scholarly journals Introducing Bayesian Thinking to High-Throughput Screening for False-Negative Rate Estimation

2013 ◽  
Vol 18 (9) ◽  
pp. 1121-1131 ◽  
Author(s):  
Xin Wei ◽  
Lin Gao ◽  
Xiaolei Zhang ◽  
Hong Qian ◽  
Karen Rowan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
False Negative ◽  
False Negative Rate ◽  
Training Data ◽  
Distribution Profile ◽  
False Negatives ◽  
Data Set ◽  
Active Compounds ◽  
Negative Rate

High-throughput screening (HTS) has been widely used to identify active compounds (hits) that bind to biological targets. Because of cost concerns, the comprehensive screening of millions of compounds is typically conducted without replication. Real hits that fail to exhibit measurable activity in the primary screen due to random experimental errors will be lost as false-negatives. Conceivably, the projected false-negative rate is a parameter that reflects screening quality. Furthermore, it can be used to guide the selection of optimal numbers of compounds for hit confirmation. Therefore, a method that predicts false-negative rates from the primary screening data is extremely valuable. In this article, we describe the implementation of a pilot screen on a representative fraction (1%) of the screening library in order to obtain information about assay variability as well as a preliminary hit activity distribution profile. Using this training data set, we then developed an algorithm based on Bayesian logic and Monte Carlo simulation to estimate the number of true active compounds and potential missed hits from the full library screen. We have applied this strategy to five screening projects. The results demonstrate that this method produces useful predictions on the numbers of false negatives.

scholarly journals False-Negative Rate of Gram-Stain Microscopy for Diagnosis of Septic Arthritis: Suggestions for Improvement

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Paul Stirling ◽  
Radwane Faroug ◽  
Suheil Amanat ◽  
Abdulkhaled Ahmed ◽  
Malcolm Armstrong ◽  
...  
Keyword(s):  
Synovial Fluid ◽  
Septic Arthritis ◽  
False Negative ◽  
False Negative Rate ◽  
Diagnostic Value ◽  
Royal Infirmary ◽  
Gram Stain ◽  
Data Set ◽  
Ongoing Research ◽  
Negative Rate

We quantify the false-negative diagnostic rate of septic arthritis using Gram-stain microscopy of synovial fluid and compare this to values reported in the peer-reviewed literature. We propose a method of improving the diagnostic value of Gram-stain microscopy using Lithium Heparin containers that prevent synovial fluid coagulation. Retrospective study of the Manchester Royal Infirmary microbiology database of patients undergoing synovial fluid Gram-stain and culture between December 2003 and March 2012 was undertaken. The initial cohort of 1896 synovial fluid analyses for suspected septic arthritis was reduced to 143 after exclusion criteria were applied. Analysis of our Gram-stain microscopy yielded 111 false-negative results from a cohort size of 143 positive synovial fluid cultures, giving a false-negative rate of 78%. We report a false-negative rate of Gram-stain microscopy for septic arthritis of 78%. Clinicians should therefore avoid the investigation until a statistically significant data set confirms its efficacy. The investigation's value could be improved by using Lithium Heparin containers to collect homogenous synovial fluid samples. Ongoing research aims to establish how much this could reduce the false-negative rate.

scholarly journals SARS-CoV2 Testing: The Limit of Detection Matters

2020 ◽  
Author(s):  
Ramy Arnaout ◽  
Rose A. Lee ◽  
Ghee Rye Lee ◽  
Cody Callahan ◽  
Christina F. Yen ◽  
...  
Keyword(s):  
Limit Of Detection ◽  
Diagnostic Testing ◽  
False Negative ◽  
False Negative Rate ◽  
Fold Increase ◽  
Test Results ◽  
Rt Pcr ◽  
False Negatives ◽  
Healthcare Network ◽  
Negative Rate

AbstractResolving the COVID-19 pandemic requires diagnostic testing to determine which individuals are infected and which are not. The current gold standard is to perform RT-PCR on nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of ~100 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved assays vary over 10,000-fold. Assays with higher LoDs will miss more infected patients, resulting in more false negatives. However, the false-negative rate for a given LoD remains unknown. Here we address this question using over 27,500 test results for patients from across our healthcare network tested using the Abbott RealTime SARS-CoV-2 EUA. These results suggest that each 10-fold increase in LoD is expected to increase the false negative rate by 13%, missing an additional one in eight infected patients. The highest LoDs on the market will miss a majority of infected patients, with false negative rates as high as 70%. These results suggest that choice of assay has meaningful clinical and epidemiological consequences. The limit of detection matters.

scholarly journals Outlier Mining in High Throughput Screening Experiments

2002 ◽  
Vol 7 (4) ◽  
pp. 341-351 ◽  
Author(s):  
Michael F.M. Engels ◽  
Luc Wouters ◽  
Rudi Verbeeck ◽  
Greet Vanhoof
Keyword(s):  
Data Mining ◽  
Biological Activity ◽  
High Throughput ◽  
High Throughput Screening ◽  
False Negative ◽  
Data Sets ◽  
Data Set ◽  
Structure Information ◽  
Screening Experiments ◽  
Mining Procedure

A data mining procedure for the rapid scoring of high-throughput screening (HTS) compounds is presented. The method is particularly useful for monitoring the quality of HTS data and tracking outliers in automated pharmaceutical or agrochemical screening, thus providing more complete and thorough structure-activity relationship (SAR) information. The method is based on the utilization of the assumed relationship between the structure of the screened compounds and the biological activity on a given screen expressed on a binary scale. By means of a data mining method, a SAR description of the data is developed that assigns probabilities of being a hit to each compound of the screen. Then, an inconsistency score expressing the degree of deviation between the adequacy of the SAR description and the actual biological activity is computed. The inconsistency score enables the identification of potential outliers that can be primed for validation experiments. The approach is particularly useful for detecting false-negative outliers and for identifying SAR-compliant hit/nonhit borderline compounds, both of which are classes of compounds that can contribute substantially to the development and understanding of robust SARs. In a first implementation of the method, one- and two-dimensional descriptors are used for encoding molecular structure information and logistic regression for calculating hits/nonhits probability scores. The approach was validated on three data sets, the first one from a publicly available screening data set and the second and third from in-house HTS screening campaigns. Because of its simplicity, robustness, and accuracy, the procedure is suitable for automation.

scholarly journals Artificial Neural Network—Based Analysis of High-Throughput Screening Data for Improved Prediction of Active Compounds

2009 ◽  
Vol 14 (10) ◽  
pp. 1236-1244 ◽  
Author(s):  
Swapan Chakrabarti ◽  
Stan R. Svojanovsky ◽  
Romana Slavik ◽  
Gunda I. Georg ◽  
George S. Wilson ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Back Propagation ◽  
Large Data ◽  
Classification Performance ◽  
Data Sets ◽  
Data Set ◽  
Active Compounds ◽  
Physicochemical Features ◽  
Artificial Neural

Artificial neural networks (ANNs) are trained using high-throughput screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a methionine aminopeptidases inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to nonactive compounds, R A/N, was 0.0321. Back-propagation ANNs were trained and validated using principal components derived from the physicochemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a 3-fold gain in R A/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was used to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a 10-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets.

scholarly journals The Wiggleometer: Measuring Larval Movement in a 96 Well Format

2000 ◽  
Vol 5 (2) ◽  
pp. 79-84 ◽  
Author(s):  
Fredric Solomon ◽  
Bruce Michael ◽  
Michel Hamelin ◽  
McHardy Smith
Keyword(s):  
Computer Vision ◽  
High Throughput ◽  
Dose Response ◽  
Haemonchus Contortus ◽  
High Throughput Screening ◽  
Animal Health ◽  
False Negative ◽  
False Negative Rate ◽  
Health Targets ◽  
Major Impediment

There has been relatively little progress in the area of high throughput screening for antiparasitic animal health targets, which involve whole organisms such as Haemonchus contortus and Caenorhabditis elegans. Most assays involve identifying compounds that can paralyze and/or kill the organism. A major impediment has been the lack of instrumentation suitable for automating the read-out of these assays. We have developed an automated reader that makes analysis of antiparasitic animal health assays possible. This reader uses computer vision techniques to determine whether or not there is larval motion in each well. The system has been validated by measuring the dose-response relationships for several nematocidal agents and by examining 1040 wells of H. contortus, with a 94.6%/94% concordance rate with a human reader with less than a 0.3% false negative rate.

scholarly journals A New Method with Flexible and Balanced Control of False Negatives and False Positives for Hit Selection in RNA Interference High-Throughput Screening Assays

2007 ◽  
Vol 12 (5) ◽  
pp. 645-655 ◽  
Author(s):  
Xiaohua Douglas Zhang
Keyword(s):  
Rna Interference ◽  
High Throughput Screening ◽  
False Negative ◽  
False Negative Rate ◽  
T Test ◽  
Mean Difference ◽  
Z Score ◽  
Negative Rate ◽  
Score Method ◽  
The Mean

The z-score method and its variants for testing mean difference are commonly used for hit selection in high-throughput screening (HTS) assays. Strictly standardized mean difference (SSMD) offers a way to measure and classify the short interfering RNA (siRNA) effects. In this article, based on SSMD, the authors propose a new testing method for hit selection in RNA interference (RNAi) HTS assays. This SSMD-based method allows the differentiation between siRNAs with large and small effects on the assay output and maintains flexible and balanced control of both the false-negative rate, in which the siRNAs with strong effects are not selected as hits, and the restricted false-positive rate, in which the siRNAs with weak or no effects are selected as hits. This method directly addresses the size of siRNA effects represented by the strength of difference between an siRNA and a negative reference, whereas the classic z-score method and t-test of testing no mean difference address whether the mean of an siRNA is exactly the same as the mean of a negative reference. This method can readily control the false-negative rate, whereas it is nontrivial for the classic z-score method and t-test to control the false-negative rate. Therefore, theoretically, the SSMD-based method offers better control of the sizes of siRNA effects and the associated false-positive and false-negative rates than the commonly used z-score method and t-test for hit selection in HTS assays. The SSMD-based method should generally be applicable to any assay in which the end point is a difference in signal compared to a reference sample, including those for RNAi, receptor, enzyme, and cellular function. (Journal of Biomolecular Screening 2007:645-655)

scholarly journals FAST: a Flexible, Accurate and Speedy Test for COVID-19

2020 ◽  
Author(s):  
Linjiajie Fang ◽  
Bing-Yi Jing ◽  
Shen Ling ◽  
Qing Yang
Keyword(s):  
Close Contact ◽  
False Negative ◽  
Group Testing ◽  
False Negative Rate ◽  
Rt Pcr ◽  
False Negatives ◽  
Improve Efficiency ◽  
Negative Rate ◽  
Pcr Test

AbstractAs the COVID-19 pandemic continues worldwide, there is an urgent need to detect infected patients as quickly and accurately as possible. Group testing proposed by Technion [1][2] could improve efficiency greatly. However, the false negative rate (FNR) would be doubled. Using USA as an example, group testing would have over 70,000 false negatives, compared to 35,000 false negatives by individual testing.In this paper, we propose a Flexible, Accurate and Speedy Test (FAST), which is faster and more accurate than any existing tests. FAST first forms small close contact subgroups, e.g. families and friends. It then pools subgroups to form larger groups before RT-PCR test is done. FAST needs a similar number of tests to Technion’s method, but sharply reduces the FNR to a negligible level. For example, FAST brings down the number of false negatives in USA to just 2000, and it is seven times faster than individual testing.

A novel clinical tool to estimate risk of false negative KRAS mutation in circulating tumor DNA testing.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3594-3594
Author(s):  
Stefania Napolitano ◽  
Ryan Sun ◽  
Aparna Raj Parikh ◽  
Jason Henry ◽  
Christine Megerdichian Parseghian ◽  
...  
Keyword(s):  
Kras Mutation ◽  
Posterior Probability ◽  
False Negative ◽  
False Negative Rate ◽  
Training Dataset ◽  
False Negatives ◽  
True Negative ◽  
Ras Mutations ◽  
Egfr Inhibition ◽  
Negative Rate

3594 Background: Recently, in metastatic colorectal cancer (mCRC), the detection of RAS mutations by circulating tumor (ct) DNA has recently emerged as a valid and non-invasive alternative approach, overall showing a high concordance with the standard tissue genotyping, giving information on response to EGFRi treatment and resistant mechanisms. However, RAS mutations may be missed due to low levels of any ctDNA in the blood (false-negative), and it has been difficult to distinguish this from patients without a RAS mutation in the tumor (true-negative). We propose a methodology that can be applied to multi-gene ctDNA testing panels to accurately distinguish true- and false-negative tests. Methods: 357 subjects with tissue and multi-panel ctDNA testing from MD Anderson (MDACC) were used as a training dataset and 295 subjects from Massachusetts General Hospital (MGH) dataset as the testing dataset. CtDNA panels contained between 65 and 70 genes, allowing evaluation of tumor ctDNA shedding from variant allele fraction (VAF) levels in the plasma from other genes (such as APC and TP53). Based on the relationship between KRAS and the VAFs of other gene, we established a Bayesian model providing a posterior probability of false negative in the ctDNA test, using thresholds of < 5% (low), 5-15% (medium), and > 15% (high). This model was validated on the MGH database. Results: Across both cohorts, 431 patients were ctDNA wild type for KRAS. Of those, 29 had tissue documenting a KRAS mutation for a false negative rate of 8%. The model provides the posterior probability that a KRAS mutation is indeed present in the tissue given the observed values of allele frequencies for other mutated genes in the plasma. In the validation cohort, a predicted low false negative had no false negatives (0/62, 95% CI 0%-5.8%), while a predicted medium false negative rate was associated with 3% false negative (1/32, 95% CI 0%-16%). In contrast, a high predicted false negative rate was associated with 5% false negative (5/100, 95% CI 1.6%-11%). The results demonstrate the ability of our tool to discriminate between subjects with true negative and false negatives, as a higher proportion of false negatives are observed at higher posterior probabilities. Conclusions: In conclusion, our approach provides increased confidence in KRAS ctDNA mutation testing in clinical practice, thereby facilitating the identification patients who will benefit from EGFR inhibition while reducing the risk of false negative tests. Extension of this methodology to NRAS and BRAF is possible, with clinical application enabled by a freely available online tool.

Clinically Meaningful Change

Methodology ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 97-105
Author(s):  
Rodrigo Ferrer ◽  
Antonio Pardo
Keyword(s):  
Effect Size ◽  
False Negative ◽  
False Negative Rate ◽  
Point Of View ◽  
Skewed Distribution ◽  
Effect Sizes ◽  
False Negatives ◽  
Large Size ◽  
Before And After ◽  
Post Test

Abstract. In a recent paper, Ferrer and Pardo (2014) tested several distribution-based methods designed to assess when test scores obtained before and after an intervention reflect a statistically reliable change. However, we still do not know how these methods perform from the point of view of false negatives. For this purpose, we have simulated change scenarios (different effect sizes in a pre-post-test design) with distributions of different shapes and with different sample sizes. For each simulated scenario, we generated 1,000 samples. In each sample, we recorded the false-negative rate of the five distribution-based methods with the best performance from the point of view of the false positives. Our results have revealed unacceptable rates of false negatives even with effects of very large size, starting from 31.8% in an optimistic scenario (effect size of 2.0 and a normal distribution) to 99.9% in the worst scenario (effect size of 0.2 and a highly skewed distribution). Therefore, our results suggest that the widely used distribution-based methods must be applied with caution in a clinical context, because they need huge effect sizes to detect a true change. However, we made some considerations regarding the effect size and the cut-off points commonly used which allow us to be more precise in our estimates.

Sign in / Sign up

Export Citation Format

Share Document