ライフサイエンスコーパス: text mining

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

2 ions of biomedical information retrieval and text mining.

3 data are created with support from automated text mining.

4 rtant problem within the field of biomedical text mining.

5 ial for developing and evaluating biomedical text mining.

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

7 tains a wealth of information for meaningful text mining.

8 n source software tool for molecular biology text mining.

9 rtant information sources for bioinformatics text mining.

10 c-means clustering algorithm for biomedical text mining.

11 owledge base about problematic antibodies by text mining.

12 rom scientific literature by using automated text mining.

13 te the research on document-level biomedical text mining.

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

15 nd rich features largely based on biomedical text mining.

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

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

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

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

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

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

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

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

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

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

26 are, using natural language processing-based text mining algorithms.

27 Text-mining algorithms that were previously validated to

28 Text-mining algorithms were used to derive cumulative do

29 ibed within CSS and conduct bibliometric and text mining analyses to characterize articles that refer

30 We applied data-driven text mining analyses to the "Methods" and "Results" sect

31 emonstrated by a drug interaction extraction text mining analysis.

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

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

34 In conclusion, using a focused automated text mining and curation approach with experimental and

35 ith the mammalian circulating system through text mining and database fusion.

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

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

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

39 These resources can help streamline text mining and meta-science projects and make text mini

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

41 This study, using advanced text mining and multiple deep learning algorithms, utili

42 In this cross-sectional study, text mining and natural language processing techniques a

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

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

45 We then employed text mining and PubChem crowdsourcing to associate phras

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

47 textual data, combining methods and tools of text mining and social network analysis.

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

49 ARTICIPANTS: This cross-sectional study used text mining and the linking of data from electronic heal

50 In this article, we employ text mining and word cloud analysis techniques to addres

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

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

53 Manually annotated data is key to developing text-mining and information-extraction algorithms.

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

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

56 enhance their comprehension of lncRNAs from text mining, and also saving their time.

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

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

59 existing resources for literature searches, text mining, and large-scale prediction of physicochemic

60 This article introduces the concept of Agile text mining, and presents the PubAnnotation ecosystem as

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

62 Many text mining applications depend on accurate named entity

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

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

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

66 l literature is critical for many downstream text mining applications in both research and real-world

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

68 sources that have been introduced to support text mining applications over the COVID-19 literature; s

69 nguistic structure are relevant for building text mining applications.

70 c classification and for building biomedical text mining applications.

71 body parts are crucial for building medical text mining applications.

72 stream bioinformatics studies and biomedical text-mining applications.

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

74 s codes with deeper clinical data, we used a text mining approach to extract symptoms from free text

75 re curation supported by a focused automated text mining approach to identify genes involved in 5 KEs

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

77 A novel self-supervised text mining approach, leveraging bidirectional encoder r

78 Utilizing the text mining approach, the proposed method leverages the

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

80 Here we present an automated text-mining approach as a form of meta-analysis to exami

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

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

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

84 We have also used a text-mining approach to search for microRNA gene names i

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

86 ubMed and extraction of words with different text mining approaches, and (ii) interactive analysis an

87 So, we have explored the effectiveness of text-mining approaches and developed optimizations to fi

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

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

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

91 m biomedical literature, existing biomedical text-mining approaches typically formulate the problem a

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

93 score of 0.67, which we increased to 0.88 by text-mining article excerpts surrounding the mentioning

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

95 ssification systems based on high-throughput text-mining as well as on a hierarchical clustering of t

96 LAITOR4HPC can be used for an efficient text mining based construction of biological networks de

97 rane accidents were firstly identified using text mining based on 203 accidents that occurred from 20

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

99 m classification of scientific literature by text mining-based classification of article abstracts.

100 a and information collection by implementing text mining-based classification of primary biomedical l

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

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

103 esentations from transformers for biomedical text mining (BioBERT) for span detection along with the

104 a, chromatograms, or images in styles new to text mining but familiar to analytical chemistry.

105 perspectives: it allows the reformulation of text mining by biomedical researchers from the task of a

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

107 Text mining can yield valuable insights from unstructure

108 , we tackled this challenge by deploying the text-mining capacities of large language models to proce

109 biomedical researchers, biocurators, and the text mining community working on biomedical relationship

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

111 actors and crane accident chain by combining text mining, complex network and integrated interpretive

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

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

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

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

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

117 We corroborated our findings by literature text-mining, expert validation, and recapitulation in mo

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

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

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

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

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

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

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

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

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

127 Text mining for translational bioinformatics is a new fi

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

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

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

131 Interest in text mining full-text biomedical research articles is gr

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

133 This highlights the value of text-mining hospital letters to replace the national abs

134 We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify m

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

136 Text mining identified code sharing in 98 (2%) articles,

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

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

139 Text mining in the biomedical domain aims at helping res

140 Text mining in the biomedical field has received much at

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

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

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

144 Biomedical text mining is a technique that extracts essential infor

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

146 Text mining is increasingly used in the biomedical domai

147 massive growth of the scientific literature, text mining is increasingly used to extract biological p

148 Text mining is increasingly used to manage the accelerat

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

150 Text mining is one promising way of extracting informati

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

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

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

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

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

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

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

158 environments in the past 35 years using the text mining method, which allows us to discover patterns

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

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

161 We present a new corpus and Text Mining methodology that can accurately identify and

162 In this study, we developed tailored text mining methods and analyzed 29 188 papers published

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

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

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

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

167 Text-mining mutation information from the literature bec

168 eering to guide ChatGPT in the automation of text mining of metal-organic framework (MOF) synthesis c

169 cinous and non-mucinous samples, integrating text mining of pathology reports, gene expression, methy

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

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

172 Technology is needed to perform large-scale text mining of research papers to extract the reported a

173 Previously, we showed that text mining of residues in freely available PubMed abstr

174 Text mining of sequence annotations allows position spec

175 xt mining and meta-science projects and make text mining of the biomedical literature more accessible

176 We show that the text mining of the clinical notes was able to complement

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

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

179 ), by incorporating past medical history and text-mining of electronic medical records.

180 dels presents an opportunity to speed up the text-mining of protein activities for biocuration.

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

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

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

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

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

186 signed for the purpose of active learning in text mining, our method achieves significant improvement

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

188 hus motivated to develop Autism_genepheno, a text mining pipeline to identify sentence-level mentions

189 ds In this retrospective study, a rule-based text mining pipeline was developed to extract descriptor

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

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

192 Conventional text mining pipelines provide tools to automatically ext

193 This task can be considered a text mining problem that requires reading a lot of acade

194 Lipid Mini-On uses a text-mining process to bin individual lipid names into m

195 w implementing three different processes for text mining, programmed by ChatGPT itself.

196 Text mining provides the necessary means to retrieve the

197 ty journals in infectious diseases using the text-mining R package, rtransparent.

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

199 ortant yet highly complex task in biomedical text mining research, as gene names can be highly ambigu

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

201 with other normalization tasks in biomedical text mining research.

202 ext remains a challenging task in biomedical text mining research.

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

204 Text mining researchers and others may download and use

205 Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall,

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

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

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

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

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

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

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

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

214 es PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles).

215 t annotated rare disease corpus from the PMC Text Mining subset.

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

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

218 aunched in 2008 to provide an ontology-based text mining system for early disease detection from open

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

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

221 ms, is based on an extension of our in-house text-mining system, SciMiner.

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

223 Text mining systems aim at knowledge discovery from text

224 automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants

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

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

227 d text annotations specifically prepared for text mining systems.

228 itate the development of advanced biomedical text mining systems.

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

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

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

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

233 ing as a necessary foundation for downstream text mining tasks and applications such as information e

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

235 shows SOTA performance on several biomedical text mining tasks when compared to existing publicly ava

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

237 art (SOTA) performance on various biomedical text mining tasks.

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

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

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

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

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

243 Automated text mining techniques for searching, reading and summar

244 modeling is one of the popular methods among text mining techniques used to discover hidden semantic

245 ur objective was to illustrate how combining text mining techniques with statistical methodologies ca

246 Text mining techniques, which involve the processes of i

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

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

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

250 Text mining technologies have been shown to reduce the l

251 d a new annotation interface that integrates text mining technologies.

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

253 ation is a fundamental problem in biomedical text mining that aims to generate ideas that are new, in

254 ze promising results and open challenges for text mining the COVID-19 literature.

255 Text mining the electronic health records of 14,017 pati

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

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

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

259 Text mining (TM) is efficient in extracting information

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

261 future paths of AI in healthcare by applying text mining to collect scientific papers and patent info

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

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

264 ere is a considerable discrepancy when using text mining to extract bio-entities related to eczema or

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

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

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

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

269 in November 2015 using machine learning and text mining to reduce the screening for inclusion worklo

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

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

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

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

274 an HPC-oriented version of PESCADOR's native text mining tool, renamed to LAITOR4HPC, aiming to acces

275 We developed ViMRT, a text-mining tool and search engine for automated virus m

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

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

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

279 Text mining tools configure an essential approach to bui

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

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

282 Most currently available text mining tools share two characteristics that make th

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

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

285 online web server based on LAITOR and NLProt text mining tools, which retrieves protein-protein co-oc

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

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

288 Previous studies have shown that automated text-mining tools are becoming increasingly important fo

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

290 Text-mining tools have rapidly matured: although not per

291 We used text-mining tools to extract biological entities associa

292 most common cases that remain difficult for text-mining tools.

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

294 Furthermore, with the data set built by text mining, we constructed a machine-learning model wit

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

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

297 Here, we combined text mining with a systematic review and formed a catalo

298 lenge, our study demonstrates that combining text mining with language model-based processing can gen

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

300 l mismatches between the assumptions of much text mining work and the preferences of potential end-us