ライフサイエンスコーパス: data mining

1 y published approaches from graph theory and data mining.

2 (1)H NMR technique and chemometric tools for data mining.

3 by third parties, in particular for text and data mining.

4 ons of many types facilitates inquiry-driven data mining.

5 e gathering methods involving text mining or data mining.

6 iologic processes and pathways identified by data mining.

7 -wide scale and will be valuable for further data mining.

8 tp://genome.ucsc.edu for visual browsing and data mining.

9 nces in the zebrafish genome using in silico data mining.

10 s, and cancer patient biopsies supported our data mining.

11 f replicates, thus providing a challenge for data mining.

12 stomized batch-mode computation for advanced data mining.

13 ss multiple disciplines from ecology to text data mining.

14 discoverable without prior hypotheses using data mining.

15 tool to structure large textual corpora for data mining.

16 E signals selected through pharmacovigilance data mining.

17 ny new safety findings in empirical Bayesian data mining.

18 relevant for routing problems, inference and data mining.

19 f vanadate-phosphatase protein structures by data mining.

20 ble on a scale and in a form best served for data-mining.

21 ficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libr

22 An integrated approach was taken involving data mining across multiple information resources includ

23 lustrate the opportunities and challenges of data mining across multiple tiers of neuroscience inform

24 lymphopoiesis, results obtained via a novel data mining algorithm (global microarray meta-analysis)

25 Consequently, a data mining algorithm was developed to sample huge numbe

26 We incorporate single-cell tracking and a data-mining algorithm into our approach to obtain RNA el

27 Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-ea

28 NAs and junction topologies predicted by our data-mining algorithm RNAJAG trained on known RNAs.

29 Optimal models came from the Weka data-mining algorithm SimpleLinearRegression, which lear

30 GC x GC-TOFMS) was used with discovery-based data mining algorithms to locate regions within the 2D c

31 s associated with thrombocytopenia by use of data mining algorithms; 1444 drugs had at least 1 report

32 Data mining an existing microarray database, we identifi

33 Here we report data-mining analyses that indicate that copies of apopto

34 Gene ontology associations and data mining analysis identified focal adhesion, adherens

35 sers to visualize data and to apply advanced data mining analysis methods to explore the data and dra

36 MT1-MMP-silenced cancer cells and a further data mining analysis of the preexisting expression datab

37 PM features new web-based tools for data-mining analysis, visualization tools and enhanced c

38 due to the fact that current approaches for data mining and analysis of IR absorption spectra have n

39 of species from biological samples, enabling data mining and automating lipid identification and exte

40 rganism community which increasingly rely on data mining and computational approaches that require ga

41 must solve MCE on instances deeply seeded in data mining and computational biology, where high-throug

42 In this article, we adapt advanced data mining and computer vision techniques to address th

43 l character recognition, in conjunction with data mining and data integration methods.

44 lectronic-structure methods with intelligent data mining and database construction, and exploiting th

45 CGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions.

46 e online database to enable multidimensional data mining and dissemination.

47 Visual browsing, data mining and download of raw and processed data files

48 Computational data mining and experimental investigation focused inter

49 ombined with synthetic promoter analysis for data mining and functional screening in plant-pathogen i

50 k in the major organs of the mouse, allowing data mining and generating knowledge to elucidate the ro

51 friendly EuSplice web interface has powerful data mining and graphics capabilities for inter-genomic

52 Using data mining and human breast cancer tissue microarrays,

53 uite of analysis and visualization tools for data mining and hypothesis generation, personal workbenc

54 ncipal Components Analysis was also used for data mining and in virgin mice, greater changes in activ

55 In silico data mining and in vitro validation identified oncostati

56 ctionally regulate FKBP5 Following in silico data mining and initial target expression validation, mi

57 art is now available allowing more automated data mining and integration with other biological databa

58 ethical issues regarding consent for future data mining and intellectual property.

59 Coupling data mining and laboratory experiments is an efficient m

60 Data mining and machine learning methods were applied to

61 d related disorders by performing literature data mining and manual curation.

62 Data mining and multivariate statistics were employed to

63 We propose that data mining and network analysis utilizing public databa

64 tomato family, Solanaceae, using large-scale data mining and new sequence data to reconstruct a megap

65 Application of a classic, well-defined data mining and pattern recognition approach termed the

66 sion profiles of Arabidopsis TTL genes using data mining and promoter-reporter beta-glucuronidase fus

67 s been developed using techniques drawn from data mining and proteochemometrics.

68 e, crAssphage, was discovered by metagenomic data mining and reported to be abundant in and closely a

69 s public resources for exploratory analysis, data mining and reproducible research.

70 loped a database and web-based resources for data mining and results visualization.

71 aphy-mass spectroscopy, followed by detailed data mining and statistical analysis.

72 graphical method is introduced for compound data mining and structure-activity relationship (SAR) da

73 adult mouse brain and the ability to perform data mining and visualization of gene expression and neu

74 y and challenge in bioinformatics relates to data mining and visualization.

75 l makes this dataset a valuable resource for data-mining and augmentation.

76 S) in steady state and applied an integrated data-mining and functional genomics approach to identify

77 tandard techniques through implementation of data-mining and machine-learning strategies.

78 most have focused on the practical aspect of data mining, and few on the biological problem and the b

79 statistical elaboration of data, literature data mining, and gene ontology-based classification.

80 e-rich web applications that provide search, data mining, and genome browser functionality, and also

81 e problems arising within signal processing, data mining, and machine learning naturally give rise to

82 interaction network prediction, coexpression data mining, and phylogenetic profiling all produced inc

83 igned to protein markers derived from public data mining, and whether mass spectrometry can be utiliz

84 We developed a data mining approach analyzing natural H1N1 human isolat

85 To bypass this difficulty, we have taken a data mining approach by first collecting, through extens

86 ated by gene expression data, the underlying data mining approach can be applied to a variety of diff

87 An integrated biochemical, analytical and data mining approach demonstrates that HAs from the huma

88 FkappaB in the kidney cortex, and a targeted data mining approach identified components of the noncan

89 Our approach uses a data mining approach known as random forests, which reli

90 This data mining approach suggested that there are six PDT-li

91 artificial neural network-based integrative data mining approach to data from three cohorts of patie

92 s a modular design and an interactive visual data mining approach to enable efficient extraction of u

93 Toward this end, we developed a data mining approach to exhaustively discover all block

94 by developing a hierarchical graph sampling/data mining approach to reduce conformational space and

95 An in silico data mining approach was thus undertaken to attempt to i

96 We combined the group theory and data mining approach within the Organic Materials Databa

97 We propose a graph-based data-mining approach to efficiently and systematically i

98 We used a nonbiased data-mining approach to identify NFkappaB as the likely

99 This tool provides a flexible data-mining approach to identifying gene-gene effects th

100 Data mining approaches have been increasingly applied to

101 for realizing the predictive capabilities of data mining approaches is a curated, open-access, up-to-

102 Contrary to prior efforts, the power of data mining approaches lies in the ability to discern sy

103 nd experiences from the machine learning and data mining approaches, six common messages were extract

104 x, unstructured data sets through a range of data-mining approaches, including the incorporation of '

105 Machine learning and data mining are alternative approaches to identifying ne

106 er cancers were identified through in silico data mining as tumor types that display amplification an

107 e that allows for reproducible, user-defined data mining as well as nomination of mutation candidates

108 ls and features to facilitate navigation and data mining as well as the acquisition of new data (phen

109 riptome coverage and to facilitate effective data mining, assembly was done using different filtering

110 It incorporates data mining, automatic annotation, use of ontologies and

111 In addition, data mining based on the search for specific sequence mo

112 We introduce FpClass, a data mining-based method for proteome-wide PPI predictio

113 nt, it may be possible to automate LCI using data mining by establishing a reproducible approach for

114 but also provide a basis for more extensive data mining by providing a comprehensive list of miRNAs

115 ols for data analysis is that the process of data mining can become uncoupled from the scientific pro

116 trate that a state-of-the-art physics-guided data mining can provide an efficient pathway for knowled

117 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large

118 elegans with capabilities for compositional data mining (CDM) across diverse domains.

119 orithm was assessed against several standard data mining classifiers and further validated against Su

120 extend the pattern mining technique from the data mining community to handle the situation where fami

121 similar to the pattern mining problem in the data mining community.

122 extraction from primary literature, text and data mining, data integration, and prediction algorithms

123 focus on developing novel image processing, data mining, database and visualization techniques to ex

124 Data mining discriminated 2 distinct bacterial populatio

125 e learning algorithms, which are part of the data mining (DM) approach, may serve for transplantation

126 e sequence data, which will be available for data-mining efforts that could facilitate better source

127 use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived f

128 ists/analytical chemists, collaboration with data-mining experts is generally advised.

129 All of these can be used for reliable data mining, extending the utility of exome sequencing.

130 arch, browse, phylogenetic context and other data-mining facilities.

131 g procedures, as well as recently introduced data mining features.

132 e on Twitter makes it a promising target for data mining for ADE identification and intervention.

133 y provides new opportunities for large-scale data mining for drug discovery.

134 Data mining for functional annotations of signature gene

135 pproach making use of public genetic/genomic data mining for one of the ongoing tree of life projects

136 Based on data mining from AERS-DM, PPI use appears to be associat

137 lpha-overexpressing mice in conjunction with data mining from the Cancer Genome Atlas showed that the

138 on of direct sequencing, KIR genotyping, and data mining from the Great Ape Genome Project, we charac

139 All data sets are available for data-mining from a unified resource to support further b

140 Algorithmic data mining has the promise to eliminate these concerns,

141 rce (www.genenetwork.org) that allows custom data mining, identification of coregulated transcripts a

142 integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up appr

143 generated requires a sophisticated means of data mining in order to extract novel information that a

144 nism and confirmed consanguinity followed by data mining in the exomes of 1,348 PD-affected individua

145 le clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussio

146 include role of open access data sharing and data mining, in this new era of big data, and opportunit

147 Here, extensive data mining, including of pathogenicity factors, host re

148 Statistical data mining, including proportional reporting ratios (PR

149 Data mining indicates that most human CCP-containing fac

150 ted LCI with existing data revealed that the data mining inventory is in reasonable agreement with ex

151 activity cliffs in two and three dimensions, data mining investigations to systematically detect all

152 m the annotation of human genetic variation, data mining is a faster and cost effective approach for

153 Data mining is a suitable tool for this purpose, especia

154 One of the ways to approach microarray data mining is to increase the number of dimensions/leve

155 neralization procedure called Cross-Ontology Data Mining-Level by Level (COLL) that takes into accoun

156 We sought to determine whether data mining longitudinal physiologic data in a nonhuman

157 al systems can be accurately predicted using data-mining, machine-learning techniques.

158 We performed a variety of data-mining manipulations on the profiles and used compl

159 ch recognition, this method has been used in data mining, medicine and bioinformatics.

160 is based on using a hybrid machine-learning/data-mining method to identify patterns in the bioinform

161 zes poses a significant challenge to current data mining methodology where many of the standard metho

162 t genome information, along with appropriate data mining methodology, can be used as a starting point

163 This study implements data-mining methodology to classify and reliably reconst

164 Data mining methods are routinely applied to such analys

165 significantly higher accuracy than standard data mining methods in almost all cases.

166 Data mining methods in bioinformatics and comparative ge

167 ining with an emphasis on recent advances in data mining methods pertinent to the unique characterist

168 Efficient data mining methods to analyze these complex data sets a

169 We used empirical Bayesian data mining methods to identify disproportionate reporti

170 Data mining methods were used to identify cellular proce

171 Unlike other data mining methods, the peculiarity of KODAMA is that i

172 gical data is driving the development of new data mining methods.

173 the results were examined using a variety of data mining methods.

174 venous thromboembolic events was noted with data mining methods.

175 association strategy tests whether agnostic data-mining methods can advance knowledge alongside or e

176 Data-mining methods were used to identify cellular proce

177 an underlying structure in the genomic data, data mining might identify this and thus improve downstr

178 Data mining modeling techniques can be beneficial to con

179 However, technological advances in data mining, modeling, multigene engineering and genome

180 By data mining of a massively parallel signature sequencing

181 BioMart provides for data mining of annotations and sequences.

182 a validation of error-prone ESTs and impedes data mining of certain functional motifs, whose detectio

183 This ISICA method should be useful to better data mining of large-scale in vivo neural datasets, lead

184 Data mining of NCBI's GEO microarrays revealed strong co

185 dology combining new field measurements with data mining of previously unavailable well attributes an

186 ols and interfaces in TFGD allow intelligent data mining of recently released and continually expandi

187 Data mining of RNA-Seq experiments with mouse models of

188 ble prediction errors of GRNs hinder optimal data mining of RNA-Seq transcriptome profiles.

189 paper, we describe a method for interactive data mining of spectral features using GPU-based manipul

190 We have used data mining of the large-scale experimental results of t

191 Advanced methods for data mining of the literature, protein databases, and kn

192 of patient and control samples, followed by data mining of the molecular read-outs (e.g., mass spect

193 es in the Poaceae family were known from the data mining of the National Center for Biotechnology Inf

194 roteins in patients with MI were acquired by data mining of the PubMed and UniProt knowledgebase, and

195 The TCW allows in-depth data mining of the results, which can lead to a better u

196 Data mining of the structures shows that one face of eac

197 Very recently, a novel approach for data mining of the vast compilations of tumour NGS data

198 tomyces clavuligerus genome, will facilitate data mining of these secondary metabolites.

199 We provide a set of tools for advanced data-mining of ASAP II with Pygr (the Python Graph Datab

200 oms non-unique, which may create problems in data-mining of the PDB.

201 In this Article, we show that data-mining of these published libraries while applying

202 Data mining on 216 genes shared between GI-AGR and GI-BR

203 Data mining on the basis of molecular function revealed

204 from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for a

205 provide researchers with excellent secondary data-mining opportunities to study genomic data beyond t

206 so introduced new comparative genomics-based data mining options and report on the continued developm

207 GRCh37 assembly, enhanced visualisation and data-mining options for the Ensembl regulatory features

208 opriate for evaluating results from targeted data mining or identifying novel candidate relationships

209 Data mining, particularly, random forests are useful in

210 ve become an important step in many text and data mining pipelines.

211 a is frustrated by the lack of an integrated data mining platform or other unifying bioinformatic res

212 MGD provides a data mining platform that enables the development of tra

213 MGD provides a data-mining platform that enables the development of tra

214 The goal is to develop integrative data-mining platforms that allow the scientific communit

215 BCCTBbp was initially developed as the data-mining portal of the Breast Cancer Campaign Tissue

216 eneration data has been added to the various data-mining portals hosted, including NemaBLAST and Nema

217 providing various interfaces to the data and data-mining possibilities.

218 S has been established providing a number of data-mining possibilities.

219 inated sites were used to demonstrate that a data mining prediction model using the classification an

220 We develop a data mining procedure based on frequent itemset mining a

221 lows researchers to select preprocessing and data-mining procedures to discover differences between m

222 The data mining process of collected Raman spectra was perfo

223 A global data mining program termed the chromatic median calculat

224 A global data mining program termed the chromatic median determin

225 r treatment decisions or for high-throughput data mining research, such as Radiomics, where manual de

226 n reactions and pathways that functions as a data mining resource and electronic textbook.

227 ae, the long-standing central repository and data mining resource for Rosaceae research, has been enh

228 R), the long-standing central repository and data mining resource for Rosaceae research, has been enh

229 MiMiR, the Microarray data Mining Resource was designed to tackle some of thes

230 Data mining revealed elevated levels of Wnt pathway mRNA

231 Data mining revealed that CaLCuV triggers a pathogen res

232 Data mining revealed that FAF1 expression is statistical

233 Initial data mining revealed that the strongest positive correla

234 In a data mining sense, this work also shows a wider point th

235 , which have subsequently been used to build data mining services, predictive tools and visualization

236 dministration Adverse Event Reporting System Data Mining Set (AERS-DM).

237 roject provides users with a single one-stop data-mining solution and has great potential to become a

238 ogramming interface (API) to generate custom data mining solutions and extensions to the site.

239 peptide separation and assist with required data mining steps, such as protein identification.

240 Efficient data mining strategies are in high demand for large scal

241 of detecting subtle behavioral effects using data mining strategies.

242 mass spectral data, and the use of a robust data mining strategy generated a characteristic profile

243 s of articles is a fundamental and intuitive data mining strategy that can help investigators address

244 We employed a robust data mining strategy using new feature annotation functi

245 A reworking of a data mining strategy, in which statistical treatment of

246 We applied a comprehensive data-mining strategy to examine the repertoires of rat a

247 We used a data-mining strategy to identify highly expressed genes

248 Most of the previous data mining studies based on the NCI-60 dataset, due to

249 Recent data mining studies have suggested a potential associati

250 Data-mining studies strongly suggest that 12-HETER1 expr

251 arameter and has the potential to be used in data-mining studies to help reduce the number of crystal

252 By using advanced data mining, supervised machine learning, and network an

253 systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

254 Machine learning (ML) is an intelligent data mining technique that builds a prediction model bas

255 Random Forests (RF) are a popular data-mining technique that can accommodate a large numbe

256 While RF remains a promising data-mining technique that extends univariate methods to

257 event, and we used association analysis as a data-mining technique to identify co-occurrences of thes

258 ted and non-targeted acquisition methods and data mining techniques (e.g. mass defect, product ion, a

259 Overall, these findings demonstrate that data mining techniques (e.g., machine learning algorithm

260 Here we show that data mining techniques applied to a large database of ne

261 ic data, application of machine learning and data mining techniques has become more attractive given

262 Data mining techniques have been applied extensively in

263 Using "big data" mining techniques, this research examines real-tim

264 e analyses are provided via a broad range of data-mining techniques, including univariate and multiva

265 roimaging data will enable powerful forms of data mining that accelerate discovery and improve resear

266 ce GRNs underlying pancreas development from data mining that integrates multiple approaches, includi

267 rk presents a new strategy for combining and data mining the NCI-60 dataset and PubChem.

268 cular hyperemia" and "vomiting" exceeded the data mining threshold; >80% of these reports were nonser

269 ental tables, making downstream analysis and data mining time-consuming and difficult.

270 This opens up for the use of data mining to discover unknown drug-drug interactions i

271 ive acquisition results was performed during data mining to simplify the process and interrogate a la

272 Recently, we have introduced the data mining tool ENDEAVOUR, which performs this task aut

273 Analyzer of Bioresource Citation (ABC) is a data mining tool extracting strain related publications,

274 thology Consortium Integrative Database is a data-mining tool that includes 379 neuropathology data s

275 Coexpression was further used both as a data-mining tool to classify and/or validate genes from

276 We use discrete graphical models as a data-mining tool, searching for single- or multilocus pa

277 These include a variety of new data mining tools and summaries, estimated transcriptome

278 level pathway viewer and improved search and data mining tools facilitate searching and visualizing p

279 elopment of advanced knowledge discovery and data mining tools for across comparisons of publicly ava

280 luster analysis is one of the most important data mining tools for investigating high-throughput biol

281 eb interface to a set of cheminformatics and data mining tools that are useful for various analysis r

282 We review computational data mining tools that have been used to analyze mass cy

283 is on the utilization of accurate-mass-based data mining tools.

284 Williams 82' genomic sequence and associated data mining tools.

285 research by providing genome annotation and data mining tools.

286 base and eight complementary, web-accessible data mining tools: Onto-Express, Onto-Compare, Onto-Desi

287 ild software instruments intended to work as data-mining tools for predicting valuable properties of

288 n annotation database and nine complementary data-mining tools.

289 etrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topi

290 Through data mining using our structural and biochemical informa

291 Protein Data Bank data mining using the HippDB database indicated that (1)

292 at integrates cheminformatic algorithms with data mining utilities to enable systematic structure and

293 data integration from disparate sources and data mining via conventional query languages.

294 ization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs.

295 se and download server for visualization and data mining via the UCSC Genome Browser and companion to

296 A new data-mining warehouse, HymenopteraMine, based on the Int

297 Empirical Bayesian data mining was used to identify disproportional reporti

298 ributing gene products using Unigene cluster data mining, we found overrepresentation of expressed se

299 re we describe the steps involved in process data mining with an emphasis on recent advances in data

300 Here, a data mining workflow based on MS/MS precursor lists targ