ライフサイエンスコーパス: data mining

1 ss multiple disciplines from ecology to text data mining.

2 discoverable without prior hypotheses using data mining.

3 tool to structure large textual corpora for data mining.

4 sformation prediction and mass-spectrometric data mining.

5 E signals selected through pharmacovigilance data mining.

6 ny new safety findings in empirical Bayesian data mining.

7 relevant for routing problems, inference and data mining.

8 f vanadate-phosphatase protein structures by data mining.

9 in vitro and/or in vivo assays; and clinical data mining.

10 y published approaches from graph theory and data mining.

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

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

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

14 e gathering methods involving text mining or data mining.

15 iologic processes and pathways identified by data mining.

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

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

18 cofactors of a given HMR, based on ChIP-seq data mining.

19 eractive manner, to facilitate faster visual data mining.

20 ming, making it challenging to use for image data mining.

21 ure is available via the PubMed database for data mining.

22 becomes increasingly important in post-GWAS data mining.

23 With the aid of database for data mining, 1380 unique N-glycopeptides were identified

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

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

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

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

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

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

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

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

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

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

34 s further evaluated based on cheminformatics/data mining analyses and activity against evolutionarily

35 it possible to run efficiently large genomic data mining analyses.

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

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

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

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

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

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 cal reaction datasets using a combination of data mining and experiments.

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 Using data mining and human breast cancer tissue microarrays,

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

53 ass web application designed to allow visual data mining and hypothesis testing from the multidimensi

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 In this paper, data mining and integration is leveraged to inspect targ

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

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

60 Coupling data mining and laboratory experiments is an efficient m

61 The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which us

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

63 Data mining and multivariate statistics were employed to

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

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

66 of transcriptomic regulation for additional data mining and pathway analysis of the process of MSC c

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

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

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

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

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

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

73 Currently, data mining and statistical approaches are confined to i

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

75 specific HML-2 elements using both in silico data mining and targeted deep-sequencing approaches.

76 rther supported by the results from the TCGA data mining and validated by immunohistochemical stainin

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

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

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

80 ow how the brain engages in such real-world "data mining" and how implicit inferences emerge.

81 e advent of high-throughput data generation, data mining, and advanced computational modeling has thr

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

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

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

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

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

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

88 s it is done in speech recognition and other data mining applications.

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

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

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

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

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

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

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

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

97 f particular relevance because our extensive data-mining approach suggests the absence of naturally p

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 d from maps derived from satellite images; a data-mining approach was used to select variables.

101 Data mining approaches have been increasingly applied to

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

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

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

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

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

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

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

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

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

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

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

113 vides a novel insight into time-series ACRHP data mining based on time-series ANLI for capital city s

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

115 wsing (JBrowse), genome annotation (Apollo), data mining (BovineMine) and sequence database searching

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

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

118 nerated for each detected compound; however, data mining can be labor intensive.

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

120 High-throughput phenotyping and data mining can capture dozens or hundreds of traits fro

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

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

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

124 rt innovated data structure for new types of data mining, data reanalysis, and networked genetic anal

125 Data mining discriminated 2 distinct bacterial populatio

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

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

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

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

130 tion have transformed medical biology into a data mining field, where new data sets are routinely dis

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

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

133 n scOrange, a newly developed extension of a data mining framework that features workflow design thro

134 lar morphogenetic motifs using a time series data mining framework.

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

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

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

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

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

140 del parameters, extracted directly from dual data mining, help characterize each airline's operationa

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

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

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

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

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

146 Data mining indicates that most human CCP-containing fac

147 It is proffered here that hypothesis-driven, data-mining-inspired, and "allochthonous" knowledge acqu

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

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

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

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

152 However, efficient data mining is challenging for experimental biologists w

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

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

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

156 e objective of this study was to use a novel data mining method that can simultaneously evaluate thou

157 features in simulated data, while the Logic Data Mining method, MALA, falls short.

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

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

160 Data mining methods are routinely applied to such analys

161 Data mining methods in bioinformatics and comparative ge

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

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

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

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

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

167 venous thromboembolic events was noted with data mining methods.

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

169 egative matrix factorization (NMF) and logic data mining MicroArray Logic Analyzer (MALA), by applyin

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

171 Here we use three complimentary data mining modalities alongside biochemical and cell bi

172 Data mining modeling techniques can be beneficial to con

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

174 Here we show that data mining of a computational screening library of over

175 d p75NTR and related genes through extensive data mining of a PubMed literature search including publ

176 s for both Amphetamine and Theophylline with data mining of available literature.

177 , and there is need for a tool to facilitate data mining of glycan array data.

178 Data mining of human adult bulk and single-cell retinal

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

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

181 Data mining of our RNA-Seq analysis of CD14(+)monocytes

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

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

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

185 Data mining of RNA-Seq from the Cancer Genome Atlas (TCG

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

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

188 Data mining of the brain transcriptome in Parkinson dise

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

190 Data mining of the present multi-omics analysis identifi

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

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

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

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

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

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

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

198 Data mining on the basis of molecular function revealed

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

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

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

202 Data mining, particularly, random forests are useful in

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

204 on-making classifier (J48) is applied over a data-mining platform (Weka) to measure accuracy and line

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

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

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

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

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

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

211 d precedent for the study of reservoirs, big data mining, predictions and subsequent outbreaks of HPA

212 The recently invented data mining procedure, Mass-Remainder Analysis (MARA), w

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

214 The data mining process of collected Raman spectra was perfo

215 A global data mining program termed the chromatic median calculat

216 A global data mining program termed the chromatic median determin

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

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

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

220 nnotated RCC GCN described herein is a novel data mining resource for the assignment of polygenic bio

221 Data mining revealed that FAF1 expression is statistical

222 Initial data mining revealed that the strongest positive correla

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

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

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

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

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

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

229 d using Independent Components Analysis, and data mining strategies developed to automatically detect

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

231 However, data mining strategy should be developed for complete ut

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

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

234 on factor binding sites, using a genome-wide data-mining strategy.

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

236 Recent data mining studies have suggested a potential associati

237 luation and classification of time series in data mining studies.

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

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

240 A cross-sectional, data-mining study was held from February through April 2

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

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

243 ustermatch to easily and efficiently perform data-mining tasks on large and highly heterogeneous data

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

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

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

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

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

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

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

251 combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR an

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

253 Data mining techniques have been applied extensively in

254 Using data mining techniques on existing microarray data, we f

255 conditions, we leverage network analysis and data mining techniques to assess, visualize, and project

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

257 New image-based 'big data' mining techniques enable the large-scale compariso

258 Here we show that design-thinking and data-mining techniques can be leveraged to optimize geno

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

260 plication of genomic data and well-developed data mining technologies can overcome these limitations

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

262 Data mining the amine-acid coupling system produced here

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

264 vailability of Electronic Health Records for data mining, the identification of relevant patterns of

265 (maize.plantbiology.msu.edu) for viewing and data-mining these resources and deployed two new views o

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

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

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

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

270 g Integrated Modeling-in vitro/vivo-Clinical Data Mining), to identify an FDA-approved drug suitable

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

272 reflect potential utility of Solr-Plant as a data mining tool for extracting and correcting plant spe

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

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

275 Using a data-mining tool, we extracted quantitative and qualitat

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

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

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

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

280 stablish Web-based single-access systems and data mining tools to make the available resources more a

281 research by providing genome annotation and data mining tools.

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

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

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

285 Through data mining using our structural and biochemical informa

286 itinib, erlotinib, afatinib, osimertinib) by data mining using the FDA adverse event reporting system

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

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

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

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

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

292 ser-friendly online platform for interactive data mining, visualization, and download.

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

294 Empirical Bayesian data mining was used to identify disproportional reporti

295 Through data mining, we also identified astrocyte pathways in Hu

296 Using text data mining, we assessed general public attention in Fra

297 e, we propose a novel approach that combines data mining with theoretical models of disease dynamics.

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

299 Therefore, we have developed a non-targeted data mining workflow to extract a higher number of known

300 ace and options for users wishing to conduct data mining workflows, and discuss our efforts to build