ライフサイエンスコーパス: classification

1 umor grading (World Health Organization 2010 classification).

2 tumor signal perturbation status and subtype classification.

3 pproach, confirming the need for a consensus classification.

4 ntial predictive biomarkers of LGG molecular classification.

5 st in meningioma and integrated into the WHO classification.

6 Responses were assessed as per Lugano 2014 classification.

7 entional radiographs according to the Risser classification.

8 mplementing a popular ResNet model for image classification.

9 aea, and their sequence- and structure-based classification.

10 that can be applied to data exploration and classification.

11 f sensitivity, specificity and total correct classification.

12 y Criteria for Adverse Events (CTCAE; v4.03) classification.

13 ) according to the 2017 Periodontal Diseases Classification.

14 nd a random forest classifier for BD vs. MDD classification.

15 rding to the 2017 European LeukemiaNet (ELN) classification.

16 or differential PM(2.5) exposure and outcome classification.

17 rchitectures, thereby enabling more accurate classification.

18 g supervised learning in tasks such as image classification.

19 re in agreement with their current taxonomic classification.

20 racy of 98.5 percent was achieved for the RF classification.

21 as the state of the art for molecular tumor classification.

22 gic review committee according to the Lugano classification.

23 ribution towards movement type and frequency classification.

24 f procedures were shifted into a new outcome classification.

25 tate cancer biopsy samples, enabling disease classification.

26 pproach for combining feature selection with classification.

27 on, with new developments in DNA methylation classification.

28 es were performed to compare LUCK and Killip classifications.

29 e the establishment of the aortic dissection classification 50 years ago.

30 n the combined dataset and found overall MLR classification accuracies: 93.2% Setser80, 87.9% Seldin

31 d frequency of the malformations with a high classification accuracy (84%).

32 yed, and the prediction model exhibited high classification accuracy (ranging from 0.89 to 0.92), hig

33 correlation and interactions to compare the classification accuracy and feature selection performanc

34 t cognition would demonstrate greater HC-SCZ classification accuracy and that combined gene-environme

35 The average classification accuracy for pairwise separation is 99% w

36 n each burn group and the average multiclass classification accuracy is 93%.

37 Overall classification accuracy of the validation set (n = 300)

38 The resulting high classification accuracy suggests that the method is a va

39 how that it is possible to achieve very high classification accuracy using datasets with as few as 26

40 act, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% C

41 tions, evaluated whether biomarkers improved classification accuracy when added to clinical evaluatio

42 nalysis, linearly weighted kappa values, and classification accuracy.

43 We propose a cross-species gene classification across the Full Spectrum of Intolerance t

44 XGBDA classification afforded 100% correct class assignment fo

45 n (N) status as required by recent biomarker classification algorithms (A/T/N).

46 compared their prediction power using three classification algorithms and rigorous statistical proce

47 Finally, machine learning classification algorithms applied to group lasso-selecte

48 In many cases, one can also use classification algorithms from the machine-learning fiel

49 stive study comparing thousands of competing classification algorithms that were trained on our propr

50 Classification algorithms were used for prediction of N-

51 Classifications among these hazards, induced through qua

52 we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data

53 Chemometric classification analysis showed that synergistic enzymes'

54 on Congenital Anomalies and Twins (EUROCAT) classification and adjudicated into four categories: rel

55 ta improves the robustness of prediction and classification and advances biomarker discovery.

56 rks have advanced the field of detection and classification and allowed for effective identification

57 The pathogenesis, classification and assessment of SSc-associated digital

58 Particle classification and averaging yields structures to 5.6 an

59 ould ultimately accelerate the comprehensive classification and characterization of individual somato

60 ifferent GPCRs, spanning different levels of classification and conformational states and totaling to

61 l images may improve their use for automated classification and detection tasks.

62 American Society of Anesthesiologists risk classification and duration of surgery as well as German

63 d secondary endpoints related to operational classification and leprosy-associated disabilities at di

64 cessing, data selection, feature extraction, classification and model validation.

65 nd 285 EAC cases from the Oesophageal Cancer Classification and Molecular Stratification consortium i

66 s including exploratory analysis, supervised classification and multivariate calibration.

67 mic data with morphologic diagnosis improves classification and prognostication.

68 utperformed state-of-the-art methods in both classification and regression settings under various dat

69 for each PIRO component was developed, and a classification and regression tree was used to stratify

70 mponents and the cut-offs estimated from the classification and regression tree, patients were strati

71 paradigms, ranging from regression to image classification and reinforcement learning.

72 The combined text classification and signal prioritization significantly e

73 ome an important consideration in SARS-CoV-2 classification and surveillance.

74 ause of an oversimplified system for patient classification and the development of drugs that do not

75 -amylase/pullulanase) falls under the former classification and the latter classification is the comb

76 discuss future prospects for disease-subtype classification and therapeutic intervention.

77 The use of dd-cfDNA may complement the Banff classification and to risk stratify patients with border

78 ancer is a barrier to accurate molecular sub-classification and treatment efficacy.

79 Continued and increased sharing of variant classifications and evidence across laboratories, and th

80 understanding of brainstem gliomagenesis and classification, and guide future studies for the develop

81 We review their properties, introduce a classification, and present their general intuition.

82 district, hospitalisation status, age, case classification, and quarter (date of case reporting aggr

83 e contrast, searchlight multivariate pattern classification, and whole-brain decoding with L1 or L2 r

84 es in data collection, missing or inaccurate classifications, and misleading or inconclusive results.

85 gh an analysis of the clinical and molecular classifications, and the complications and clinical mana

86 Given that symptom-based classification approaches do not align with underlying n

87 The results of classification are characterized by a set of indices cal

88 Our results demonstrate that despite IGPS's classification as a carboxy-lyase (i.e. decarboxylase),

89 with some distinct features that warrant its classification as belonging to a novel family of short-c

90 h both WHO integrated histology and mutation classification as well as methylation-based classificati

91 acteristics of patients by change in symptom classification at 12 months (improved=decreased RC, no c

92 al were more likely to have worsened symptom classification at 12 months.

93 Nets to perform residue-level ensemble error classifications at multiple predefined error thresholds,

94 kton biomass or nutrient loadings on trophic classification based on APPP.

95 Ensemble is an ensembled classification based on distinct feature selection and m

96 Classification based on root traits resulted in four clu

97 as applied for the first time for golden rum classification based on several factors as fermentation

98 cterial host range, GC content, and existing classifications based on replicon and mobility (MOB) typ

99 ich was 10.5% better than the document-level classification baseline.

100 ides novel biological insights into RGC type classification, brain connectivity, and cytoarchitectoni

101 tral EEG features that contribute to texture classification but have low contribution towards movemen

102 redictions for Chemlali variety (99% correct classification), but had more difficulty to discriminate

103 ty of America (IDSA) diabetic foot infection classification by adding a separate tier for osteomyelit

104 tivars using NMR and HRMS data, but only for classification by marketability.

105 We show how classification capacity improves along the hierarchies o

106 crease in or remaining in 1 of the 2 highest classification categories of the Western Ontario and McM

107 tumours, the World Health Organisation (WHO) classification categorises bone tumours based on their s

108 -wide independent loci across 19,155 disease classification codes from 320,644 participants in the UK

109 Even within a given classification, considerable variation exists in disease

110 G4-RD Responder Index) and the validation of classification criteria, both of which were the products

111 nspired method, "disassembly asymmetry score classification (DASC)", that resolves ACs from CCPs base

112 Relaxing the presumption of a binary classification, data-driven analysis identified 4 subgro

113 idual genes or gene-gene interactions affect classification decisions.

114 Modern practice for training classification deepnets involves a terminal phase of tra

115 e functional connections used for diagnostic classification deviated from typical development.

116 o analyze and classify migrating cells, such classification did not exploit SPHARM spectra in their d

117 A proteomics-informed classification distinguished the clinical characteristic

118 pathology and clinical presentation, genomic classification enables earlier treatment for high-risk p

119 nd NEC-based DNAm signatures exerted a lower classification error than the PBMC-based DNAm markers (p

120 The classification, first proposed by Alfred Russel Wallace

121 ure T-cell or NK lymphomas (WHO 2001 or 2008 classifications) from 74 centres in 13 countries (in Asi

122 FTIRS gave correct classification greater than FAs (97.4% vs. 81.1%) during

123 imensional and 3D CNNs applied to ACL lesion classification had high sensitivity and specificity, sug

124 Molecular classification has strong prognostic value in high-risk

125 ility graph motifs produce fast and accurate classifications, highlighting that purchase prediction i

126 ive classes obtained after three-dimensional classification identifies several rotated states.

127 The new classification includes 2 classes, 6 types and 33 subtyp

128 the ST and SM groups (P = 0.0073); and (iv) classification incorporating genomic data was highly pre

129 ing that facial traits critical for accurate classification influence selective attention toward con-

130 linked International Neuroblastoma Pathology Classification (INPC) criteria.

131 hology and antigen expression profile enable classification into one of the four types of classic HL

132 The current mechanism for classification into these molecular subtypes is through

133 ully incorporated the most relevant previous classifications into a treatment-oriented diagnostic mat

134 atients do not currently exist and the PTLDS classification is based on the report of persistent, sub

135 le cells to build predictive models for cell classification is demonstrated.

136 Moreover, pursuit classification is far worse in the absence of head movem

137 o construct the textural feature set and the classification is performed using nonlinear support vect

138 der the former classification and the latter classification is the combination of two of each of the

139 e goal of developing an integrated molecular classification is to improve diagnostic classification,

140 Each of these classifications is assigned a calculated probability as

141 onservative treatment according to the TLICS classification (match rate 94.9%, 131/138).

142 s poor inter-rater reliability of Alpha/Beta classification (mean kappa = 0.31).

143 dance peak height and width, three different classification methods were tested and compared.

144 Multivariate classification methods were used to find patterns among

145 er is to compare and contrast enrichment and classification methods, offering two contributions.

146 ncrease) using remote sensing data and image classification methods.

147 (nCV) is a common approach that chooses the classification model and features to represent a given o

148 first ML model included a random forest (RF) classification model, which was used to identify wet or

149 assification of class 1 patients in the Mayo classification model.

150 tic resonance (TD-NMR), and machine learning classification models (ML) for monitoring soluble pectin

151 We optimized classification models for preoperative Computed Tomograp

152 subcellular location information, and built classification models for the complex protein spatial di

153 Tests of the classification models on an external validation set reve

154 ecific molecular information and to generate classification models using machine learning technology.

155 Classification models were applied to GSSCP from Eocene

156 Chemometric classification models were built on individual and combi

157 Random forest classification models were trained to differentiate infe

158 proteins were used to develop regression and classification models with the GUSAR software.

159 a second larger set to build prediction and classification models.

160 ped three convolutional neural network (CNN) classification models: maximum projection (MPM), multisl

161 Finally, using connectome-based classification, most models trained on dFC network inter

162 Based on our transcriptional classifications, most homed HSCs in bone marrow and sple

163 The classification network aimed to grade the tumors accordi

164 This detection was used as input for the classification network.

165 This community-based classification, nomenclature and data aggregation could

166 and June 2018 with a Lung-RADS (version 1.0) classification of 2, 3, 4A, or 4B in the clinical settin

167 models and machine learning methods for the classification of 6 types based on colour and residual s

168 nt a new, automated method for arriving at a classification of a MALDI-ToF sample, provided the colla

169 Classification of ACL injuries using deep learning invol

170 verage of structural data, aiming to provide classification of almost all domain superfamilies with r

171 Using clinical classification of AMD with color photography, RPD were s

172 imensions, opening a route toward a complete classification of amorphous topological states in real s

173 This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-m

174 defined in accordance to recently published Classification of Atrophy Meeting criteria as sharply de

175 a were consistent with those proposed by the Classification of Atrophy Meetings (CAM) group: hypertra

176 scheme is then applied to obtaining the full classification of bosonic TCSs protected by several onsi

177 N and mini-GCNs are useful resources for the classification of brain regions and identification of bi

178 ls performed to ensure the BDNF level in the classification of CAD from healthy controls.

179 limited in the United States by the ongoing classification of cannabis as a Schedule 1 controlled su

180 lling of class analogy with 97%) and correct classification of carrot samples.

181 Further sub-classification of clinical-molecular correlates stratifi

182 pithelial neoplasia, and the WHO 4th edition classification of conjunctival melanocytic intraepitheli

183 vides a comprehensive molecular and cellular classification of conventional and unconventional outflo

184 Patients were identified using International Classification of Disease for Oncology, Third Edition, c

185 r more records with a relevant International Classification of Disease in the patient register (in th

186 tic disorder (AD) according to International Classification of Disease-9th edition (ICD-9) codes.

187 s were identified based on the International Classification of Diseases (ICD-10) for cirrhosis or its

188 were queried for the following International Classification of Diseases codes from May 20, 2014, thro

189 related AEs were identified by International Classification of Diseases diagnosis codes.

190 International Classification of Diseases diagnostic codes are used to

191 es of two previously published International Classification of Diseases, 10th Edition, coding strateg

192 of Diseases, 9th Edition, and International Classification of Diseases, 10th Edition, coding systems

193 riteria, and for records using International Classification of Diseases, 10th Edition, we deployed a

194 rs of age by primary/secondary International Classification of Diseases, 10th Revision (ICD-10) diagn

195 farction (AMI) or stroke using International Classification of Diseases, 10th revision codes.

196 across the transition from the International Classification of Diseases, 9th Edition, and Internation

197 For the records using International Classification of Diseases, 9th Edition, we used previou

198 ed individuals ages 18-64 with International Classification of Diseases, 9thRevision diagnosis codes

199 iagnosis of endophthalmitis by International Classification of Diseases, Ninth and Tenth Editions, co

200 diagnoses were ascertained by International Classification of Diseases, Ninth Revision (ICD-9) codes

201 International Classification of Diseases, Ninth Revision (ICD-9) codes

202 patients with histoplasmosis (International Classification of Diseases, Ninth Revision, Clinical Mod

203 dentify adult outpatients with International Classification of Diseases, Tenth Revision, Clinical Mod

204 genome sequences have a rich history in the classification of DNA sequences.

205 n Fourier spectrum features allows efficient classification of electrograms recordings as AF driver o

206 A convolutional neural network was used for classification of enhancing lesions on unenhanced MRI sc

207 informative features were identified for the classification of ER agonists/antagonists.

208 he fully automated anatomic localization and classification of fluorine 18-fluorodeoxyglucose PET upt

209 Classification of full tears by both networks was also c

210 classification as well as methylation-based classification of gliomas.

211 Classification of high-uptake regions using deep learnin

212 mor stroma composition and built a TME-based classification of ICC tumors that detects potentially ta

213 methodology for automated identification and classification of ILA patterns in computed tomography (C

214 rpose, with uses ranging from annotation and classification of individual signals or signal-clusters

215 rge-scale sequencing efforts have led to the classification of melanoma into four major subtypes (i.e

216 nt which is widely relied upon for taxonomic classification of metagenomic sequences.

217 This allows for a classification of minimal autocatalytic motifs called co

218 The classification of MPD had similar or lower success rates

219 t unsolved diagnostic issues in the 2017 WHO classification of myeloid neoplasms and the importance o

220 Current computational tools for classification of nat-siRNAs are limited in number and c

221 proteomics approach to facilitate diagnostic classification of pathogen groups with reticulated phylo

222 s useful, it should not be viewed as a rigid classification of pathogenic microbes, which exhibit rem

223 outliers may severely undermine the correct classification of patients and the identification of rel

224 Prior classification of peaks as either from isoleucine, leuci

225 ehavioral inhibition) to provide more robust classification of pediatric anxiety problems.

226 The new classification of periodontal diseases recognizes the ke

227 Among them, the 2017 classification of phenotype and gingival recession succe

228 binding protein lineages in the Evolutionary Classification of Protein Domains database.

229 learning produced highly accurate and robust classification of resistance to HIV protease inhibitors.

230 bined with chemometric methods were used for classification of six genotypes (five varieties and a pa

231 e been established by the Banff 2007 Working Classification of Skin-Containing Composite Tissue Allog

232 or International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), examinat

233 ion of CDh neurons and strongly impaired the classification of task-epochs based on CDh activity.

234 s we have at our disposal, I propose a broad classification of techniques into six complementary appr

235 definition in the fourth edition of the WHO classification of the digestive tract tumors of 2010 the

236 nologies, a new 'worldview' is emerging: the classification of TIMCs into subtypes that are conserved

237 cation systems exist for the description and classification of UTIs, with the common rationale that c

238 ined among other multivariate algorithms for classification of wines.

239 There is no change in the classifications of "proven," "probable," and "possible"

240 provide information based on sex and common classifications of race/ethnicity, socioeconomic status

241 eening Reporting and Data System (Lung-RADS) classifications of solid lung nodules detected at lung c

242 ercial lots of caMHB, resulting in different classifications of susceptibility among MBL-harboring En

243 In the binary classification, one of the new DTI features obtained the

244 Little is known about the changes in symptom classification over time in patients with peripheral art

245 0.0001), while CD had more severe Mayo risk classification (p < 0.0001) and more PKD1 mutations (p =

246 radiomic features were most relevant to both classifications (p <= 0.044).

247 w AMR detection models, development of a new classification paradigm and expansion of analytical tool

248 ch types of features are best for optimizing classification performance and which algorithms are best

249 -environment stratification modulates HC-SCZ classification performance of cognition, perhaps providi

250 In this study, we assessed the classification performance of TMS parameters in the diff

251 and become a crucial aspect for determining classification performance.

252 Second, we introduce the new gene expression classification problem, which focuses on identifying exp

253 , and their combination improved the correct classification rate.

254 d the association between changes in symptom classification (RC) at 12 months and subsequent cardiova

255 ease), we examined the changes in Rutherford classification (RC) of patients over 12 months.

256 and Oueslati varieties (98% and 90% correct classification respectively).

257 ui and Oueslati samples (95% and 84% correct classification respectively).

258 These good classification results in addition to the potential for

259 Furthermore, the tumor classification results in this work is ranked at second

260 ular classification is to improve diagnostic classification, risk stratification and assignment of mo

261 te and severe infection criteria improve the classification's ability to direct therapy and determine

262 ing Group published a new 3-tier morphologic classification scheme derived from in-depth statistical

263 Interaction Z-Score Assessment), is a binary classification scheme for identification of native prote

264 Recognizing the need for a systematic classification scheme, several groups have used single-c

265 omography (SD-OCT) imaging and present a new classification scheme.

266 interconnectivity with previously identified classification schemes and high robustness of the mesenc

267 EC molecular classification should be incorporated in the risk strati

268 subjective experience-and compare supervised classification solutions with those from unsupervised cl

269 used FNAs and nanomaterials along with their classification, structure, and application features.

270 The classification, synthesis, properties and applications o

271 tiative for Chronic Obstructive Lung Disease classification system (P = .0015), more frequent exacerb

272 Each category of this new classification system is linked with treatment recommend

273 Any future kidney stone classification system should be aimed at distinguishing

274 of the TERT/telomere pathway and establish a classification system whereby the associations between T

275 c intraepithelial neoplasia, and 81% for WHO classification system.

276 Several classification systems exist for the description and cla

277 greatest interpretative challenge with all 3 classification systems.

278 candidate and forms the basis of a number of classification systems.

279 n-stained virtual microscopic slides using 3 classification systems: PAM, conjunctival melanocytic in

280 roaches to regularizing the high-dimensional classification task with a larger regression dataset, al

281 imates for these typically highly multiclass classification tasks are still lacking.

282 data confirm the clinical impact of the WHO classification that separates ISM from CM and from other

283 Results validate the PVN classification, that is, the 3 PVN disease classes predi

284 ng Freedom House's coding and terminological classifications, the proportion of often illicit Onion/H

285 n approach that leverages previous scRNA-seq classification to identify cell types using multiplexed

286 is study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens

287 ied by age, sex, ethnicity, and aetiological classification (Trial of Org 10172 in Acute Stroke Treat

288 Degree of EI was categorized as Kansas City classification: type 1: erythema; type 2: ulcers (2a: su

289 erty brings difficulties for disease outcome classification using deep learning techniques.

290 A novel classification using standard pathology as proxy for poo

291 work to identify informative features for AD classification using tau positron emission tomography (P

292 The percentage of correct classification was higher for the fingerprinting (100%)

293 to assess if parameters included in the new classification were predictive of tooth loss after a lon

294 xpression network analysis and Random Forest classification were used to discover potential biomarker

295 kappa values across all Lung-RADS classifications were greater than or equal to 0.81, with

296 r framework combing supervised deep learning classification with automated un-supervised clustering f

297 se and the association of changes in symptom classification with subsequent cardiovascular disease ev

298 calculated probability as well as alternate classifications with associated probabilities.

299 -body DW MRI- and FDG PET/MRI-based response classifications with Krippendorff alpha statistics.

300 ignment as well as a standard Within Subject Classification (WSC) approach.