ライフサイエンスコーパス: bioinformatics

1 cations of supervised learning approaches in bioinformatics.

2 ilarity is a central challenge in structural bioinformatics.

3 ild-type and E571K CRM1 with structure-based bioinformatics.

4 erequisite for productively doing structural bioinformatics.

5 genome alignment is a challenging problem in bioinformatics.

6 e current sequence and structural methods in bioinformatics.

7 ial intelligence (AI), signal processing and bioinformatics.

8 r biology, biochemistry, systems biology and bioinformatics.

9 and one of the most difficult challenges in bioinformatics.

10 to be published in this special issue of BMC Bioinformatics.

11 ne expression data) are ubiquitous in modern bioinformatics.

12 that can be answered when utilizing NGS and bioinformatics.

13 ds and identified KIR genotypes using custom bioinformatics.

14 en BCREval and Bismark (Krueger and Andrews, Bioinformatics 27:1571-1572, 2011), a widely used BCR ev

15 types, brain tissue regions and advances in bioinformatics algorithms, have presented an opportunity

16 In this study, using bioinformatics algorithms, we examined the chemical rela

17 Using extensive computational sequence and bioinformatics analyses and cellular localization studie

18 Our integrated bioinformatics analyses and experimental validation plat

19 Bioinformatics analyses and functional validation define

20 Various studies together with bioinformatics analyses have shown that many genes contr

21 We combined this information with bioinformatics analyses of natural variants and with exi

22 We performed bioinformatics analyses of SFRP1 expression in human can

23 Bioinformatics analyses prioritize candidate causal gene

24 Bioinformatics analyses revealed clear delineations betw

25 Bioinformatics analyses revealed the bull was a compound

26 Bioinformatics analyses revealed the presence of RgNanOx

27 e conducted to identify genetic variants and bioinformatics analyses were performed to prioritize can

28 Here, using affinity chromatography, bioinformatics analyses, NAD synthetase activity, and bi

29 ries on murine ovaries, coupled with several bioinformatics analyses, the complete dynamic genetic pr

30 Using bioinformatics analyses, we identified two distinct leuc

31 genetic, population genetics, and structural bioinformatics analyses.

32 rometry, with the support of statistical and bioinformatics analyses.

33 or protein functions, as demonstrated by our bioinformatics analyses.

34 methods we argue that both should be used in bioinformatics analyses.

35 rcoded before high-throughput sequencing and bioinformatics analyses.

36 es is considered nowadays a core part of the bioinformatics analyses.

37 Through bioinformatics analysis and dedicated experiments, we id

38 ipheral blood mononuclear cells, single-cell bioinformatics analysis and immunohistochemistry of lung

39 Bioinformatics analysis and luciferase reporter assay co

40 Preliminary functional studies, including bioinformatics analysis and TOP-/FOP-flash reporter assa

41 Finally, our bioinformatics analysis demonstrates that Apd6 and their

42 Bioinformatics analysis found that this SNP and its adja

43 RNA-Sequencing and bioinformatics analysis further identified a PDE10A-regu

44 Bioinformatics analysis identified cell differentiation

45 Unbiased bioinformatics analysis identified that inducible costim

46 oughput RNA sequencing integrated with iSyTE-bioinformatics analysis identified the molecular chapero

47 Next-generation sequencing and bioinformatics analysis in the present study demonstrate

48 We conducted a bioinformatics analysis of COVID-19 comorbidity-associat

49 Our validated bioinformatics analysis of human skin transcriptome indu

50 Bioinformatics analysis of KAT8, the gene encoding hMOF,

51 Interestingly, bioinformatics analysis of PMEL17 homologs from other ma

52 Bioinformatics analysis of RNA sequencing data identifie

53 A pan-cancer bioinformatics analysis of the 20 RGS domains with GAP a

54 Remarkably, a bioinformatics analysis of the amino acidic sequence of

55 Bioinformatics analysis of The Cancer Genome Atlas datas

56 Next, we performed a comprehensive bioinformatics analysis of the Capsicum ANK gene family

57 In addition, through bioinformatics analysis of the genes mapped to the 22q11

58 To prove it, we first performed a bioinformatics analysis of the Protein Data Bank protein

59 To sum up, this bioinformatics analysis of transcriptome may provide new

60 This notion was further supported by bioinformatics analysis of transcriptome profiles in LIN

61 In bioinformatics analysis of upstream regulator networks,

62 In the present study, we develop a bioinformatics analysis pipeline to build a predictive g

63 ughput processing of sequencing data in many bioinformatics analysis pipelines primarily due to their

64 Bioinformatics analysis predicts that miR-379 targets EI

65 Bioinformatics analysis revealed miR-34a could target 30

66 Bioinformatics analysis revealed that there were biologi

67 Interestingly, bioinformatics analysis revealed the presence of transcr

68 Furthermore, bioinformatics analysis revealed their 125 target genes,

69 Bioinformatics analysis showed that the nearby genes wer

70 Bioinformatics analysis showed that the SrrB histidine k

71 Bioinformatics analysis suggested that the KH domain pre

72 Bioinformatics analysis suggests that MHETase evolved fr

73 Bioinformatics analysis suggests that the putative C. tr

74 Bioinformatics analysis suggests the presence of the pot

75 c phage were analyzed by deep sequencing and bioinformatics analysis to identify a total of 78 300 +/

76 Here, results of a bioinformatics analysis using a sequence similarity netw

77 Bioinformatics analysis was used to refine this list and

78 Using mass spectrometry, bioinformatics analysis, coimmunoprecipitation, and pull

79 With the aid of bioinformatics analysis, from a window frame of ~2 Mb co

80 ally expressed genes and pathways in ESCC by bioinformatics analysis, potentially providing valuable

81 By gene microarray profiling with bioinformatics analysis, we found higher expression of t

82 tation is an important step for all in-depth bioinformatics analysis.

83 thways associated with ESCC by comprehensive bioinformatics analysis.

84 We apply three approaches-bioinformatics, analytical modelling and computational s

85 tion, these protocols are not in wide use in bioinformatics and are difficult to use for even technol

86 Here, using bioinformatics and biochemical analyses, we identified a

87 Using bioinformatics and biochemical methods, we identified an

88 utions and models encountered when analyzing bioinformatics and clinical data.

89 era, one of the most challenging problems in bioinformatics and computational biology is to computati

90 the Python programming language counts many Bioinformatics and Computational Biology libraries; none

91 Despite these sobering statistics, most bioinformatics and computational biology research and fu

92 COMP'19-The 2019 International Conference on Bioinformatics and Computational Biology.

93 ated by metabolomics-fluxomics combined with bioinformatics and computational modelling.

94 Pedigree files are ubiquitously used within bioinformatics and genetics studies to convey critical i

95 ble for their biosynthesis was identified by bioinformatics and insertional mutagenesis.

96 is particularly crucial for applications to bioinformatics and medical informatics, namely covariate

97 Utilizing bioinformatics and patient samples, we provide evidence

98 field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, wh

99 proaches have attracted a lot of interest in bioinformatics and related fields.

100 We used bioinformatics and reverse genetics approaches to study

101 geting users with intermediate experience in bioinformatics and statistics and using R with Bioconduc

102 SHH-MB might be identified from large-scale bioinformatics and systems biology analyses.

103 In parallel, bioinformatics and systems biology approaches including

104 dem affinity purification-mass spectrometry, bioinformatics, and biochemical approaches, we found tha

105 AMON is an open-source bioinformatics application that can be used to annotate

106 fulness of the DeepMSA in protein structural bioinformatics applications, especially for targets with

107 ence comparisons have become popular in many bioinformatics applications, specifically in the estimat

108 discussed and compared 43 such databases and bioinformatics applications, which enable users to conne

109 In this study, we combined a bioinformatics approach exploring both Molecular Taxonom

110 A specific bioinformatics approach identified 10 of these miRNAs to

111 Here, we used a bioinformatics approach to identify a family of neuronal

112 cells by CyTOF, and use a 'nearest neighbor' bioinformatics approach to trace cells to their original

113 We implemented an efficient bioinformatics approach using word embedding to summariz

114 An integrative bioinformatics approach, which connects the HSPC gene ex

115 lled cortical impact model, validated with a bioinformatics approach.

116 egories requires appropriately sophisticated bioinformatics approaches and thorough validation in div

117 ering procedure in CNV studies with suitable bioinformatics approaches can identify ADHD candidate ge

118 Bioinformatics approaches confirmed that Env7 Ser-331 is

119 become anticancer therapeutics, chemical and bioinformatics approaches for PROTAC design, and safety

120 For instance, NGS and bioinformatics approaches have been used to identify out

121 e, biomedical data science and translational bioinformatics approaches may help to develop better str

122 The combination of systems biology and bioinformatics approaches, together with powerful labora

123 n language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuou

124 Many bioinformatics areas require us to assign domain matches

125 ich has spurred a rapid growth in the use of bioinformatics as a means of interrogating antibody vari

126 ot automated or are restricted to users with bioinformatics backgrounds.

127 d flow cytometry analysis was validated with bioinformatics-based analysis and machine learning algor

128 We used bioinformatics-based phenotypic profiling and informatio

129 ide-copper complexes, while a combination of bioinformatics-based structure modelling, Cu(2+) ion doc

130 Here, through a combination of bioinformatics, biochemical and structural approaches, w

131 By single-cell transcriptomics and bioinformatics, both signaling and canonical translation

132 equencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classific

133 r-prone Nanopore sequencing is a substantial bioinformatics challenge.

134 ortunities provided by genome sequencing and bioinformatics, challenges associated with translating g

135 methods from the DMI DREAM Challenge for the bioinformatics community.

136 Numerous bioinformatics computational frameworks have been develo

137 ropose a model that bridges experimental and bioinformatics concepts using the Oxford Nanopore Techno

138 Rules to follow when writing an algorithmic bioinformatics conference paper to avoid having it rejec

139 An important problem in bioinformatics consists of identifying the most importan

140 A typical task in bioinformatics consists of identifying which features ar

141 Finally, we validated the proteomic and bioinformatics data by analysing glutathione metabolism

142 Screening was done with bioinformatics databases for unpublished/unexplored micr

143 ored the biology of co-expressed genes using bioinformatics databases, and identified known drug-gene

144 of top-ranked predictors in high-dimensional bioinformatics datasets, in order to avoid the potential

145 w drug candidates and natural compounds upon bioinformatics drug repurposing analyses, such as calciu

146 nvironments and can be used independently of bioinformatics expertise and resources.

147 An integrative bioinformatics, flux balance analysis, and experimental

148 and considerations for setting up an NGS and bioinformatics-focused infectious disease research publi

149 More broadly, we show that sequencing and bioinformatics followed by synthesis-enabled reverse gen

150 essor of Smt3) in C. albicans (CaWss1) using bioinformatics, genetic complementation, and biochemical

151 In bioinformatics, genome-wide experiments look for importa

152 ted by continuous advances in sequencing and bioinformatics, genomics is increasingly being applied a

153 xt generation sequencing (NGS) combined with bioinformatics has successfully been used in a vast arra

154 nces in molecular biology, microfluidics and bioinformatics have empowered the study of thousands or

155 in molecular biology, optics, genetics, and bioinformatics have opened the door to mapping, in molec

156 However, modern advances in genomics and bioinformatics have radically altered our view of HGT in

157 Comparative bioinformatics identified classical PC gene signatures a

158 In this study, we performed bioinformatics, immunohistochemical, and biochemical ana

159 ever, implementation of high-quality NGS and bioinformatics in research and public health laboratorie

160 l tools that will be useful for federating a bioinformatics infrastructure and the open research chal

161 r Biotechnology Information (NCBI), European Bioinformatics Institute (EBI) and the DNA Data Bank of

162 applications of deep learning algorithms in bioinformatics is increasing as they usually achieve sup

163 Data handling in clinical bioinformatics is often inadequate.

164 usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end l

165 In this era of data science-driven bioinformatics, machine learning research has focused on

166 ion from microscopy, structural biology, and bioinformatics may be integrated to build structural mod

167 m and the sequencing approach, the choice of bioinformatics methodologies, access to the appropriate

168 Here, we present machine learning and bioinformatics methods to leverage the evolutionary info

169 Demand for training life scientists in bioinformatics methods, tools and resources and computat

170 ted gene signatures by using statistical and bioinformatics methods.

171 ut for the biologist whose main focus is not bioinformatics, much of the computational work required-

172 Using bioinformatics, mutation and NMR, we identify a 7-residu

173 charomyces cerevisiae using a combination of bioinformatics, mutational experiments, and evolutionary

174 Finally, we summarize the bioinformatics of advanced viral, bacterial, and parasit

175 ) containing qcSSMDhomo is also available at Bioinformatics online.

176 Supplementary information is available at Bioinformatics online.

177 plementary Code and figures are available at Bioinformatics online.

178 supplementary data available at Bioinformatics online.

180 Here, we present a bioinformatics package, named APAlyzer, for examining 3'

181 We integrated bioinformatics pathogenicity triage, mean serum Ca conce

182 genetic variants (SparkINFERNO), a scalable bioinformatics pipeline characterizing non-coding genome

183 GBS-SNP-CROP is a bioinformatics pipeline originally developed to support

184 raction-based microbiome discovery (CSMD), a bioinformatics pipeline specifically developed to genera

185 ong with MEGARes, we released AmrPlusPlus, a bioinformatics pipeline that interfaces with MEGARes to

186 We integrated an optimized bioinformatics pipeline with high-resolution mycobiota s

187 A customized open-source bioinformatics pipeline, BLENDER (blunt end finder), the

188 A bioinformatics pipeline, Cenote-Taker, was developed to

189 Coupled with an easy-to-use bioinformatics pipeline, this method is particularly use

190 nge PCR and nanopore sequencing with a novel bioinformatics pipeline.

191 of CovReport that can be integrated into any bioinformatics pipeline.

192 is to manually perform many steps in a long bioinformatics pipeline.

193 s the potential to improve the robustness of bioinformatics pipelines for surveillance and could be a

194 Standard bioinformatics pipelines for the analysis of bacterial t

195 neration sequencing approaches combined with bioinformatics pipelines to identify novel gene networks

196 ribe a consortium-based approach to optimize bioinformatics pipelines to sensitively and accurately p

197 addition, we discuss the range of potential bioinformatics pipelines, including structural and funct

198 mmand-line interface readily integrates into bioinformatics pipelines, the intuitive web front-end wi

199 eers processed with identical sequencing and bioinformatics pipelines.

200 ), annotations can be easily integrated into bioinformatics pipelines.

201 leveraged IDSeq, a new open-access microbial bioinformatics portal.

202 we also performed functional annotations by bioinformatics prediction and expression quantitative tr

203 on that also will support the improvement of bioinformatics predictors, which critically relies on th

204 In recent years, to investigate challenging bioinformatics problems, the utilization of multiple gen

205 Next-generation sequencing (NGS) and bioinformatics processing were used for the identificati

206 s located?' is limited and requires advanced bioinformatics processing.

207 The data generated by the bioinformatics programs confirm the tendency of these pr

208 The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an

209 The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) serves many

210 y RNA sequencing (RNA-Seq), with appropriate bioinformatics, provides a robust tool to identify addit

211 ological databases are a fundamental part of bioinformatics, providing underlying components to many

212 Large-scale data analysis in bioinformatics requires pipelined execution of multiple

213 ue to be rapidly generated, a major focus of bioinformatics research has been aimed toward integratin

214 les reflect current trend and development in bioinformatics research.

215 ch can also be used in other protein-related bioinformatics research.

216 arch infrastructures, can describe their own bioinformatics resources and share these via bio.tools.

217 ion for life science that aims to coordinate bioinformatics resources in a single infrastructure acro

218 The corpus of bioinformatics resources is huge and expanding rapidly,

219 pathway analysis were performed using DAVID Bioinformatics Resources v6.7.

220 e great venues for disseminating algorithmic bioinformatics results, but they unfortunately do not of

221 Here, using a bioinformatics screen, we searched for putative protein

222 Here, using a comprehensive bioinformatics screening, we identified a putative EF-ha

223 Such bioinformatics scripts necessarily include similar basic

224 ed us to use an HSP70 inhibitor as bait in a bioinformatics search for structurally similar Food and

225 ited by researchers with different levels of bioinformatics skills and programming experience.

226 lly for researchers who do not specialize in bioinformatics skills or do not have access to expensive

227 ta in any genomic region without need of any bioinformatics skills or special computing resources.

228 of 10-13 d and require molecular biology and bioinformatics skills.

229 ale to the values of k and w used in current bioinformatics software programs.

230 In recent years, proprietary and open-source bioinformatics software tools have been developed for th

231 f unambiguous sulfopeptide identification by bioinformatics software.

232 is methods and support the data by employing bioinformatics statistical tools.

233 Finally, using bioinformatics, structural data, and targeted mutagenesi

234 CUBN variants were analyzed using bioinformatics, structural modeling, and epidemiological

235 nificantly improve performance in downstream bioinformatics studies and biomedical text-mining applic

236 arallel, we performed shotgun proteomics and bioinformatics studies on extracts of ruptured and stabl

237 Bioinformatics studies to match side-chain orientations

238 dontitis previously investigated by GWAS and bioinformatics studies.

239 Here we report a functional, structural, and bioinformatics study of Mtb enzymes initiating cholester

240 , the number of core facilities that provide bioinformatics support are also increasing.

241 datasets has necessitated the development of bioinformatics support core facilities that aid laborato

242 ed in core support facilities when providing bioinformatics support, drawing on our own experiences w

243 rd operating procedures to provide effective bioinformatics support.

244 ncluding single-cell mRNA-sequence analysis, bioinformatics, synthetic biology and high-throughput fu

245 It includes tools for popular bioinformatics tasks such as gene prioritization, sample

246 rediction is a fundamental precursor to many bioinformatics tasks.

247 NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New Yo

248 n editorial report of the supplements to BMC Bioinformatics that includes 6 papers selected from the

249 a scientist who is not trained in structural bioinformatics to access this information comprehensivel

250 The present study aimed to use bioinformatics to determine OPRM1 brain expression profi

251 We use bioinformatics to examine the domain architecture and ge

252 egulators of RBP-Jkappa DNA binding, we used bioinformatics to identify cellular DNA-binding protein

253 d how this information can be used alongside bioinformatics to understand mechanisms of binding and i

254 we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative m

255 We developed an innovative bioinformatics tool for visualizing functional annotatio

256 ation and phenotypic AMR, we developed a new bioinformatics tool, variant mapping and prediction of a

257 tic analyses greatly depend on the choice of bioinformatics tool.

258 development of the experimental methods and bioinformatics toolkits that have provided a fuller unde

259 ietary database ARESdb with state-of-the-art bioinformatics tools and curated public data.

260 uencing technologies, and highlight relevant bioinformatics tools and pipelines to predict the presen

261 nces in metagenomic sequencing, coupled with bioinformatics tools and population genetic models, faci

262 addition, we provide a review of some common bioinformatics tools and procedures used for pathogen di

263 Several advanced bioinformatics tools could help locate the potential che

264 Long-read sequencing coupled with bioinformatics tools enables the estimation of repeat co

265 lse-positive discovery rate of commonly used bioinformatics tools for detecting driver genes.

266 The package includes multiple bioinformatics tools including data normalization, annot

267 We determined that available bioinformatics tools may be ill-suited for verification

268 use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analy

269 Data analyses using various bioinformatics tools rely on programming and command-lin

270 This study utilizes published bioinformatics tools to quantify the frequency of aberra

271 We used a number of bioinformatics tools to uncover markers and sources of E

272 Bioinformatics tools were used to characterize epitopes

273 Several bioinformatics tools were used to filter the variants.

274 Compared to existing bioinformatics tools, AcrFinder has the following unique

275 odelling, laying the foundation for improved bioinformatics tools, knowledge-based resources and scie

276 ships and disease associations using various bioinformatics tools.

277 These 12 manuscripts cover a wide range of bioinformatics topics including network analysis, imagin

278 es in a single infrastructure across Europe; bioinformatics training is central to its strategy, whic

279 an evidence-based approach for strengthening bioinformatics training programmes across Europe, the EL

280 on dataset size, and assumes no specialized bioinformatics training.

281 Improvements in sequencing and bioinformatics turned the complex and cumbersome library

282 Structural bioinformatics was used to facilitate the identification

283 ing isotope labelling, mass spectrometry and bioinformatics, we calculate turnover rates of individua

284 ombining biophysical experiments, theory and bioinformatics, we dissect the interplay between the DNA

285 Using bioinformatics, we identified Glt(Ph) gain-of-function m

286 Using bioinformatics, we linked 6 h genes previously unknown t

287 To improve genome-wide TE bioinformatics, we performed long-read sequencing of cDN

288 Using structural bioinformatics, we show that a universal mutational prop

289 Using bioinformatics, we show that this imperfect targeting is

290 Using bioinformatics, we trace the evolution of the system thr

291 This paper presents GSP4PDB, a bioinformatics web tool that enables the user to visuali

292 stgraduate certificate (PG Cert) in Clinical Bioinformatics with a more technical audience.

293 t by combining the electron density map with bioinformatics without previous knowledge of the pilin s

294 In the bioinformatics work, we examine the distributions of ove

295 This paper presents a bioinformatics workflow for using RNA-seq data to discov

296 We developed a bioinformatics workflow to discover alternative splicing

297 A bioinformatics workflow was developed and evaluated usin

298 review, we summarize the most common NGS and bioinformatics workflows in the context of infectious di

299 allows for IgBLAST to be used in customized bioinformatics workflows.

300 s and publishes analyses using Galaxy, leads bioinformatics workshops that introduce and use Galaxy,