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1 d be responsible for a given disorder proved false.

2 tion index for all assemblages-adjusted by a false-absence ratio-which was examined using structural

3 sk for circulatory failure with a much lower false-alarm rate than conventional threshold-based syste

4 For both databases, the number of false alarms per hour reached values less than 0.5/h for

5 ns (i.e., pattern completion) underlies lure false alarms.

6 But, this is false, because many social norms are obviously maladapti

7 motor representations may underpin automatic false-belief tracking.

8 erage or genome reference bias, resulting in false calls.

9 n of signal properties, to identification of false detections and false positive rate estimation.

10 tive in recognizing key features and control false discoveries for class-imbalance learning.

11 ow to select the proper controls to minimize false discoveries, and experimental variations among bio

12 achieved the best trade-off between true and false discoveries, and this advantage is more apparent i

13 stributional assumptions, ability to control false discoveries, concordance, power, and correct ident

14 ignificant enrichments across nine diseases (false discovery rate < 0.05) (for example, NKX3-1 for pr

15 ectrometry (MS3) detected >3000 significant (false discovery rate < 0.05) phosphorylation events on >

16 We identified seven CpGs (false discovery rate < 0.05), of which three CpGs are lo

17 erformed using DESeq2 (|fold change|>1.5 and false discovery rate < 0.3), in patients compared to con

18 Strong evidence of association (false discovery rate <0.05) was found between PD and a p

19 17,974 differentially expressed genes (DEGs; false discovery rate <0.05; log-fold change cutoff = 0)

20 ographic traits in cross-sectional analyses (false discovery rate <0.10), and 8 of these proteins had

21 genes involved in the extracellular matrix (false discovery rate <0.25; NES, 2.25).

22 cell cycle and DNA damage checkpoint genes (false discovery rate <0.25; normalized enrichment statis

23 n = 954 controls), 343 genes were found with false discovery rate <5% (standardized mean difference m

24 rs at higher frequency among those with SLD (false discovery rate <= 1%), which expressed CD45RA, CCR

25 usters at higher abundance in the SLD group (false discovery rate <= 1%).

26 se who did not (log(2)fold change >=1.25 and false discovery rate <=5%).

27 = 417,508) using a conditional/conjunctional false discovery rate (FDR) approach to evaluate overlap

28 20 CpGs was associated with urinary As after false discovery rate (FDR) correction (FDR < 0.05).

29 The false discovery rate (FDR) from the target-decoy databas

30 Accurate estimation of false discovery rate (FDR) of spectral identification is

31 = 269,867) using a conditional/conjunctional false discovery rate (FDR) statistical framework that in

32 ong the 6 genetic variants selected at a 20% false discovery rate (FDR) threshold, the minor allele o

33 At a false discovery rate (FDR)-corrected level of 0.05, we f

34 s with multiple testing adjustment using the false discovery rate (FDR).

35 l were evaluated with the Benjamini-Hochberg false discovery rate (q) and logistic regression and the

36 in controls, respectively (fold change >= 2; false discovery rate [FDR] < 0.05).

37 and in the expression of 3,857 transcripts (false discovery rate [FDR] <= 0.1 and absolute fold chan

38 ple testing following the Benjamini-Hochberg false discovery rate [FDR] showed that 18 miRNAs were si

39 ine dinucleotide regions for 82 transcripts (false discovery rate [FDR]-P < 0.05).

40 complete a broad assessment of the empirical false discovery rate across other subject areas and char

41 the NHS and 96 metabolites in the HPFS after false discovery rate adjustment.

42 sting methods by obtaining better control of false discovery rate and comparable statistical power.

43 of order statistics, our method controls the false discovery rate and improves the power of detecting

44 The software introduces an MS2 false discovery rate approach, which is based on single

45 cirrhosis in phase 2 validation cohort, at a false discovery rate below 5%.

46 rformance in identifying correct biomarkers, false discovery rate control, and minimum estimation bia

47 on through data augmentation under preserved false discovery rate control.

48 21 to .49, canonical r(test) = .10 to .39, p(false discovery rate corrected) < .0001).

49 can be computed efficiently enough to enable false discovery rate estimation via permutation.

50 a method from Jager and Leek to estimate the false discovery rate for 94 journals over a 5-year perio

51 We also find that false discovery rate is negatively associated with log j

52 0.320, 95% CI - 0.015, 0.046, adjusted mean false discovery rate Open Access = 0.241 vs. closed acce

53 not satisfactory, having either a very high false discovery rate or strong dependence on sequencing

54 21 were significantly related to depression (false discovery rate q < .05).

55 hat were significantly associated with eGFR (false discovery rate Q value < 0.05) among HIV-positive

56 or vs 1.38 tumor hemisphere seeded; P = .03, false discovery rate threshold = 0.01).

57 ion and controlling for multiplicity using a false discovery rate threshold of 0.10.

58 ,000 abstracts enabling the study of how the false discovery rate varies by journal characteristics.

59 The false discovery rate was controlled at 1% (with continuo

60 were inferred using several methods, and the false discovery rate was controlled by the NestBoot fram

61 atistically significant evidence of a higher false discovery rate, on average, for Open Access versus

62 otypy, also in medial prefrontal cortex; all false discovery rate-corrected ps < .05), which are regi

63 ant changes in 164 metabolites (92.6%) at 5% false discovery rate.

64 d by using a sample permutation test and the false discovery rate.

65 ene associations while maintaining a nominal false discovery rate.

66 lipids using MS-DIAL 4 with a 1-2% estimated false discovery rate.

67 osition pair for every 7 amino acids at a 1% false discovery rate.

68 y-seven differential acetylation peaks (FDR [false discovery rate], 5%) pointed to pathways altered i

69 sults showing positive associations obtained false discovery rates < 0.05.

70 oundaries, remove interferences, and control false discovery rates.

71 njamini-Hochberg (BH) procedure was used for false-discovery control.

72 bacterial taxa were differentially abundant (false-discovery rate, <0.05) by asthma, atopy, or hay fe

73 our exploratory analysis were not robust to false-discovery-rate adjustment.

74 But what if this is a false distinction?

75 associated with blood cancers may result in false estimates of rare variant penetrance from populati

76 environmental conditions are rejected in the false expectation that better options will materialize.

77 ies, weakening statistical power and risking false findings.

78 ell as cause artificial imaging artifacts or false heterogeneity.

79 hich we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time

80 terate FMP with melamine or urea to give the false impression that it contains sufficient protein.

81 e the way for manipulation and the spread of false information.

82 the suprarenal abdominal aorta, often with a false lumen and intramural thrombus that thickens the wa

83 oss-sectional diameter, true-lumen area, and false-lumen area over several follow-up examinations wer

84 ine was used to accurately identify true and false lumina on CT angiograms of aortic dissection.

85 ross-sectional area profiles of the true and false lumina.

86 tial complexity informs our understanding of false memories or of the development of recollection and

87 aker [1] argue that boundary extension (BE), false memory beyond a view, is an artifact of stimulus s

88 Furthermore, both true and false memory entailed the reinstatement of concept-relat

89 Critically, subsequent false memory for lures was mitigated when high concept-s

90 en concept (e.g., apple) influences true and false memory for target or lure images.

91 inct representations protected against later false memory.

92 icated participants were more susceptible to false-memory creation using a virtual-reality eyewitness

93 all methods, we found evidence for enhanced false-memory effects in intoxicated participants.

94 nt and a test item, as assessed by different false-memory paradigms.

95 Cannabis seems to increase false-memory proneness, with decreasing strength of asso

96 at specificity, but are affected by moderate false negative and missing value rates.

97 The rate of false negative discordance was 8.6%.

98 by reducing the number of false positive and false negative interactions and is better optimized to p

99 Seven isolates were identified as false negative on the basis of the RDTs results.

100 We consider the false negative rate acceptable for routine clinical use;

101 andard kits and protocols, with an estimated false negative rate of 10%.

102 ptimization of XCMS parameters can lead to a false negative rate of up to 80% for chemicals spiked at

103 y be required to rule out the possibility of false negative results and there is currently a shortage

104 ting utility in mitigating false positive or false negative results of direct SARS-CoV-2 tests.

105 To minimize false positive and false negative test results in population-screening assa

106 The percentages of "false negative" samples were equal to 23.3% for indirect

107 False-negative (FN) screening examinations can be a surr

108 ved benchmark set for identification of both false-negative and false-positive germline large inserti

109 sensitivity and specificity (>90%), but the false-negative and false-positive rates makes the test s

110 have been misinterpreted, reviews causes of false-negative and false-positive results, and provides

111 ttings, resulting in high false-positive and false-negative calls.

112 CXR findings were the largest factor behind false-negative CXRs (40% normal and 87% combined normal/

113 -RADS resulted in nine false-positive and 16 false-negative findings, whereas VRC with a 5% threshold

114 reshold resulted in 29 false-positive and 10 false-negative findings.

115 LTBI screening is frequently false-negative in this patient population, likely due to

116 high likelihoods of both false-positive and false-negative inferences.

117 using dHRM analysis reduces the inclusion of false-negative partitions, changing the calculated DNA c

118 n endpoints were the detection rate (DR) and false-negative rate (FNR) of TLNB and TAD after NST.

119 ificant difference in biopsy rate (P = .54), false-negative rate (P = .38), cancer detection rate (P

120 Within the RAVEN cohort, the expected false-negative rate for detection at lower rep numbers u

121 The false-negative rate of the Accula POC test calls for a m

122 etection rate (CDR) per 1000 women screened, false-negative rate per 1000 women screened, positive pr

123 ded if the false-positive rate was <=20% and false-negative rate was <=35%.

124 The false-negative rate was 35% (14.2% [two of 14] of patien

125 On the day of symptom onset, the median false-negative rate was 38% (CI, 18% to 65%).

126 Recall rate, biopsy rate, false-negative rate, cancer detection rate, positive pre

127 False-negative rates were obtained for the DL risk score

128 False-negative rates were slightly lower for DBT (0.6 pe

129 ure of sample quality that could help reduce false-negative rates.

130 ternal amplification controls to account for false-negative reactions and amplicon high-resolution me

131 The sample with a false-negative result by CrAgSQ (n = 1) had a titer of <

132 In 19 of the 37 cases (51.4%), the false-negative result was potentially avoidable.

133 The pitfalls involve potential false-negative results due to blurring or missing lesion

134 and 96%, depending on the unknown number of false-negative results in single-tested patients.

135 False-negative results occurred in patients with PTLD in

136 All (16/16) CryptoPS false-negative results were obtained for samples with IM

137 had pre-existing PVD at the time of surgery (false-negative results).

138 sociated morbidity, fewer false-positive and false-negative results, lower-cost, and higher analytica

139 of false-positive results without generating false-negative results.

140 on that could help exclude false-positive or false-negative results.

141 clinical implications of false-positive and false-negative results.

142 fferentiation of highly positive samples and false-negative samples and can indicate whether the LFIA

143 False-negative severe acute respiratory syndrome coronav

144 False-negative SLNB results were reported in 5 articles

145 s based on high-risk characteristics, and no false-negative tears progressed to detachment at follow-

146 Eleven (84.6%) false-negative tears underwent follow-up within days bas

147 polymerase chain reaction testing, although false-negative test results may occur in up to 20% to 67

148 False-negative test results occurred in approximately 11

149 ative diagnostic test results (ie, suspected false-negative test results) compared with a representat

150 ease this probability, resulting in delayed (false-negative) declarations.

151 strategy resulted in fewer QALYs due to more false negatives but an ICER of $3,012, making this strat

152 Because of detection of potential false negatives in that cohort, the NAAT results of pair

153 ffected by high rates of false positives and false negatives(2).

154 tured by experiments on a rodent population (false negatives).

155 mal tests in conditions other than syphilis, false negatives, and automated nontreponemal tests.

156 sider, which can lead to false positives and false negatives.

157 of patients while 40% remained diagnosed as false negatives.

158 UK) in false positives and 9.4% and 2.7% in false negatives.

159 he perceived accuracy of both mainstream and false news headlines, but effects on the latter were sig

160 mpaign, which provided "tips" on how to spot false news to people in 14 countries.

161 ive evidence of endophyte inclusion creating false PBR distinctions but unexpectedly, several E- and

162 ssociated clinical net benefit in minimizing false poor outcome attribution might potentially prevent

163 ematic because of the discovery of excessive false positive (FP) mutations when sequencing picogram q

164 f the PPI networks by reducing the number of false positive and false negative interactions and is be

165 To minimize false positive and false negative test results in popula

166 rtions, whereas over-amplification increased false positive calls.

167 inst detection of colonization, resulting in false positive catheter-associated urinary tract infecti

168 t statistics in order to achieve the desired false positive control and was compared to the asymptoti

169 alternating laser excitation, which reduces false positive cross-correlation and facilitates comappi

170 lso show that the pH-sensitive probes reduce false positive detection rates in a mouse model of non-c

171 25 D+ and 50 D- (22 recipients from D- with false positive HIV tests).

172 However, the definition of a false positive in CAFA has not fully accounted for the o

173 ted another 8 cases of ON invasion that were false positive on histopathology (accuracy: 63.3%; sensi

174 viral load, suggesting utility in mitigating false positive or false negative results of direct SARS-

175 by certain chemical structures resulting in false positive outcomes.

176 rrent approaches also often suffer from high false positive prediction rates.

177 hat the approach allows drastic reduction of false positive quantitations and identifications even fr

178 prove diagnostic performance by reducing the false positive rate and improving the positive predictiv

179 the proposed procedures properly control the false positive rate at the nominal level.

180 s, to identification of false detections and false positive rate estimation.

181 The false positive rate in 140 patients with laboratory conf

182 atabase show a detection rate of 93.6% and a false positive rate of 0.16 per hour (FP/h); furthermore

183 on F measure, which combines sensitivity and false positive rate, Look4TRs outperformed TRF and MISA-

184 r data without incurring an inflation of the false positive rate.

185 genomes, balancing high sensitivity and low false positive rate.

186 had the best outlier detection accuracy with false positive rates < 0.05 and high sensitivity, and en

187 sion analysis and compared true positive and false positive rates (TPR/FPR).

188 genomes, we obtain estimates of the methods' false positive rates.

189 This phenomenon results in up to 5-10% of false positive results, depending on the chemical librar

190 ill lead to additional endoscopies with some false positive results.

191 n frequency in future fNIRS studies to avoid false positive results.

192 oor precision for these tasks, yielding many false positive signals.

193 nment bias that limit accuracy, resulting in false positive variant calls.

194 Of the 11 sera that were false positive with ELISA, seven samples (63.6%) were se

195 motif acts as an NES (true positive) or not (false positive).

196 rculation in the United States and found one false positive, indicating a specificity of 99.90%.

197 ering steps that can minimize the chances of false positive-findings due to sex-specific sequencing e

198 -2 produced 100% clinical specificity and no false positive.

199 mechanistic investigations, we show that the false-positive 18F-FDG-PET/CT result for detecting nodal

200 To reduce false-positive alarm rates and improve the accuracy of a

201 ereas VRC with a 5% threshold resulted in 29 false-positive and 10 false-negative findings.

202 Lung-RADS resulted in nine false-positive and 16 false-negative findings, whereas V

203 sed in screening settings, resulting in high false-positive and false-negative calls.

204 al data, leading to high likelihoods of both false-positive and false-negative inferences.

205 treatment, lower associated morbidity, fewer false-positive and false-negative results, lower-cost, a

206 by considering the clinical implications of false-positive and false-negative results.

207 e association studies (GWAS) and can lead to false-positive associations.

208 rticipants underwent PET-CT imaging based on false-positive blood tests, and 0.22% underwent a futile

209 Two false-positive cases of LR-5 included a cholangiocarcino

210 evels, which can lead to severe inflation of false-positive colocalization findings.

211 for appropriate disposition of patients with false-positive CT findings.

212 ngue virus and SARS-CoV-2, which can lead to false-positive dengue serology among COVID-19 patients a

213 Failing to consider COVID-19 due to false-positive dengue serology can have serious implicat

214 atures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinform

215 e complexity and the potential for causing a false-positive DNA barcoding paradox have been underesti

216 ence of intraretinal hemmorhage predicting a false-positive examination (adjusted odds ratio, 3.86; 9

217 The 1 patient with false-positive findings had a marginal negative CRM of o

218 levels and large increases in the number of false-positive genes and transcripts.

219 or identification of both false-negative and false-positive germline large insertions and deletions.

220 SQDIA reduced false-positive identifications, compared to experiments

221 vity spectral deconvolution, leading to less false-positive identifications.

222 primary prostate cancer, an equal number of false-positive lesions was observed among the different

223 Upon repeat testing, only a single false-positive MCR-2 producer remained, as the isolates

224 mportant information that could help exclude false-positive or false-negative results.

225 sults: Among 56 participants, 13 (22.8%) had false-positive osseous (68)Ga-PSMA-11 findings and 43 (7

226 tration rate less than 60 mL/min/1.73 m, the false-positive rate can be reduced when estimated glomer

227 actures were confirmed in 74, representing a false-positive rate of 16%.

228 We formalize how to minimize the false-positive rate of miBFs when classifying sequences

229 The VFs were included if the false-positive rate was <=20% and false-negative rate wa

230 although not statistically significant, the false-positive rate was higher in FMM (9.1%) than in FBB

231 Existing methods suffer from high false-positive rates and are unable to effectively diffe

232 sed but have their drawbacks, including high false-positive rates and limited antibody availability,

233 main advantages of DISCOVER-seq are (i) low false-positive rates because DNA repair enzyme binding i

234 ecificity (>90%), but the false-negative and false-positive rates makes the test suboptimal for preva

235 rning architectures are capable of producing false-positive rates that are orders of magnitude lower

236 spatial proximity with high sensitivity, low false-positive rates, and tunable detection distances.

237 es with single-nucleotide resolution and low false-positive rates.

238 r biofluids and substances that can elicit a false-positive response to colorimetric or presumptive t

239 had a titer of <1:5, while the sample with a false-positive result (n = 1) yielded a 1+ result.

240 r whether the benefits outweigh the risks of false-positive results and overdiagnosis of insignifican

241 s the likelihood of obtaining and publishing false-positive results and overestimated effect sizes.

242 esults due to blurring or missing lesions or false-positive results due to pseudo-low-uptake patterns

243 improved as a result of screening, and many false-positive results required additional, subsequent M

244 ing of nonviral targets avoided 75% (3/4) of false-positive results without generating false-negative

245 ve attached vitreous at the time of surgery (false-positive results).

246 preted, reviews causes of false-negative and false-positive results, and provides strategies to avoid

247 ed with DM and led to 2% (four of 159) fewer false-positive results.

248 ion efficiency with a negligible increase of false-positive risk, it contains several step-by-step op

249 Of the 21 false-positive samples genotyped by PANDAA, only 6 (29%)

250 oth strategies had the most screening tests, false-positive screening results, and benign biopsy resu

251 quencher-free approach that is resistant to false-positive signals, overcoming limitations associate

252 artifacts were miscalled as struts giving 1% false-positive strut detection.

253 be strongly outweighed by factors including false-positive TB treatment, reduced sensitivity, and fo

254 Ten (4.1%) false-positive tears or detachments were identified, wit

255 ther Spiromastigoides isolates as a cause of false-positive testing results, their phylogenetic relat

256 ak performance for predicting true-positive, false-positive, and negative examinations (AUC range, 0.

257 -100%) and specificity (86-90%) and very low false positives (6-10%) and negatives (< 5%), and it als

258 greater fixation loss but a similar level of False Positives (FP) as the HFA.

259 provided a detection sensitivity of 87% with false positives (FPs)/scan of 0.42.

260 False positives also varied by class: 20% for Br, 37% fo

261 e reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives.

262 ff-target background fluorescence, decreases false positives and enables accurate RNA profiling in un

263 n of mammograms is affected by high rates of false positives and false negatives(2).

264 gression) do not consider, which can lead to false positives and false negatives.

265 id and portable; however, they often display false positives and lack sensitivity.

266 ated with caution, such as the potential for false positives because of the exploratory nature of the

267 h in which we demonstrate a 10% reduction of false positives from 2.5 million analyses.

268 False positives in bioinformatic searches of the genome

269 rate, in simulations, that CAUSE avoids more false positives induced by correlated horizontal pleiotr

270 across the species' range, and that the low false positives make the output of the algorithm amenabl

271 rt against any culture-positive result, with false positives of <1% and 5.5% for Xpert and Ultra.

272 lculated and error types were categorized as false positives or negatives.

273 neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calib

274 more parsimonious set of features with fewer false positives than nCV.

275 accepting both more true positives and fewer false positives than the conventional approach of hidden

276 able factors are common and may lead to many false positives using alternative methods.

277 dramatically improve estimates and eliminate false positives when the assumptions of existing methods

278 cal workflows, harm resulting from potential false positives, and identifying the appropriate scope o

279 ion procedures that can reduce the number of false positives, and the challenges associated with thes

280 microRNA annotations contained not only many false positives, but surprisingly lacked >2000 bona fide

281 iments, our original design yielded numerous false positives.

282 tistical specificity, reducing the number of false positives.

283 in many clinical scenarios it is hampered by false positives.

284 t, multiple statistical testing may increase false positives.

285 analysis to distinguish true positives from false positives.

286 ethods but this resulted in a high number of false positives.

287 screening tests, showed a high percentage of false positives.

288 PTLD, given the observed high proportions of false-positives both at interim and at end-of-treatment

289 For estimation of specificity, false-positives were obtained by assessing for progressi

290 rs such as (18)F-PSMA, is required to reduce false-positives.

291 of nonpleocytic CSF samples, test yield and false-positivity rate, and time to appropriate deescalat

292 ken estimates of parameter values, and makes false predictions of dynamical features such as ultrasen

293 n, we also tested the method's robustness to false priors on a benchmark dataset, comparing the propo

294 miliarity due to repetition can also lead to false recognition of related but new items, particularly

295 have potentially magnified the variation in false spring risk among species with an increase in risk

296 w climate change has reshaped the drivers of false spring risk, complicating forecasts of future fals

297 focus on the multiple underlying drivers of false spring risk.

298 aped by late spring freezes after budburst - false springs - which may shift with climate change.

299 pring risk, complicating forecasts of future false springs, and potentially reshaping plant community

300 DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA-seque