ライフサイエンスコーパス: classification

1 sify unknown samples correctly (100% correct classification).

2 SVM models obtained better results (>92% of classification).

3 econd intervals (steeper dilation = "Longer" classifications).

4 sessment in lymphoma according to the Lugano classification.

5 Relief-F feature selection and random forest classification.

6 identifying clinical biomarkers for disease classification.

7 usters, demonstrating reproducibility of the classification.

8 of proteins have not been annotated for fold classification.

9 ucted at different levels are fused for eMCI classification.

10 s in extraction efficiency and bioinformatic classification.

11 ed on fundus photographs using the Rotterdam Classification.

12 ne expression profile testing and prognostic classification.

13 ommon subtypes and correlated with the basal classification.

14 done using a functional-group transformation classification.

15 of fistulas according to the standard Parks classification.

16 h unsupervised clustering and reference-free classification.

17 nt calling in addition to accurate taxonomic classification.

18 e motor and somatosensory systems and lesion classification.

19 dded to update the World Health Organization classification.

20 sessment in lymphoma according to the Lugano classification.

21 and PET features were the most important for classification.

22 ccurate multi-analyte signatures for patient classification.

23 cits that cuts across traditional, DSM-based classification.

24 (PPA) variants defined by current diagnostic classification.

25 ing ontologies to the outputs of data-driven classification.

26 ot always correspond to the established FXYD classification.

27 As for mutation detection and non-coding RNA classification.

28 reliability of tumor node metastasis system classification.

29 uires an accurate phenotypic description and classification.

30 ered minimally standard for their respective classifications.

31 by the World Health Organization and Panama classifications.

32 gency department visits using updated sepsis classifications.

33 n after considering several latency exposure classifications.

34 r integration of lncRNAs in molecular cancer classifications.

35 r adverse (34%) risk by European LeukemiaNet classification; 50% of patients had underlying myelodysp

36 ess meat and fish intake and improve subject classification according to the amount and type of meat

37 ted coefficients of determination (Q(2)) and classification accuracies ranging from 0.35 to 0.99 and

38 laparoscopic cholecystectomy, (2) assess the classification accuracy and (3) credibility of these sta

39 a are associated with instability, decreased classification accuracy and high-computational burden.

40 The classification accuracy of the transcranial magnetic sti

41 Using classification accuracy to guide platform optimization,

42 inated metastatic from primary melanoma (87% classification accuracy).

43 ormance and provides a marker list with high classification accuracy.

44 a small number of OTUs were assigned unique classifications across programs.

45 The new classification addressed to DHRs will enable the collect

46 ifferent training sets of CRMs, and employ a classification algorithm to integrate these similarity s

47 e classification models, various statistical classification algorithms are compared, and the k-NN (k

48 ations of epigenomes, and choosing different classification algorithms provided by our tool.

49 l taxonomic unit (OTU) generation, taxonomic classification, alpha- and beta-diversity measures for d

50 phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype number

51 Classification analyses also revealed that oscillatory p

52 emains intact, as shown through multivariate classification analyses of electroencephalogram (EEG) da

53 Cross-classification analyses showed that only 8.1% of foods w

54 Classification analysis of their brain network identifie

55 Further, with classification analysis, we predict the behavioural diag

56 s with CD were scored according to the Marsh classification and characterized for leukocyte infiltrat

57 Although the existing classification and grading approach is of prognostic val

58 ng a new technology that facilitates variant classification and keeps pace with variant discovery.

59 software that predicts the histopathological classification and post-treatment disease-free survival

60 enome sequences can be used to improve viral classification and provide insight into the viral "tree

61 acknowledges the inherent uncertainty of the classification and provides a natural measure of precisi

62 on that permits accurate automated taxonomic classification and quantitative data about organism ultr

63 apid qualitative susceptible/non-susceptible classification and quantitative MIC measurement in a sin

64 cation and regression tasks, yielding nodule classification and rating performance concordant with th

65 We trained Gaussian process (GP) classification and regression models with expression and

66 provement by the CNN model over HIST in both classification and regression tasks, yielding nodule cla

67 ve, Kaplan-Meier method, Cox regression, and classification and regression tree (CART) analyses were

68 Classification and regression tree (CART) analysis and l

69 We use a classification and regression tree model to further refi

70 lications were graded with the Clavien-Dindo Classification and scored with the Comprehensive Complic

71 ciated rectosigmoid location, combined Paris classification and surface morphology, and increasing si

72 oped, lead to further advances in diagnostic classification and treatment.

73 to compute environmental and genetic disease classifications and corresponding reliability measures.

74 including concepts for drug delivery and new classifications and therapeutic options for various form

75 g Discriminant Analysis, with 95% correct re-classification, and 97% grouping of grape skin and seed

76 dramatic effect on the accuracy of taxonomic classification, and alpha- and beta-diversity estimation

77 Here we developed new terminology, classification, and computer algorithms for automatic de

78 aluated by investigators per the 2014 Lugano classification, and patients could proceed to autologous

79 improve both subsystem annotation and k-mer classification, and tags new genomes as having signature

80 eness of Hetero-RP in diverse clustering and classification applications.

81 Automatic feature extraction and classification are two main tasks in abnormal ECG beat r

82 Taxonomic classifications are assigned from the species to the phy

83 This level of accuracy for classification as well as severity prediction far exceed

84 ariable models; for example, the Beck et al. classification AUC from 0.59 to 0.75 combining proportio

85 Orientation classification based on data from V1 thus paralleled the

86 is highlights the unreliability of ethnicity classification based on patient self-reports.

87 ates all diagnostic categories and optimizes classification by selectively combining a subset of moda

88 However, the accuracy of single cell classification by these features remains limited for mos

89 However, on further classification by time-averaged hematuria, only those pa

90 The lineage classifications by the tools generally only differed in

91 These classifications can potentially be used for patient stra

92 ts proposed new or revised species names and classification changes associated with fungi of medical

93 be little advantage from the use of the NOVA classification compared with the current epidemiologic a

94 ding performed 1 month later used predefined classification criteria and involved a third senior radi

95 Moreover, small tissue classification differences (2.23% +/- 0.68%) between gen

96 interdependence of the cell-of-origin (COO) classification, dual expression (DE) of MYC and BCL2 pro

97 The classification efficacy of both weighted SC schemes in t

98 The classification efficiency for test samples were recorded

99 tructures and measurements than any previous classification effort.

100 te differential network estimation and lower classification error than that achieved by other state-o

101 Sensitivity of the risk classification for AMI ranged from 87.5% to 100% in indi

102 metric technique" allowing the most accurate classification for each monovarietal extra-VOO was highl

103 ormed by 2 reviewers according to the Lugano classification for staging and response assessment.

104 e ability of the model showed a satisfactory classification for the test samples.

105 We developed a statistical classification framework that could diagnose CMV status

106 These data will inform tumor classification, genetic testing, and clinical trial desi

107 analysis, the vast differences in taxonomic classification, genome size, and radioresistance between

108 In rodents, subtype classification has associated subtypes to function.

109 clinical impact and prognostic value of this classification has been confirmed in numerous studies, a

110 Hierarchical classification (HC) stratifies and classifies data from

111 We consider methylation-based tumour classification highly relevant for the future diagnosis

112 e the utilization rate of BI-RADS category 3 classification; however, the overall number of patients

113 ere informative in 542 cases (88%), allowing classification in 10 exclusive primary cytogenetic subgr

114 e outpatient clinic, nasal endoscopy changed classification in only four patients (4.9%).

115 mas were reviewed, according to the 2008 WHO classification, in real time by experts through the Lymp

116 traumatic brain injury (Barell Matrix Type 1 classification, International Classification of Diseases

117 Data-driven classification into one of the 10 most common pathologic

118 A previous classification into simple, prolonged, and difficult wea

119 This classification is a more complete representation of chan

120 These results demonstrate that climate classification is an important factor when comparing hos

121 The present classification is compared with those proposed for other

122 appeared to be a reliable marker, allowing a classification matching the genetic diversity of the lin

123 ysis tool, Methylation-based Gene Expression Classification (ME-Class), to explain specific variation

124 microscope was utilized, and a computational classification method was developed to analyze the IR sp

125 An automated unsupervised network-based classification method was developed to simulate the appr

126 al companies yields a dynamic, more accurate classification method.

127 Here we critically evaluate three MOA classification methodologies using an aquatic toxicity d

128 This information, coupled with different classification methods (Partial Least Square Discriminat

129 ator outperforms two established multi-class classification methods on simulations and real data, eve

130 th standard and state-of-the-art multi-label classification methods.

131 a need for a more refined, molecularly based classification model for glioblastoma (GBM) in the temoz

132 ort #1, n = 70), we constructed a supervised classification model involving the most predictive featu

133 s from classical Bayesian methods in which a classification model is assumed and prior distributions

134 This research tested furan classification models in fried matrices based on the pat

135 Classification models were constructed to identify weigh

136 Hybrid classification models were developed that could predict

137 work focuses on supervised generative binary classification models, specifically linear discriminant

138 To construct cell type classification models, various statistical classificatio

139 ectosigmoid location, 0-Is or 0-IIa+Is Paris classification, non-granular surface morphology, and inc

140 Regardless of fuel, the current classification of "fresh" tailpipe emissions as nonhygro

141 urement of two milk mixtures yielded correct classification of >94%.

142 entation of a HC approach for "omics-driven" classification of 15 bacterial species at various taxono

143 blind samples, obtaining an accuracy in oil classification of 70%, taking the official established m

144 rticular, this dual approach allowed correct classification of 82% EVOO samples, while separate IRMS

145 outperforms existing methods with regard to classification of active compounds for cancers.

146 comes (KDIGO) criteria for the diagnosis and classification of acute kidney injury (AKI) in patients

147 rofiling platforms into the routine clinical classification of adult brain tumors.

148 riables contributing to characterization and classification of analyzed samples regarding the fruit o

149 e diagnosis away from the traditional binary classification of apparently infected versus uninfected

150 mmarize the results of 2 consensus meetings (Classification of Atrophy Meeting [CAM]) on conventional

151 This review describes the molecular classification of breast cancer and the use of predictiv

152 used for the intraoperative or postoperative classification of bulk tissue samples.

153 Our results support measurement and classification of CBE techniques and provide the foundat

154 -SNE)-based visualization (viSNE); automatic classification of cellular expression by nonlinear stoch

155 sions classified as grade I or II by the WHO classification of CNS tumors.

156 content heavily impacts the transcriptional classification of colorectal cancer (CRC), with clinical

157 he overall methodology may find uses for the classification of data from other biological networking

158 Multi-label classification of data remains to be a challenging probl

159 We developed an electrophysiology-based classification of dentate granule cells and mossy cells

160 These findings do not support the classification of DIF-negative patients, meeting the cli

161 useful biomarkers that can provide critical classification of disease for accurate diagnosis and to

162 riteria that used a combination of change in classification of disease severity based on alveolar bon

163 th primary aldosteronism using International Classification of Disease, 9th and 10th Revision codes,

164 dwide increasingly use the WHO International Classification of Diseases (ICD) system to classify diag

165 alth conditions defined by the International Classification of Diseases 10th revision chapter of the

166 determined on the basis of the International Classification of Diseases 9th or 10th Revision codes.

167 fective psychoses (International Statistical Classification of Diseases and Related Health Problems,

168 cal (defined using International Statistical Classification of Diseases and Related Health Problems,

169 tient visits and defined using International Classification of Diseases codes for bipolar disorder an

170 color Doppler in FHS and from International Classification of Diseases codes in Sweden.

171 ish version of the International Statistical Classification of Diseases version 8, 9, or 10.

172 cular conditions identified by International Classification of Diseases, billing codes.

173 [ICD-10] codes F20 to F29 and International Classification of Diseases, Ninth Revision [ICD-9] codes

174 es mellitus diagnosis by using International Classification of Diseases, Ninth Revision codes and cli

175 Matrix Type 1 classification, International Classification of Diseases, Ninth Revision head region s

176 ry occlusion was identified by International Classification of Diseases, Ninth Revision, Clinical Mod

177 ons diagnosed with giardiasis (International Classification of Diseases, Ninth Revision, Clinical Mod

178 re identified by the principal International Classification of Diseases, Ninth Revision, Clinical Mod

179 s were identified by secondary International Classification of Diseases, Ninth Revision, Clinical Mod

180 iagnoses of ASDs identified by International Classification of Diseases, Ninth Revision, Clinical Mod

181 ncluded 40011 patients with an International Classification of Diseases, Ninth Revision, coded diagno

182 we identified 156 743 with an International Classification of Diseases, Ninth Revision, diagnosis of

183 ation as a binary outcome, and International Classification of Diseases, Tenth, Revision, diagnosis a

184 ention" in accordance with the International Classification of Diseases-10th Revision (ICD-10).

185 with a admission diagnosis of International Classification of Diseases-9 code 584.xx (acute kidney i

186 AND Patients undergoing TAVR (International Classification of Diseases-Ninth Revision-CM codes 35.05

187 assay promises to be a powerful tool for the classification of effectors as well as for the functiona

188 tiveness and efficiency of our method in the classification of enhancers against random sequences, ex

189 identification of related animal models and classification of entire assay descriptions.

190 control region (left V1) yielded successful classification of facial identity.

191 s systematically, we provide a comprehensive classification of families based on their scaling proper

192 The NOVA classification of foods proposes 4 categories: unprocess

193 fully automated opportunity to customize the classification of genome-wide nucleotide variant data mo

194 d next-generation sequencing have led to the classification of HCCs based on molecular features and a

195 s) in ECs and use them as biomarkers for the classification of histological subtypes and the predicti

196 We investigated whether functional classification of HNF1A rare coding variants can inform

197 as powerful diagnostic methods for accurate classification of hydatidiform moles.

198 an reveal hidden principles of the system by classification of individual components, analyzing their

199 nt a framework for the automated large-scale classification of ion channel models.

200 s were as a result of inconsistencies in the classification of land-use categories during the study p

201 The 2016 World Health Organization classification of lymphoid neoplasms recently acknowledg

202 c mechanisms involved in lymphoma impact the classification of lymphoma and have significant implicat

203 ect to the degree of methylation, functional classification of methylated transcripts, and position b

204 The classification of miscible and immiscible systems of bin

205 ivity in initiating carbapenemase tests, and classification of most carbapenemases.

206 had an area under the curve of 0.82 for the classification of OA and control samples.

207 ts of olive oil, enhancing its potential for classification of olive oil samples according to their q

208 uantification models was proven and the best classification of olive oils according to the altitude o

209 taxonomies are ubiquitous, ranging from the classification of organisms to the file system on a comp

210 rders, how paradigm shifts in the phenotypic classification of patients would empower the search for

211 s ranging from whole-genome phylogeny to the classification of protein families, identification of ho

212 e first comprehensive machine learning based classification of protein kinase active/inactive conform

213 rth group IV to V, and 37 were International Classification of Retinoblastoma group C to E.

214 Classification of RMP subpopulations based on CD11b/CD11

215 and then classified according to Vertucci's classification of root canals.

216 for determining the safranal content and the classification of saffron for commercial purposes.

217 We attempted geographical classification of saffron using UV-visible spectroscopy,

218 thod was the only approach successful in the classification of samples, and thus prioritization, when

219 approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug

220 The simplified binary labelling and classification of sexual behaviour in dementia as approp

221 Here we demonstrate classification of skin lesions using a single CNN, train

222 Automated classification of skin lesions using images is a challen

223 found that pupil hazard rates predicted the classification of sub-second intervals (steeper dilation

224 re registered according to the Clavien-Dindo Classification of Surgical Complications, and Comprehens

225 Focused classification of the 14 subunits in each oligomer revea

226 Sequence alignments enabled classification of the evasins into two subfamilies: C8 e

227 work opens a venue for the expansion of the classification of topological phases of matter.

228 The 2016 revision of the WHO Classification of Tumours of Haematopoietic and Lymphoid

229 t (TAR) RNA, we achieved nucleotide-specific classification of two independent secondary structure mo

230 el of concordance between chest radiographic classifications of A and B Readers in a national surveil

231 to mining these data largely rely on binary classifications of disease vs. control, and are not able

232 Classifications of the complexity of ERCP have been pres

233 ugh leave-one-out cross validation and cross-classification on independent datasets, we show that thi

234 ks enables taxonomists to revise and propose classifications on an objective basis, changing ranks of

235 ive aspects and dimensions used for anatomic classification (PADUA) scoring systems and other tumor b

236 ility index with a cut-off of 1.05 gave good classification performance for Parkinson's disease tremo

237 ue is available that has proven to have good classification performance, and the diagnostic gold stan

238 LD response, and allowed for increased shape-classification performance, when it matched rather than

239 Even when using the same classification pipeline, the specific OTU-generation str

240 ettings in QIIME, MOTHUR and a pplacer-based classification pipeline, using a novel software package:

241 members of the fungal community, and greater classification power is realized by generating consensus

242 Regardless of the classification, predictive value for development of peri

243 automated detection was applied to a 2-class classification problem in which the task was to distingu

244 ariables (eg, age, comorbidities, ASA, wound classification), procedure type (eg, laparoscopic vs ope

245 ed by spectral analysis and supported by the classification provided by the PCA analysis.

246 ation was evaluated and ranked using several classification quality metrics.

247 ity measures were used to determine possible classification rates for differential treatment outcomes

248 Results showed correct classification rates for genuine and false samples over

249 vity scores for these three regions, overall classification rates of 72%-78% for remission and 75%-89

250 se hits for each query sequence, and further classification reliabilities are evaluated by bootstrap

251 This classification restricts patients presenting with clinic

252 The best new expanded classification rule was platelet count >110 x 10(9) cell

253 leukemia (ALL), a uniform CSF and risk group classification schema was incorporated into Children's O

254 was used to create a novel miRNA-based risk classification scheme (AMLmiR36).

255 Here, we report a morphologic classification scheme for definitive PVN from the Banff

256 We recommend using this morphologic PVN classification scheme for diagnostic communication, espe

257 ce, we select features that best benefit the classification scheme in the kernel space.

258 This novel stability-guided classification scheme is delivered in two flavours: duri

259 s a byproduct, we develop an order-parameter classification scheme that predicts supertransitions bet

260 play an important role in making data driven classifications searchable and query-able, but fulfillin

261 igmatic, suggesting a need to define disease classification subtypes that inform disease progression

262 sources not consistently captured by common classifications such as "humic-like" fluorescence.

263 A classification system and criteria for OCT-defined atrop

264 discussions at 3 meetings over 12 months, a classification system based on OCT was proposed for atro

265 ded according to the OCT-based International Classification System developed by the International Vit

266 s were graded according to the International Classification System for age-related maculopathy and st

267 A classification system has been described as predictive o

268 s between 2012 and 2014 and used a validated classification system to map services to seven subspecia

269 ature regarding FCDs, addressing the current classification system, histopathology, molecular genetic

270 ntity in the World Health Organization (WHO) classification system, is readily recognized as a partic

271 iopsy and classified according to the Vienna Classification system.

272 raded according to the modified Airlie House classification system.

273 mmon utilization of inadequate or inaccurate classification systems among healthcare providers.

274 Ultimately, these classification systems should be unified into an all-enc

275 uggest that widely used harmonised commodity classification systems should evolve to address these ga

276 dardized the terminology around chronic GVHD classification systems to ensure that a common language

277 ant anal sphincter anatomy, imaging methods, classification systems, and treatment objectives.

278 st text-based formats that contain taxonomic classifications, taxon names, taxon identifiers, or sequ

279 igh accuracy and is effective as a new image classification technique.

280 e used fMRI in combination with multivariate classification techniques to determine which brain regio

281 orrelation (CaMCCo), for fusion and cascaded classification that incorporates all diagnostic categori

282 al for using the outputs of data-driven cell classification to structure ontologies and integrate the

283 ndings support the use of the SOFA and qSOFA classifications to identify patients with infection who

284 r prediction, orthology and multigene family classification, transcriptome analyses, phylogenetic ana

285 Classification tree analysis was performed to identify h

286 ven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus

287 motility disorders was based on the Chicago Classification validated for SWS (CCv3) and with STM (CC

288 cal baseline variables could not achieve any classification (validation AUC: 0.50; 95% CI: 0.36, 0.64

289 Future research needs to employ sex as a classification variable, as sex differences can generall

290 Performance of the classification was evaluated and ranked using several cl

291 Results COO classification was successful in 414 of 452 samples.

292 Type 5 Vertucci's classification was the most frequently observed canal co

293 riodontitis at age 31 years according to six classifications was used as the gold standard to compute

294 ate the robustness of these proposed subtype classifications, we evaluated 12 public datasets, togeth

295 for one class in a GLM or that receive high classification weights in MVPA tend to exhibit high nois

296 according to the European LeukemiaNet (ELN) classification were eligible for ASCT in first remission

297 rding to the METAVIR scoring system or Brunt classification when appropriate.

298 ed to standardize nomenclature and taxonomic classification, while incorporating new allele submissio

299 earning algorithms achieve blind single cell classification with up to 95% accuracy.

300 ere analyzed with the Lymph2Cx assay for COO classification, with immunohistochemistry for MYC and BC