ライフサイエンスコーパス: big data

1 ies for managing, organising, and analysing "big data".

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

3 cy and the evolving scientific enterprise of big data.

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

5 ers and hierarchical statistical analysis of big data.

6 dth, and, above all, the predictive power of big data.

7 in making insights about brain function from big data.

8 t behavioral scientists could use to analyze big data.

9 t molecular data correlation calculation for Big Data.

10 o directly extract functional gene sets from big data.

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

12 powerful strategy in general for mining such big data.

13 approaches particularly suited to leveraging big data.

14 ponse element reporter gene assay models and big data.

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

16 iscuss recent developments related to use of big data.

17 lp researchers navigate and understand these big data.

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

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

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

21 Times column, David Brooks discussed how the big-data agenda lacks a coherent framework of social the

22 Big data analyses reveal that the set of optimal influen

23 a software framework intended to facilitate big data analysis and reduce the time to scientific insi

24 In this contemporary, cross-sectional, big data analysis of US adults who underwent advanced li

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

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

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

28 tary we argue that researchers interested in big data and collective behavior, including the way huma

29 allenge becomes more important in the era of big data and complex statistical modeling.

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

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

32 In this new era of big data and small effects, a recalibration of views abo

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

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

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

36 sharing and data mining, in this new era of big data, and opportunities new sensors that measure a v

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

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

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

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

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

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

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

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

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

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

47 the harnessing of information technology and big data are some areas where important advances were ma

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

49 Rather than treating big data as a record of, and also a predictor of, where

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

51 enomics with three other major generators of Big Data: astronomy, YouTube, and Twitter.

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

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

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

55 As a result, there is much excitement about Big Data, but there is also a discussion on just what Bi

56 the social scientific contextualization of "big data" by proposing a four-quadrant model.

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

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

59 Big Data can point toward unexpected correlations, and t

60 s (GFT) has generated significant hope that "big data" can be an effective tool for estimating diseas

61 Here, we test whether this source of "big data" can be used to approximate visitation rates.

62 Social media 'big data' can provide valuable insights about people's b

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

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

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

66 s biology has been an important part of the "big data" challenge.

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

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

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

70 but there is also a discussion on just what Big Data contributes to solving a biological problem.

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

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

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

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

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

76 Big Data drawing on Medicare claims and IRIS Registry re

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

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

79 would be more useful, and the problem in the big-data era is deciding when it is better to rely on de

80 f overwhelming amounts of information in our big-data era society is growing.

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

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

83 treme storage prerequisites for research in "big data" fields.

84 ata convert numerous small data sources into big data for improved knowledge about neuroscience-relat

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

86 k discusses the importance of open access to big data for translating knowledge of cancer heterogenei

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

88 ying, processing, and visualizing genomics' "Big Data" from sources like The Cancer Genome Atlas (TCG

89 le computationally intensive analysis of the big data generated.

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

91 Big data has transformed fields such as physics and geno

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

93 lopments in technology continue, the era of "big data" has arrived, the general public is more and mo

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

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

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

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

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

99 an the brain teaches important lessons about big data in biology.

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

101 es many important questions about the use of big data in health care.

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

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

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

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

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

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

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

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

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

111 provide specific clues about how biomedical big data initiatives should be exposed as public resourc

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

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

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

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

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

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

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

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

120 The textual, big-data literature misses Bentley et al.'s message on d

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

122 elds that address the individuals underlying big-data media streams.

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

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

125 Using "big data" mining techniques, this research examines real

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

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

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

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

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

131 g desirable scaling for future transcriptome Big Data platforms.

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

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

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

135 cedented growth of sequencing information in big data projects.

136 e reliability and the biases inherent to the big data "proxies" of social life are still open.

137 stasis could provide a useful framework for 'big data' quantitative biology, particularly of stress-s

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

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

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

141 Big data repositories, including those for molecular, cl

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

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

144 e place, the BOB model is interested in what big data reveal about how decisions are being made, how

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

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

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

148 ecular Biology has been at the heart of the 'big data' revolution from its very beginning, and the ne

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

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

151 ethods and training mechanisms to integrate "big data" science into the practice of epidemiology; 3)

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

153 The ability to track patient outcomes in Big Data sets provides opportunities for further researc

154 Neuroscience is set to collect its own big data sets, but to exploit its full potential, there

155 ledge across highly heterogeneous biomedical big data sets.

156 y, behavioral scientists now have access to "big data" sets - those from Twitter and Facebook, for ex

157 e shown a major rise in network analysis of "big data" sets in the social sciences, revealing non-obv

158 Using three prototypical "Big Data" sets, we investigate the scaling behaviors ass

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

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

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

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

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

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

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

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

167 icology using cheminformatics approaches and big data sources.

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

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

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

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

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

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

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

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

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

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

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

179 ry databases will help organize some of this Big Data thereby allowing users better navigate, search

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

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

182 s, high-throughput technologies have brought big data to the life sciences.

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

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

185 arge-scale individual-based trajectory data (big data) to better understand environmental implication

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

187 However, modern connectomics produces 'big data', unprecedented quantities of digital informati

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

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

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

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

192 but posit that a successful attempt at using big data will include more sensitive measurements, more

193 At the same time, big data, with its potential to drive rapid understandin

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