ライフサイエンスコーパス: big data

1 ious populations, cell types, and disorders (big data).

2 leveraging novel approaches using omics and big data.

3 unknown and multivariate distinctions within big data.

4 ers and hierarchical statistical analysis of big data.

5 ess of machine learning models in the era of big data.

6 o directly extract functional gene sets from big data.

7 of the body including the skin is in era of big data.

8 powerful strategy in general for mining such big data.

9 approaches particularly suited to leveraging big data.

10 ponse element reporter gene assay models and big data.

11 is and attend to new challenges presented by big data.

12 iscuss recent developments related to use of big data.

13 lp researchers navigate and understand these big data.

14 ecially in the era of biological and medical big data.

15 ecially in the era of biological and medical big data.

16 cy and the evolving scientific enterprise of big data.

17 We live in the era of genomics and big data.

18 elopment status using highway transportation big data.

19 cer management by unlocking the potential of big data.

20 experimental models and multiomics-generated big data.

21 2)), rendering BP prohibitive for modern-day big-data.

22 how can reliable knowledge be extracted from big data?

23 ular, has been enabled by the use of labeled big data, along with markedly enhanced computing power a

24 Big data analyses could benefit the planet if tightly co

25 Recent big data analyses have illuminated marine microbial dive

26 Big data analyses reveal that the set of optimal influen

27 e designed to meet the increasing demand for big-data analyses, ranging from bulk sequence processing

28 ologies are critical to support the types of big data analysis necessary for kidney precision medicin

29 Machine Learning (ML) is a powerful tool for big data analysis that shows substantial potential in th

30 ant for applications of AutoML to biomedical big data analysis.

31 Multithreading and indexing enable efficient big data analysis.

32 d methods of data collection, responding to "big data" analysis challenges.

33 software provides built-in optimization for 'big data' analysis by storing all relevant outputs in an

34 rds from the Baidu index platform, a Chinese big data analyst similar to GFT.

35 neous datasets presents new opportunities to big data analysts, because the knowledge that can be acq

36 Applying big-data analytic techniques to brain images from 18,707

37 Combining the power of Big data analytics (including AI) with existing and futu

38 Thus, emerging tools in big data analytics and systems biology are facilitating

39 iterative process based on interpretation of big data analytics from birth and patient cohorts, respo

40 Medical big data analytics has revolutionized the human healthca

41 sisting, but also promote the development of big data analytics in intelligent sports industry.

42 urthermore, ongoing advances in the field of big data analytics promise to create an exciting opportu

43 nerator for self-powered sensing in athletic big data analytics.

44 ution in the water sector will encounter the Big data and Artificial Intelligence (AI) revolution.

45 re was just discovered to the present day of big data and epigenetics.

46 Ultimately, the incorporation of big data and experimental medicine approaches should aim

47 compute intensive making them unsuitable for big data and high-throughput environments.

48 mart systems, photonics, robotics, textiles, Big Data and ICT (information & communication technologi

49 drug development and biomedical engineering, big data and information technology and allergic disease

50 that can be addressed by the integration of big data and interpreted using novel analytical methodol

51 as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment t

52 base quality: notwithstanding the arrival of big data and machine learning, it remains perilous to ig

53 an genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc.

54 Big data and predictive analytic methods will be part of

55 PIA fills in the gap between cancer genomics big data and the delivery of integrated information to e

56 rch in the interpretation and application of big data and the impact of digital technologies on healt

57 ation discusses the security implications of Big Data and the need for security in the life sciences.

58 We discuss sources of big data and tools for its analysis to help inform the t

59 ional genomics studies have stepped into the big-data and high-throughput era.

60 Kidney research is entering an era of 'big data' and molecular omics data can provide comprehen

61 asing availability of large public datasets (big data) and computational capabilities, data science i

62 o many imaging processing problems involving big data, and have recently shown potential for the stud

63 Advances in imaging technology, big data, and machine learning, as well as carefully des

64 cience, next-generation sequencing, genomics big data, and machine learning, could contribute to meet

65 Polyomics, big data, and systems biology have demonstrated a profou

66 Here we improved LSTM for big data application in protein-protein interaction inte

67 Other big data applications and uses of the dataset are discus

68 ology, however, cannot scale to satisfy such big data applications with the required throughput and e

69 available for analysis-the typical case for big data applications-the replications permit convention

70 In study 2, we employed a big data approach to explore the time course of rumor tr

71 Here we show, using a big-data approach that combines satellite-tracked moveme

72 mic data needed to build the tree, we used a Big-data approach that searched automatically epidemiolo

73 level with CV and non-CV mortality using a "big data" approach.

74 ogy to quantifying brain features, requiring big data approaches for data integration.

75 discuss the benefits and challenges of such Big Data approaches in biology and how cell and molecula

76 c and phenomics data, will take advantage of big data approaches including machine learning to unders

77 ic data, demand cerebrovascular expertise on big data approaches to clinically relevant paradigms.

78 e much more knowledge when interrogated with Big Data approaches, such as applying integrative method

79 Therefore, longitudinal studies and big-data approaches assessing sleep dynamics are lacking

80 "Big data" approaches in the form of large-scale human ge

81 Integrating "big data" approaches into treatment decisions and trial

82 a re-use activities in the field, including 'big data' approaches, is enabling novel research and new

83 Big data are an important ingredient for furthering our

84 arge volumes of high-resolution measurements-big data-are now being brought to bear on this problem.

85 entify, but we find it most useful to define Big Data as a data collection that is complete.

86 Biomedical big data, as a whole, covers numerous features, while ea

87 ormation about the hidden structures within "big data" assets, and medical professionals, epidemiolog

88 Global neuroscience projects are producing big data at an unprecedented rate that informatic and ar

89 highly scalable, enabling it to exploit the 'big data' available in modern genomics.

90 Here, I argue that big data biology also raises fundamental questions in th

91 In the era of big data biology, tandem mass spectra are often searched

92 ng precision health requires making sense of big data, both at the population level and at the molecu

93 we outline the legal and ethical challenges big data brings to patient privacy.

94 ncreases TPOT's efficiency in application on big data by slicing the entire dataset into smaller sets

95 gnition and visualization of similarities in big data can also infer the network angular coordinates

96 me these challenges by investigating whether big data can be used to benchmark a highly phenotyped he

97 Advantages of big data can include ease and low cost of collection, ab

98 Big Data can point toward unexpected correlations, and t

99 nference from large observational databases (big data) can be viewed as an attempt to emulate a rando

100 s can lead to novel investigations; however, Big Data cannot establish causation.

101 n diagnostics, remote rhythm monitoring, and big data capabilities, we anticipate that adoption of a

102 Recent advances in personalized medicine, "big data", causal inference using observational data, no

103 g benchmark study, DREAM Alzheimer's Disease Big Data challenge to predict changes in Mini-Mental Sta

104 Here we use big data collected by the American Bureau of Transportat

105 In the new era of internet of things, big data collection and analysis based on widely distrib

106 Oncology, the field where Big Data collection and utilization got a heard start wi

107 But with big data comes big risks and challenges, among them sign

108 ed our algorithm with other state of the art big data compression algorithms namely gzip, bzip2, fast

109 B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and gli

110 We demonstrate such Big Data computing paradigms can provide orders of magni

111 The brain processes and learns from "big data" concurrently via trillions of synapses in para

112 ve, or even suggest, that location and other big-data data sets can be anonymized and of general use.

113 ive parameters from medical images to create big-data data sets that can identify distinct patterns i

114 Harnessing big data, deep data, and smart data from state-of-the-ar

115 it requires complex multi-step processing of big data demanding considerable computational expertise

116 ther the needs of genomics will exceed other Big Data domains.

117 Big Data drawing on Medicare claims and IRIS Registry re

118 based disparities of TNBC.Significance: This big data-driven study comparing normal and cancer transc

119 se requirements could be met by developing a Big-Data-driven stem cell science strategy and community

120 amentally restrains computers from handling "big data" efficiently.

121 s of computational toxicology in the current big data era and can be extended to develop predictive m

122 Big data era in genomics promises a breakthrough in medi

123 The urgency is more obvious in the big data era when GWAS are conducted simultaneously for

124 yze and interpret OMICs-based studies in the big data era.

125 breakthrough in associative genomics in the big data era.

126 y useful tool for biomarker discovery in the big data era.

127 quencing techniques has led biology into the big-data era.

128 ions have pushed brain development into the "big data" era, and that current and future transversal a

129 in C++, accounts for compatibility with the 'big data' era in biological science, and it primarily fo

130 In this 'big data' era, the research community has identified a s

131 ut in developing countries, fewer sources of big data exist.

132 Testing for associations in big data faces the problem of multiple comparisons, wher

133 ative Medicines Initiative 2 and part of the Big Data for Better Outcomes Programme (BD4BO), the over

134 We consider a broad definition of big data for public health, one encompassing patient inf

135 chine-learning approach using remote sensing big data for the detection of archaeological mounds in C

136 a, but the effective mining and modeling of 'big data' for new biological discoveries remains a signi

137 Analysis of "big data" frequently involves statistical comparison of

138 rful computers and availability of so-called big data from a variety of sources means that data scien

139 Big data from clinical registries like the IRIS Registry

140 Simulation models based on big data from EHRs can test clinic changes before real-l

141 the level of individual patients), building "big data" from a longitudinal perspective and not only a

142 with cardiovascular imaging, combined with "big data" from the electronic health record and patholog

143 ches and the need for new capacity to manage big data generated in advanced health research.

144 le computationally intensive analysis of the big data generated.

145 t of high-throughput technologies leading to big data generation, increasing number of gene signature

146 This Series paper will highlight emerging big data genomic approaches with the potential to accele

147 The Age of Big Data harbors tremendous opportunity for biomedical a

148 Big data has become the ubiquitous watch word of medical

149 The availability of big data has the potential to transform many areas of th

150 ovides an overview of how the realization of big data has transformed our field and what may lie in s

151 In recent years, "big data" has been sold as a panacea for generating hypo

152 "Big Data" has surpassed "systems biology" and "omics" as

153 his work, we provide examples of how spatial big data have been used thus far in epidemiological anal

154 While big data have proven immensely useful in fields such as

155 Spatial big data have the velocity, volume, and variety of big d

156 he implications of the rising use of spatial big data in epidemiology to health risk communication, a

157 d Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) is a European network of ex

158 erials design enabled by the availability of big data in imaging and data analytics approaches, inclu

159 d ethical challenges with the use of spatial big data in infectious disease surveillance and inferenc

160 ilitate the integration and understanding of big data in neuroscience.

161 ) and other nations in the implementation of Big Data in patient care with regards to their centraliz

162 EER) is a European network of excellence for big data in prostate cancer, consisting of 32 private an

163 highly scalable approach for the analysis of big data in proteomics, i.e. microbiome or metaproteome

164 ploiting the increasingly available genomics big data in statistical inference and presents a promisi

165 us Diseases to review the recent advances of big data in strengthening disease surveillance, monitori

166 nction prediction, and our ability to manage big data in the era of large experimental screens.

167 tomatic data exchange within the context of "Big Data" in biology.

168 ening an unprecedented opportunity of using "big data" in biomedical text mining.

169 'Big data' in healthcare encompass measurements collated

170 ll;31/10/tpc.119.tt0819/FIG1F1fig1The age of big data includes sophisticated imaging datasets.

171 onal tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medic

172 With big data increasingly enabling genomic discovery in psyc

173 This availability enables big data informatics approaches to be used to study the

174 Research Team (CANHEART) Immigrant study, a big data initiative, linking information from Citizenshi

175 To demonstrate the potential for integrating big data into a functional microbiology workflow, we rev

176 ressively pursue the integration of emerging big data into regional transportation emissions modeling

177 Big Data is a premier example, especially with respect t

178 Big data is a term used for any collection of datasets w

179 not only process, but ideally also interpret big data is becoming continuously more pressing.

180 A clear definition of what constitutes "Big Data" is difficult to identify, but we find it most

181 "Big data" is the most frequently cited topic currently l

182 The era of 'big data' is also the era of abundant data, creating new

183 hiving and sharing approaches, to tackle the big data issues presented by biologging.

184 thereby avoiding the many restrictions (and big data issues) that can accompany access to individual

185 Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle

186 Here, we examine how this big data-led approach is impacting many diverse areas of

187 istry, * ca. 2020?" Then move to examples of Big Data, machine learning and neural networks in action

188 ry, * ca. 2020?" Then we move to examples of Big Data, machine learning and neural networks in action

189 Big data management for information centralization (i.e.

190 s study underscores how a common practice in big data mapping studies shows an apparent high predicti

191 Instead of relying exclusively on big data measurements of initial conditions, we should a

192 Existing datasets, combined with 'big data' methodologies, can serve as a platform to supp

193 roaches range from drug discovery efforts to big-data methods and direct-to-consumer (DTC) strategies

194 at having so many materials allows us to use big-data methods as a powerful technique to study these

195 re, we discuss the application of so-called "big-data" methods to high dimensional microscopy data, u

196 good precedent for the study of reservoirs, big data mining, predictions and subsequent outbreaks of

197 New image-based 'big data' mining techniques enable the large-scale compa

198 the current study, we use spatial navigation big data (n = 27,108) from the Sea Hero Quest (SHQ) game

199 t assays of fully random DNA can provide the big data necessary to develop complex, predictive models

200 Big data now deposited in the TCGA network offers a wind

201 e brought about great progress in processing big data obtained from high-dimensional chromatographic

202 the underlying GRN, a model that integrates big data of diverse types is expected to increase both t

203 Today, "big data" often generated by administrative activities o

204 and cost-effective repurposing efforts using big data ("omics") have been designed to characterize dr

205 cking software can be used for analyzing the big data on doses for auditing patient safety, scanner u

206 time perspectives can be approximated using "big data" on search engine queries, and that these indic

207 cedented amounts of clinical data-so called "big data"-on a minute-by-minute basis.

208 Insights distilled from integrating multiple big-data or "omic" datasets have revealed functional hie

209 that is entirely different from the existing Big Data paradigm.

210 tantial pressure because of the emphasis on "big data," phenomenology, and personalized medical thera

211 g desirable scaling for future transcriptome Big Data platforms.

212 gful interpretation of such complex forms of big data poses serious challenges to users of MD.

213 er genomes and transcriptomes has produced a big data problem that precludes many cancer biologists a

214 on of the algorithm, SJARACNe, to solve this big data problem, based on sophisticated software engine

215 tworks have gained immense popularity in the Big Data problem; however, the availability of training

216 algorithmic developments to tackle emerging 'big data' problems in biomedical research brought on by

217 To support big data processing capability, a novel parallel match s

218 e imbalance between algorithm innovation and big data processing has been more serious and urgent.

219 neurons, thus paving the way towards robust big-data processors.

220 cedented growth of sequencing information in big data projects.

221 Answering genetics' big data questions often needs an interdisciplinary team

222 mprove efficiency, flexibility, support for 'big data' (R's long vectors), ease of use and quality ch

223 We develop BIRD, Big Data Regression for predicting DH, to handle this hi

224 has contributed to very large data sets (ie, big data) relevant for public health.

225 Emerging scientific endeavors are creating big data repositories of data from millions of individua

226 Big data repositories, including those for molecular, cl

227 ortium to unite scientists with expertise in big data research and epidemiology to develop the COVID

228 to Big Data such as the lack of diversity in Big Data research, and the security and transparency ris

229 rehensive resource for clinical practice or "big data" research.

230 CANHEART study cohort to serve as a powerful big data resource for scientific research aimed at impro

231 ntal and Craniofacial Research in 2009 as a 'big data' resource for the craniofacial research communi

232 on of progression algorithms and of applying big-data results to individual practices.

233 We conclude that transportation big data reveal the status of regional economic developm

234 Big data reveals new, stark pictures of the state of our

235 The big data revolution has transformed the landscape of imm

236 illance systems and awaiting the fruits of a big data revolution.

237 Overall, we are optimistic that the big-data revolution will vastly improve the granularity

238 ased power and speed of computers, with the "big data" revolution having already happened in genomics

239 Genomics is a Big Data science and is going to get much bigger, very s

240 These data types present new challenges to big data science.

241 w gives an introduction to the principles of big-data science.

242 impact of mobile technologies and associated big data sets are outlined.

243 ghlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepa

244 The interpretation of the resulting big data sets is complex and often constrained due to a

245 ledge across highly heterogeneous biomedical big data sets.

246 In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size

247 y and data-security concerns associated with Big Data should not be taken lightly.

248 is elegans have a long history of collecting Big Data, since the organism was selected with the idea

249 data transfer, we created a tool called the Big Data Smart Socket (BDSS) that abstracts data transfe

250 For instance, for big-data social networks of 200 million users (e.g., Twi

251 ffordable approach to this problem, bringing big data solutions within the reach of laboratories with

252 -bla computational model and HTS data from a big data source (PubChem) were used to profile environme

253 ta have the velocity, volume, and variety of big data sources and contain additional geographic infor

254 and cost-effectiveness of utilizing existing big data sources to conduct population health studies.

255 asket and umbrella studies and research from big data sources, such as electronic health records, adm

256 truction of biological networks derived from big data sources, such as MEDLINE abstracts.

257 nformation from traditional surveillance and big data sources, which seems the most promising option

258 icology using cheminformatics approaches and big data sources.

259 nd then describe three studies drawing from "big data" sources to assess liberal-conservative differe

260 stinguished from low-risk participants using big-data spatial navigation benchmarks.

261 sufficient rigor, particularly when applying big data statistics.

262 emergence of surveillance records mined from big data such as health-related online queries and socia

263 he topic by discussing potential pitfalls to Big Data such as the lack of diversity in Big Data resea

264 omputational resource limits when working on big data such as whole-genome expression data.

265 A new generation of big data surveillance systems is needed to achieve rapid

266 Herein, inspired by the big data survey, we develop a golden Seebeck coefficient

267 These results highlight the potential for "big data" techniques to provide new insights into moveme

268 rovide integrated information services using big data technology for microbial resource centers and m

269 Advances in molecular imaging and big data technology, including in multiomics and network

270 for comparative effectiveness research using big data that makes the target trial explicit.

271 c health records (eHRs) are a source of such big data that provide a multitude of health related clin

272 alytical approaches, developed to cope with "big data", that require no 'a priori' assumptions about

273 Using modern computational approaches for big data, the denser point clouds can efficiently be pro

274 asy-to-use steps for scientists working with big data, the Odyssey pipeline was developed.

275 Taking into consideration the explosion of "big data," the advent of more sophisticated data collect

276 Under the banner of "big data," the detection and classification of structure

277 In the era of big data, there are increasing interests on clustering v

278 key goal of cancer systems biology is to use big data to elucidate the molecular networks by which ca

279 significant potential for the application of Big Data to healthcare, but there are still some impedim

280 ng the heterogeneity of these transcriptomic Big Data to identify defective biological processes rema

281 Realizing the potential for big data to improve critical care patient outcomes will

282 We provide an analysis of the Big Data to Knowledge (BD2K) training program strengths

283 xity of biological data sets has led to the 'Big Data to Knowledge' challenge.

284 DARTS leverages public RNA-seq big data to provide a knowledge base of splicing regulat

285 The current study uses big data to study prosocial behavior by analyzing donati

286 This study used "big data" to assess whether RM is associated with improv

287 The potential of "Big Data" to estimate socioeconomic factors in Africa ha

288 otential synergy between deep learning from "big data" (to create semantic features for individual wo

289 d opportunities and challenges that parallel big data transformations in other fields and has rapidly

290 rties of the language of life from unlabeled big data (UniRef50).

291 Moreover, the analysis creates the original big data unveiling three general features of BRaf signal

292 graming can now work hands on with their own big data using this easy-to-use pipeline.

293 In complex models and big data we anticipate that saddle-transitions will be e

294 In this era of big data, we are amassing large amounts of information t

295 hould be more frequently adopted to leverage big data while minimizing bias in hospital performance a

296 Big Data will be an integral part of the next generation

297 fferent stages of the disease, the resulting big data will be assembled into a single innovative data

298 itate real-time inference and learning from "big data" with high efficiency and speed in intelligent

299 Envisioning a future role for Big Data within the digital healthcare context means bal

300 ection errors and naturally integrates in a "big data" workflow used for large-scale analyses.