pes of omics data and thereby enabling multi-omics comparison and visualization at both gene and path

74 do not directly support the high-dimensional omics data across the whole genome (Such as ATAC-seq pro

75 nd guide process optimization, comprehensive omics data analysis and management have been a challenge

76 ics is a Shiny-based application for linked -omics data analysis, allowing users to visualize microbi

77 y used in genomic risk prediction, for multi-omics data analysis.

78 with different combinations of clinical and omics data and identified biological features that appea

79 ation analytics will maximize the utility of omics data and lead to a new paradigm for biomedical res

80 select features from the different layers of omics data and random forest analysis to develop the mod

81 However, analyzing the resulting multi-omics data and their correlations remains a significant

82 he flexibility to accommodate other types of omics data and thereby enabling multi-omics comparison a

83 to other microbiome systems for which multi-omics data are available.

84 , enormous amounts of DNA sequence and other omics data are generated.

85 To this end, omics data are integrated with other data types, e.g., c

86 Systems biologists currently working with omics data are invited to consider phase portrait analys

87 Omics data are then used to assign penalties to genes, i

88 been made not only in gathering terabytes of omics data but also in detailing the histologic-molecula

89 cIGANs is not only an application of GANs in omics data but also represents a competing imputation me

90 ly the predictive effects from each layer of omics data but also their interactions via using multipl

91 study demonstrates that integration of multi-omics data can help identify critical molecular determin

92 entering an era of 'big data' and molecular omics data can provide comprehensive insights into the m

93 tes how active learning can be used to guide omics data collection for training predictive models, ma

94 The advent of multi-omics data enables measurement of both DNA methylation a

95 emonstrate here to analyze large-scale multi-omics data from a natural soil environment is applicable

96 We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epi

97 In summary, our multi-omics data from the hybrid mice provided haploid-specifi

98 In the analysis of high-throughput omics data from tissue samples, estimating and accountin

99 d to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell l

100 e challenge in predictive model building for omics data has been the small number of samples (n) vers

101 lowering, and highlight the utility of multi-omics data in deciphering important ornamental traits in

102 ion based on prospective clinical and (multi)omics data in incident PD cases.

103 yers of symptoms, physiological changes, or -omics data in isolation will allow for validation of mec

104 g strategies to integrate these two types of omics data in order to further accelerate discovery.

105 Even though very limited studies have multi-omics data in place, we expect such data will increase q

106 The diverse and growing omics data in public domains provide researchers with tr

107 By integrating multi-omics data in the QSMART model, we not only predict drug

108 m of assessing associations between multiple omics data including genomics and metabolomics data to i

109 distinct molecular subgroups based on multi-omics data is an important issue in the context of preci

110 Increasingly, omics data is being used to complement this, as it has t

111 ple information, genotype, and corresponding omics data is critical for integrative analyses.

112 of lung adenocarcinoma (LUAD) based on multi-omics data of long non-coding RNAs (lncRNAs), microRNAs

113 The increasing omics data present a daunting informatics challenge.

114 While multi-layer high-dimensional omics data provide unprecedented data resources for pred

115 rstanding the interactions between different omics data requires increasingly complex concepts and me

116 Functional omics data reveals extensive variation in gene expressio

117 analysis in simulations and apply it to two omics data sets illustrating the integration of gene exp

118 ing number of large-scale, multidimensional 'omics data sets.

119 of the PI cycle informed by experimental and omics data taken from a single cell type, namely platele

120 ol for integrative analysis of CHO cell line omics data that provides an interactive visualization of

121 oaches to use network models in concert with omics data to better characterize experimental systems h

122 e developed a strategy to integrate multiple omics data to identify AF-related genes.

123 oonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes.

124 sampling and integrating complementary multi-omics data to identify functional mechanisms that can se

125 or new visualizations of other longitudinal -omics data types.

126 ests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups

127 k, HumanNet, by integrating diverse types of omics data using Bayesian statistics framework and demon

128 Methods and Results: Three types of omics data were integrated: (1) summary statistics from

129 tive regions as well as predictive layers of omics data, and achieves robust selection performance.

130 egy that combines pharmacovigilance data and omics data, and evaluate associations between multi-omic

131 ined integration of experiments, large-scale omics data, and mathematical modeling, complemented by t

132 w, we describe the techniques and sources of omics data, outline network theory, and highlight exempl

133 nologies have given rise to an abundance of -omics data, particularly metabolomic data.

134 d be obtained through integration with multi-omics data, reproducibility of published studies, or met

135 pathways, users can upload and analyze their omics data, such as the gene-expression data, and overla

136 Through applying these techniques to omics data, systems biology addresses the problems posed

137 et of machine learning experiments on cancer omics data, we find that current prevalent schemes of mu

138 By integrating additional omics data, we find that highly secretory cells have ada

139 developing a pipeline for integrating multi-omics data, we identify 789 (~17%) phosphorylation sites

140 nal AF-related genes by integrating multiple omics data.

141 using both pharmacovigilance data and multi-omics data.

142 lution by computational integration of multi-omics data.

143 ted that labeling errors are not uncommon in omics data.

144 es are increasingly generating multiplatform omics data.

145 e a scaffold for the integrative analysis of omics data.

146 ackage for clustering analysis of multilayer omics data.

147 ,632 genome sequences and longitudinal multi-omics data.

148 hed literatures, PPI-network and large-scale omics data.

149 addition to sparse deep learning models for omics data.

150 ncer with candidate genes by analyzing multi-omics data.

151 enefit from integrative analyses of multiple omics data.

152 ery - the design, collection and analysis of omics data; representation - the iterative modelling, in

153 verse range of R/Bioconductor packages into 'omics' data analysis workflows represents a significant

154 The high dimensional (omics) data from the molecular analyses of the correspon

155 at exploits the integration of complementary omics-data as prior knowledge within a Bayesian framewor

156 This Bayesian omics-data fusion based methodology allows to gain a gen

157 at allows rapid collaborative exploration of omics-data.

158 arium of comprehensive, large, foundational -omics databases, across species and capturing developmen

159 le causal networks on a large human AD multi-omics dataset, integrating clinical features of AD, DNA

160 have enabled researchers to collect multiple OMICS datasets for the same individuals.

161 The ready availability of multi-omics datasets has led to the development of numerous me

162 arch; however, inferring interactions across omics datasets has multiple statistical challenges.

163 Multi-omics datasets represent distinct aspects of the central

164 Overall pathway analyses on the multi-omics datasets showed significant enrichment for mitocho

165 this work we show that integrating multiple OMICS datasets together, instead of analysing them separ

166 tools, including glycan biosynthesis models, omics datasets, and systems-level analyses.

167 ocusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal

168 l for generating and testing hypotheses from omics datasets, this study puts forth a means to identif

169 distinct profiles with regards to the other omics datasets, with strong underlying connections betwe

170 ative analysis of multiple high-dimensional -omics datasets.

171 be used to access, discover and disseminate omics datasets.

172 est, or to test for associations between the OMICS datasets.

173 explains the poor overlap between published "omics"-defined NASH signatures.

174 The Omics Discovery Index is an open source platform that ca

175 e of the recent surge of information in this omics era.

176 y the availability of large datasets in the "omics" era of biology, the design of the next generation

177 Integrated multi-omics evaluation of 823 tumors from advanced renal cell

178 th mutations and methylations based on multi-omics evidence.

179 nd assess the optimal sample size in a multi-omics experiment.

180 method (coined OPEX) to identify informative omics experiments using machine learning models for both

181 genuity Pathway Analysis, miRDB, and Qlucore Omics Explorer.

182 vidual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation ap

183 We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framewor

184 ata, and evaluate associations between multi-omics factors and irAE reporting odds ratio across diffe

185 er, the integration of several related multi-omics features facilitated identifying and annotating th

186 ability of LoopPredictor, we fed mouse multi-omics features into a model trained on human data and fo

187 -genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) Program, we called

188 279) with WGS (~38x coverage) from the Trans-Omics for Precision Medicine (TOPMed) program.

189 rom COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program.

190 ional Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and ide

191 s Project, and an index of all 108 070 Trans-Omics for Precision Medicine Freeze 5 chromosome 17 hapl

192 nd 17,822 replication samples from the Trans-Omics for Precision Medicine Program.

193 puted whole-genome sequencing from the Trans-Omics for Precision Medicine project to identify novel l

194 ries (e.g. The Cancer Genome Atlas and Trans-Omics for Precision Medicine) have the potential to revo

195 Multi-omics (genetic, transcriptomic, proteomic, and metabolom

196 From the 63 solution-sets, our omics guided process identifies one experimentally feasi

197 The recent boom in single-cell omics has brought researchers one step closer to underst

198 fective repurposing efforts using big data ("omics") have been designed to characterize drugs by stru

199 A model ensemble integrating multi-omics identified 64% of the non-responders with 80% conf

200 h expectations to promote the broader use of omics in CHO cell research.

201 New technologies such as single-cell omics, in combination with functional studies, will be i

202 ially growing biomedical literature and deep omics insights is unavailable.

203 re we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping.

204 Multi-omics integration prioritizes AD-related molecules and p

205 The development of multi-omics integrative analyses have enabled new ways to diss

206 fied interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporate

207 Altogether, we argue that large multi-omics investigations have pushed brain development into

208 The use of omics is gaining importance in the field of nanoecotoxic

209 hylome prediction (WGMP) with combining both omics layers.

210 en partitioning phenotypic variance by multi-omics layers.

211 we present RETrace as a foundation for multi-omics lineage mapping and cell typing of single cells.

212 a top-down method, 'nativeomics', unifying 'omics' (lipidomics, proteomics, metabolomics) analysis w

213 egrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of und

214 ally extract biological interactions between omics markers.

215 We conducted multidimensional omics measurements including protein, mRNA, and transcri

216 The approach can be extended to other omics measurements such as proteomics and is thus of rel

217 is shown in simulations as well as two real omics meta-analysis applications to demonstrate its insi

218 We recently developed scDam&T-seq, a multi-omics method that can simultaneously quantify protein-DN

219 approach combining anatomic techniques with omics methodology in a tenotomy-induced sheep model of r

220 recent advances in large-scale, quantitative omics methods as well as in integrative analytical strat

221 We used integrative, high resolution multi-omics methods to delineate the methylome landscape and c

222 methodologies along with clinical and multi-omics observations.

223 measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels o

224 the mechanistic underpinnings through cross-omics of metabolites and inflammatory proteins.

225 roughs in genome engineering and the various omics, organoid technology is making possible studies of

226 We investigated a wide omics panel of metabolites and lipids related to DR in t

227 egrative analysis of molQTLs through a multi-omics perspective.

228 med from an obscure specialty into a major "-omics" platform for studying metabolic processes and bio

229 ration of independent data resources across -omics platforms offers transformative opportunity for no

230 of metabolomics data that has enabled other -omics platforms to make impactful discoveries and meanin

231 g repurposing, its integration with multiple omics platforms, and how this data can be used for clini

232 allergic disorders and the central role that omics play in creating molecular signatures and biomarke

233 this study, we consider the circadian genes' omics profile, such as copy number changes and RNA-seque

234 a computational framework integrating multi-omics profiles analyses, including RNA sequencing (RNA-s

235 uated the contribution of different types of omics profiles for assessing drug response.

236 y, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic ch

237 d miRNAs (DEmiRs) were identified from multi-omics profiles.

238 es, point out the dissimilarity in different omics-profiles, and overlay the transcriptional response

239 nition of health and show that comprehensive omics profiling in a longitudinal manner is a path forwa

240 Genome-wide multi-omics profiling of complex diseases provides valuable re

241 med an integrative network analysis of multi-omics profiling of four cortical areas across 364 donors

242 Future studies should include omics profiling to investigate sex-associated molecular

243 study of inborn errors of immunity and multi-omics profiling together with developments in analytical

244 Unbiased omics profiling, such as whole genome bisulfite sequenci

245 Omics-profiling is a collection of increasingly prominen

246 WAS summary statistics with multiple sets of omics QTL summary statistics from different cellular con

247 ulk segregant analysis, and high-throughput 'omics readouts.

248 the past several years, as genomic and other omics-related experiments have become more cost-effectiv

249 We then discuss the latest multi-omics research combining high-throughput phenotyping wit

250 The toolkit can help omics researchers perform quality control and exchange i

251 Different 'omics' resources capture various equally important biolo

252 s, including nanotechnology, microfluidics, -omics science, next-generation sequencing, genomics big

253 In this quest, omics sciences could help to gain new insights to unders

254 Single-cell multimodal omics (scMulti-omics) technologies have made it possible

255 y molecular mechanisms, represented by cross-omics scores.

256 lved with the application of high-resolution omics screening to populations enrolled in large-scale o

257 eterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) for co-mapping of mRNAs and

258 hnology, from imaging approaches through to 'omics' strategies.

259 Recently our omics studies identified activated ephrin (EPH) receptor

260 we review the history of the development of omics studies of C. acetobutylicum, summarize the recent

261 The use of DRAMS in multi-omics studies will strengthen statistical power of the s

262 red studies and will pave the way for larger omics studies, including proteomics, metabolomics and li

263 et, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking f

264 rom transgenic manipulations and large-scale omics studies.

265 s potentials have not been fully utilized in omics studies.

266 trongly argues for the need of human-focused omics studies.

267 In this retrospective omics study, we performed targeted proteomics (N=625) of

268 To reveal these causes, we used a multi-omics, systems biology analytical approach using biomedi

269 Advanced 'omics techniques can help to accelerate breeding by faci

270 Omics techniques generate large, multidimensional data t

271 therefore only provide average information (-omics techniques in particular), which could obscure imp

272 With the advent of high-throughput -omics technologies and more advanced computational model

273 st decade, systems-level approaches based on omics technologies have become an important approach for

274 ssociation studies and studies using various omics technologies individually to identify mechanisms o

275 he application of single-cell and integrated omics technologies to the identification of refractory R

276 ns, along with leveraging novel sensing and -omics technologies to understand microbial fitness in th

277 ide association studies, advances in various omics technologies, including genomics, transcriptomics,

278 an exponential growth in the development of -omics technologies.

279 nalysis workflows for metabolomics and other omics technologies.

280 dentify molecularly driven phenotypes using "omics" technologies.

281 Advances in a range of 'omics' technologies and statistical approaches, includin

282 Given these emerging scenarios, downstream 'omics' technologies reflective of edited affects, such a

283 (i) concerted efforts in the advancement of 'omics' technologies, such as metabolomics, and (ii) an e

284 The advent of high-throughput (omics) technologies gives opportunities to perform media

285 Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellu

286 erimental technologies for single-cell multi-omics that enable the capture and integration of multipl

287 d illustrates the power of single-cell multi-omics to discover tumor-specific therapeutic targets and

288 anomedicine, biosensors, microfluidics, and -omics to enable early diagnosis of HIV.

289 By applying advanced microscopies and omics to in vitro and in vivo systems, together with in

290 We applied multi-omics to reveal that intracellular triacylglycerols (TAG

291 a broad range of species and evolve as a new omics tool in environmental health assessment.

292 Although single-cell omics tools can yield snapshots of the cell-state landsc

293 s are still emerging, being identified when "omics" tools (genomics, proteomics, and transcriptomics)

294 association patterns of SNPs to complex and omics traits.

295 istical principles are now available for all omics types.

296 The prediction models included clinical and omics variables separately or in combination.

297 d better prediction ability than the complex omics variables.

298 uman studies and lack of an integrated multi-omics view of disease-specific physiological changes.

299 The use of multiple omics was essential, as some MoAs were virtually undetec

300 genomic and phenotypic studies will enhance 'omics-wide associations of molecular signatures with agr