ライフサイエンスコーパス: text mining

1 rom scientific literature by using automated text mining.

2 ial for developing and evaluating biomedical text mining.

3 s source code or web services for biomedical text mining.

4 n source software tool for molecular biology text mining.

5 rtant information sources for bioinformatics text mining.

6 ecognition (NER) is a key task in biomedical text mining.

7 nd rich features largely based on biomedical text mining.

8 pportunity of using "big data" in biomedical text mining.

9 le to a wide range of problems in biomedical text mining.

10 ions of biomedical information retrieval and text mining.

11 rtant problem within the field of biomedical text mining.

12 ous cells and animal models recorded through text-mining.

13 It remains unclear, however, whether text mining actually benefits from documented synonymy a

14 RCancer, is updated regularly by running the text mining algorithm against PubMed.

15 This work presents our text mining algorithm and demonstrates its use to uncove

16 nd primary GPCR resources using an automated text mining algorithm combined with manual validation, w

17 More generally, our text mining algorithm may be used to uncover information

18 A new text-mining algorithm was proposed to collect GPCR-ligan

19 ces on such a large scale, however, requires text mining algorithms that can recognize when different

20 ing equipped with multiple challenge-winning text mining algorithms to ensure the quality of its auto

21 Text-mining algorithms that were previously validated to

22 emonstrated by a drug interaction extraction text mining analysis.

23 Automated text-mining analysis was performed to extract data on P

24 we propose a novel approach that integrates text mining and automated reasoning to derive DDIs.

25 ion extraction as an enabling technology for text mining and given also the ready adaptability of sys

26 ucial for many important tasks in biomedical text mining and information retrieval.

27 rate semantic classification is valuable for text mining and knowledge-based tasks that perform infer

28 , we employed network pharmacology including text mining and molecular docking to identify the potent

29 We introduce an automatic approach based on text mining and network analysis to predict gene-disease

30 antage of principles in image understanding, text mining and optical character recognition (OCR) to r

31 e were predicted by serum pharmacochemistry, text mining and similarity match.

32 e to extract microRNA-cancer associations by text mining and store them in a database called miRCance

33 ent additions include integration of various text-mining and biodatabase plugins, demonstrating the s

34 Using automated text-mining and expert (human) curation we have systemat

35 e constructed an AAgAtlas database 1.0 using text-mining and manual curation.

36 dence with results from logistic regression, text-mining and molecular-level measures for comorbiditi

37 of manual literature curation, computational text mining, and genome analysis.

38 ioRAT is a Biological Research Assistant for Text mining, and incorporates a document search ability

39 open-source data sets through web crawling, text mining, and social media analytics, primarily in th

40 Many text mining applications depend on accurate named entity

41 A variety of methods for evaluating text mining applications exist, including corpora, struc

42 The integration with FDA drug labels enables text mining applications for drug adverse events and cli

43 nd their subsequent annotation together with text mining applications for linking chemistry with biol

44 well as can be utilised to develop efficient text mining applications on cell types and cell lines.

45 c classification and for building biomedical text mining applications.

46 nguistic structure are relevant for building text mining applications.

47 related discovery and innovation (LRDI) is a text mining approach for bridging unconnected discipline

48 In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI

49 sis of functional annotations using a simple text mining approach.

50 Here, we report tmVar, a text-mining approach based on conditional random field (

51 Here, we report the results of a novel text-mining approach that extracts DNA sequences from bi

52 We apply a semantic text-mining approach to identify the phenotypes (signs a

53 n literature, which urges the development of text mining approaches to facilitate the process by auto

54 in interaction extraction) relatively simple text-mining approaches can prove surprisingly effective.

55 Existing text-mining approaches focus on finding gene names or id

56 navigate the literature and has incorporated text-mining approaches to semantically enrich content an

57 to expedite curation through semi-automated text-mining approaches, and to enhance curation quality

58 uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (

59 ndom forest based retention time prediction, text-mining based false positive removal/true positive r

60 s and then turns them into queries for three text mining-based MEDLINE literature search systems.

61 dation and disease association using a novel text-mining-based machine learning approach.

62 otential to significantly advance biomedical text mining by providing a high-quality gold standard fo

63 reserving two sets of 15 articles for future text-mining competitions (after which these too will be

64 lable, hereby stimulating the application of text mining data in future plant biology studies.

65 tion between proteins and diseases, based on text mining data processed from scientific literature.

66 Previous research in the biomedical text-mining domain has historically been limited to titl

67 ata Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations

68 e a computational-experimental approach with text mining-enhanced quantitative proteomics.

69 Applications of text mining fall both into the category of T1 translatio

70 stical filtering of GWAS results followed by text-mining filtering revealed relationships between ADG

71 n statistical filtering of GWAS results, and text-mining filtering using Gene Relationships Across Im

72 We conclude that the deployment of text mining for document abstraction or rich search and

73 s should encourage much wider exploration of text mining for implicit information among the general s

74 ering (BSQA) system that performs integrated text mining for insect biology, covering diverse aspects

75 f drug discovery.These opportunities include text mining for new drug leads, modeling molecular pathw

76 These opportunities include text mining for new drug leads, modeling molecular pathw

77 tudy, we assess the potential of large-scale text mining for plant biology research in general and fo

78 Text mining for translational bioinformatics is a new fi

79 een clinical terms is an important aspect of text mining from electronic health records, which are in

80 Research into event-based text mining from the biomedical literature has been grow

81 rches for the community, we conducted manual text-mining from published literature and built a databa

82 Automated text mining has been widely used in recreating protein i

83 Text mining identified 4,572,043 P values in 1,608,736 M

84 The theme of the workshop was 'Roles for text mining in biomedical knowledge discovery and transl

85 e cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biologica

86 Text mining in the biomedical domain aims at helping res

87 Furthermore, we combine text mining information with both protein-protein and re

88 ponent that can be used in the interoperable text mining infrastructure, U-Compare.

89 ectious diseases through in-depth literature text-mining, integrated outbreak metadata, outbreak surv

90 The text mining is based on 75 rules we have constructed, wh

91 Text mining is increasingly used in the biomedical domai

92 Text mining is increasingly used to manage the accelerat

93 One challenge in text mining is linking ambiguous word forms to unambiguo

94 Text mining is one promising way of extracting informati

95 One important task in biomedical text mining is relation extraction, which aims to identi

96 Research in biomedical text mining is starting to produce technology which can

97 strate that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted

98 g such information have been proposed in the text mining literature.

99 Text mining may provide useful tools to assist in the cu

100 automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniqu

101 We suggest a text mining method for efficient corpus-based term acqui

102 s of Cancer taxonomy and developed automatic text mining methodology and a tool (CHAT) capable of ret

103 However, only recently has advanced text mining methodology been implemented with sufficient

104 d in Vitro in biomedical literature by using text mining methods and present our results.

105 ionally mine the literature for such events, text mining methods that can detect, extract and annotat

106 of biomedical networks and models, aided by text mining methods that provide evidence from literatur

107 s, this result has significance for clinical text mining more generally, though further work to confi

108 Text-mining mutation information from the literature bec

109 corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug inte

110 Pub includes 2767 fusion genes obtained from text mining of PubMed abstracts.

111 Text mining of sequence annotations allows position spec

112 , benchmarking alignment tools and continued text mining of the extensive literature on transcription

113 Through the text mining of the publication record of multiple diseas

114 tive signals across genomes, (iii) automated text-mining of the scientific literature and (iv) comput

115 KB/Swiss-Prot format, which would facilitate text-mining of UniProtKB/Swiss-Prot.

116 ms a series of advanced image processing and text mining operations to comprehensively extract the se

117 or for evidence gathering methods involving text mining or data mining.

118 ed to evaluate GO predictions generated from text mining or protein interaction experiments.

119 Our work demonstrates the usefulness of a text mining pipeline in facilitating complex research ta

120 y high precision and high-throughput of this text-mining pipeline makes this activity possible both a

121 We used a sophisticated text-mining pipeline to extract 1.15 million unique whol

122 Text mining provides the necessary means to retrieve the

123 Increasingly biological text mining research is focusing on the extraction of co

124 s in text is an important task in biomedical text mining research, facilitating for instance the iden

125 with other normalization tasks in biomedical text mining research.

126 Text-mining research in the biomedical domain has been m

127 Text mining researchers and others may download and use

128 NSC), analysis of annotations and literature text mining, reveals previously unappreciated tumor subc

129 There are two basic approaches to text mining: rule-based, also known as knowledge-based;

130 ccurrence, enzyme/disease relationships from text mining, sequences and 3D structures from other data

131 report here our recently developed web-based text mining services for biomedical concept recognition

132 terature-database relationships are found by text mining, since these relationships are also not pres

133 e all translational bioinformatics software, text mining software for translational bioinformatics ca

134 Unlike most text-mining software tools, our web services integrate s

135 could serve as a gold standard to benchmark text mining solutions.

136 intensive, the development of semi-automated text mining support is hindered by unavailability of tra

137 ology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and P

138 In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations,

139 We developed and evaluated a text mining system, MutD, which extracts protein mutatio

140 om the biomedical literature using the eGIFT text-mining system.

141 Text mining systems aim at knowledge discovery from text

142 Multiple text mining systems have been developed, but most of the

143 To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA

144 itate the development of advanced biomedical text mining systems.

145 d text annotations specifically prepared for text mining systems.

146 arly of gene symbols, is a big challenge for text-mining systems in the biomedical domain.

147 d as a component to be integrated with other text-mining systems, as a framework to add user-specific

148 ne GUI application, or integrated into other text-mining systems.

149 identification is an important step in many text mining tasks aiming to extract useful information f

150 nique that can support a range of Biomedical Text Mining tasks and can help readers of biomedical lit

151 ing results and proved useful for biomedical text mining tasks.

152 nalysis of large batches of data, performing text-mining tasks and the casual or systematic evaluatio

153 into these categories can benefit many other text-mining tasks.

154 We demonstrated that DTM, a text mining technique, can be a powerful computational a

155 Information structure (IS) analysis is a text mining technique, which classifies text in biomedic

156 has made it impossible to determine whether text mining techniques are sufficiently mature to be use

157 Text mining techniques, which involve the processes of i

158 We applied text-mining techniques to identify 27 individual studies

159 We have applied computational text-mining techniques to parse and map mutagenesis and

160 The development and application of text mining technologies has been proposed as a way of d

161 Text mining technologies have been shown to reduce the l

162 ble by manual curation or sophisticated text text-mining technology to extract the relevant informati

163 Recently, in the field of biomedical text mining, the development and enhancement of event-ba

164 Hence, there is much interest in text mining, the use of computational tools to enhance t

165 Most current text mining (TM) approaches to extract information about

166 Text-mining (TM) solutions are developing into efficient

167 These results demonstrate the ability of text mining to contribute to the ongoing debate about th

168 tomics datasets of human asthma, followed by text mining to evaluate functional marker relevance of d

169 In this study, we use text mining to extract information from the descriptions

170 asets overdue for public release by applying text mining to identify dataset references in published

171 , describing our first steps into the use of text mining to identify protein-related entities, the la

172 omputational method that applies statistical text mining to PubMed abstracts, to score these 179 loci

173 -developed areas of research, we applied the text mining to structural modeling of protein-protein co

174 Here we use text mining to study 15,311 research papers in which mic

175 We describe CRAB - a fully integrated text mining tool designed to support chemical health ris

176 or time- and cost-effective development of a text mining tool for expansion of controlled vocabularie

177 address this issue we developed OncoScore, a text-mining tool that ranks genes according to their ass

178 With the assistance of the PubTator text-mining tool, we tagged more than 10 000 articles to

179 t quality databases require manual curation, text mining tools can facilitate the curation process, i

180 tics corpus, it will be difficult to develop text mining tools for pharmacokinetics data collection f

181 A number of freely available text mining tools have been put together to extract high

182 ces for the rapid development of large-scale text mining tools targeting complex biological informati

183 However, to date there are no available text mining tools that offer high-accuracy performance f

184 g as useful tools for integrating biomedical text mining tools.

185 ng need to assist biocuration with automated text mining tools.

186 Emerging text-mining tools can help by identifying topics and dis

187 We conducted full-scale text mining using miRTex to process all the Medline abst

188 ntity recognition is critical for biomedical text mining, where it is not unusual to find entities la

189 y emerging computational approaches based on text mining which offer a great opportunity to organize

190 eatures of semantic database integration and text mining with methods for graph-based analysis.