scholarly journals MIXTURE MODELS FOR DETECTING DIFFERENTIALLY EXPRESSED GENES IN MICROARRAYS

2006 ◽  
Vol 16 (05) ◽  
pp. 353-362 ◽  
Author(s):  
LIAT BEN-TOVIM JONES ◽  
RICHARD BEAN ◽  
GEOFFREY J. MCLACHLAN ◽  
JUSTIN XI ZHU
Keyword(s):  
Mixture Models ◽  
False Negative ◽  
False Negative Rate ◽  
Multiple Hypothesis Testing ◽  
Differentially Expressed ◽  
Data Set ◽  
False Discovery ◽  
Mixture Model Approach ◽  
Number Of Classes ◽  
Selection Of

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.

Using mixture models to detect differentially expressed genes

2005 ◽  
Vol 45 (8) ◽  
pp. 859 ◽  
Author(s):  
G. J. McLachlan ◽  
R. W. Bean ◽  
L. Ben-Tovim Jones ◽  
J. X. Zhu
Keyword(s):  
False Discovery Rate ◽  
Mixture Models ◽  
False Negative ◽  
False Negative Rate ◽  
Multiple Hypothesis Testing ◽  
Differentially Expressed ◽  
False Discovery ◽  
Mixture Model Approach ◽  
Number Of Classes ◽  
Selection Of

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local false discovery rate is provided for each gene, and it can be implemented so that the implied global false discovery rate is bounded as with the Benjamini-Hochberg methodology based on tail areas. The latter procedure is too conservative, unless it is modified according to the prior probability that a gene is not differentially expressed. An attractive feature of the mixture model approach is that it provides a framework for the estimation of this probability and its subsequent use in forming a decision rule. The rule can also be formed to take the false negative rate into account.

“Hold them or fold them:” evidence-based decisions to discontinue investigations of non-domestic minor violence

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kent McFadzien ◽  
Lawrence W. Sherman
Keyword(s):  
Design Methodology ◽  
False Negative ◽  
False Negative Rate ◽  
Random Selection ◽  
Content Type ◽  
Continuous Basis ◽  
Randomised Controlled ◽  
Practical Implications ◽  
Full Investigation ◽  
Selection Of

PurposeThe purpose of this paper is to demonstrate a “maintenance pathway” for ensuring a low false negative rate in closing investigations unlikely to lead to a clearance (detection).Design/methodology/approachA randomised controlled experiment testing solvability factors for non-domestic cases of minor violence.FindingsA random selection of 788 cases, of which 428 would have been screened out, were sent forward for full investigation. The number of cases actually detected was 22. A total of 19 of these were from the 360 recommended for allocation. This represents an improvement of accuracy over the original tests of the model three years earlier.Research limitations/implicationsThis study shows how the safety of an investigative triage tool can be checked on a continuous basis for accuracy in predicting the cases unlikely to be solved if referred for full investigations.Practical implicationsThis safety check pathway means that many more cases can be closed after preliminary investigations, thus saving substantial time for working on cases more likely to yield a detection if sufficient time is put into the cases.Social implicationsMore offenders may be caught and brought to justice by using triage with a safety backstop for accurate forecasting.Originality/valueThis is the first published study of a maintenance pathway based on a random selection of cases that would otherwise not have been investigated. If widely applied, it could yield far greater time for police to pursue high-harm, serious violence.

One- year mortality in patients with advanced hepatocellular carcinoma on immunotherapy: Prediction using machine learning models (Preprint)

2021 ◽  
Author(s):  
Thomas Ka-Luen Lui ◽  
Ka Shing, Michael Cheung ◽  
Wai Keung Leung
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Characteristic Curve ◽  
False Negative ◽  
False Negative Rate ◽  
Absolute Error ◽  
Advanced Hepatocellular Carcinoma ◽  
Data Set ◽  
One Year ◽  
Related Mortality

BACKGROUND Immunotherapy is a new promising treatment for patients with advanced hepatocellular carcinoma (HCC), but is costly and potentially associated with considerable side effects. OBJECTIVE This study aimed to evaluate the role of machine learning (ML) models in predicting the one-year cancer-related mortality in advanced HCC patients treated with immunotherapy METHODS 395 HCC patients who had received immunotherapy (including nivolumab, pembrolizumab or ipilimumab) in 2014 - 2019 in Hong Kong were included. The whole data set were randomly divided into training (n=316) and validation (n=79) set. The data set, including 45 clinical variables, was used to construct six different ML models in predicting the risk of one-year mortality. The performances of ML models were measured by the area under receiver operating characteristic curve (AUC) and the mean absolute error (MAE) using calibration analysis. RESULTS The overall one-year cancer-related mortality was 51.1%. Of the six ML models, the random forest (RF) has the highest AUC of 0.93 (95%CI: 0.86-0.98), which was better than logistic regression (0.82, p=0.01) and XGBoost (0.86, p=0.04). RF also had the lowest false positive (6.7%) and false negative rate (2.8%). High baseline AFP, bilirubin and alkaline phosphatase were three common risk factors identified by all ML models. CONCLUSIONS ML models could predict one-year cancer-related mortality of HCC patients treated with immunotherapy, which may help to select patients who would most benefit from this new treatment option.

scholarly journals An Effective Method for Controlling False Discovery and False Nondiscovery Rates in Genome-Scale RNAi Screens

2010 ◽  
Vol 15 (9) ◽  
pp. 1116-1122 ◽  
Author(s):  
Xiaohua Douglas Zhang
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
False Positives ◽  
P Value ◽  
Statistical Control ◽  
Small Interfering Rnas ◽  
False Negatives ◽  
Activation Effect ◽  
False Discovery ◽  
Genome Scale

In most genome-scale RNA interference (RNAi) screens, the ultimate goal is to select siRNAs with a large inhibition or activation effect. The selection of hits typically requires statistical control of 2 errors: false positives and false negatives. Traditional methods of controlling false positives and false negatives do not take into account the important feature in RNAi screens: many small-interfering RNAs (siRNAs) may have very small but real nonzero average effects on the measured response and thus cannot allow us to effectively control false positives and false negatives. To address for deficiencies in the application of traditional approaches in RNAi screening, the author proposes a new method for controlling false positives and false negatives in RNAi high-throughput screens. The false negatives are statistically controlled through a false-negative rate (FNR) or false nondiscovery rate (FNDR). FNR is the proportion of false negatives among all siRNAs examined, whereas FNDR is the proportion of false negatives among declared nonhits. The author also proposes new concepts, q*-value and p*-value, to control FNR and FNDR, respectively. The proposed method should have broad utility for hit selection in which one needs to control both false discovery and false nondiscovery rates in genome-scale RNAi screens in a robust manner.

Correlation of decreased expression of PD-L1 on circulating tumor cells and clinical benefit in SCLC Patients treated with RRx-001, a CD47 downregulator, in a phase II trial.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 9062-9062
Author(s):  
Corey Carter ◽  
Yusuke Tomita ◽  
Akira Yuno ◽  
Jonathan Baker ◽  
Min-Jung Lee ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Clinical Benefit ◽  
Logistic Model ◽  
Goodness Of Fit ◽  
False Negative ◽  
False Negative Rate ◽  
Cycle Duration ◽  
Data Set ◽  
Platinum Doublet

9062 Background: In a Phase 2 trial called QUADRUPLE THREAT (QT) (NCT02489903), where 2nd line+ small cell lung cancer (SCLC) patients were treated with RRx-001 and a platinum doublet, the programmed death-ligand 1 (PD-L1) status of circulating tumor cells (CTCs) in 14 patient samples was evaluated. Methods: 26 consented patients received weekly RRx-001 4 mg followed by a reintroduced platinum doublet; epithelial cell adhesion molecule (EPCAM+) CTCs from 10 ml of blood on two consecutive timepoints cycle 1 day 1 and cycle 3 day 8 (cycle duration = 1 week) were detected by EpCAM-based immunomagnetic capture and flow cytometric analysis. CTCs were further characterized for protein expression of PD-L1. Tumor response was classified as partial or complete response based on the response evaluation criteria in solid tumors (RECISTv1.1) measured every 6 weeks. Results: The analyzed clinical data set comprised 14 RECIST-evaluable patients. 50% were females (7/14) and the median age (years) at baseline was 64.5 (Min = 48.5, Max = 84.2, SD = 10.3). The logistic model McFadden goodness of fit score (0 to 100) is 0.477, which is a strong correlation value. The logistic model analyzing the association of CTC PD-L1 expression at two timepoints and response had an approximate 92.8% accuracy in its prediction of clinical benefit (SD/PR/CR). Accuracy is defined in the standard way as 1- (False positive rate + False negative rate). The estimated ROC displayed in Figure 1 suggests a ROC AUC of 0.93 (95% CI: 0.78, 0.99), an excellent measure of performance. Conclusions: Reduction of PD-L1 expression was correlated with good clinical outcome after RRx-001 + platinum doublet treatment. PD-L1 expression reduction in favor of RRx-001 RECIST clinical benefit was clinically significant as compared to non-responders with progressive disease (PD). In the ongoing SCLC Phase 3 study called REPLATINUM (NCT03699956), analyses are planned to correlate response and survival with expression of CD47 and PD-L1 on CTCs. Clinical trial information: NCT02489903.

Tuning IMS station processing parameters and detection thresholds to increase detection precision and decrease detection miss rate

2020 ◽  
Author(s):  
Christos Saragiotis ◽  
Ivan Kitov
Keyword(s):  
False Discovery Rate ◽  
Binary Classification ◽  
False Negative ◽  
Detection Threshold ◽  
False Negative Rate ◽  
Processing Parameters ◽  
Detection Algorithm ◽  
Detection Scheme ◽  
Statistical Measures ◽  
False Discovery

<p>Two principal performance measures of the International Monitoring System (IMS) stations detection capability are the rate of automatic detections associated with events in the Reviewed Event Bulletin (REB) and the rate of detections manually added to the REB. These two metrics roughly correspond to the precision (which is the complement of the false-discovery rate) and miss rate or false-negative rate statistical measures of a binary classification test, respectively. The false-discovery and miss rates are clearly significantly influenced by the number of phases detected by the detection algorithm, which in turn depends on prespecified slowness-, frequency- and azimuth- dependent threshold values used in the short-term average over long-term average ratio detection scheme of the IMS stations. In particular, the lower the threshold, the more the detections and therefore the lower the miss rate but the higher the false discovery rate; the higher the threshold, the less the detections and therefore the higher the miss rate but also the lower the false discovery rate. In that sense decreasing both the false-discovery rate and the miss rate are conflicting goals that need to be balanced. On one hand, it is essential that the miss rate is as low as possible since no nuclear explosion should go unnoticed by the IMS. On the other hand, a high false-discovery rate compromises the quality of the automatically generated event lists and adds heavy and unnecessary workload to the seismic analysts during the interactive processing stage.</p><p>A previous study concluded that a way to decrease both the miss and false-discovery rates as well as the analyst workload is to increase the retiming interval, i.e., the maximum allowable time that an analyst is allowed to move an arrival pick without having to declare a new arrival. Indeed, when a detection needs to be moved by an interval larger than the retiming interval, not only is this a much more time-consuming task for the analyst than just retiming it, but it also affects negatively both the associated rate (the automatic detection is deleted and therefore not associated to an event) and the added rate (a new arrival has to be added to arrival list). The International Data Centre has increased the retiming interval from 4 s to 10 s since October 2018. We show how this change affected the associated-detections and added-detections rates and how the values of these metrics can be further improved by tuning the detection threshold levels.</p>

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 Using an artificial neural network to predict traumatic brain injury

2019 ◽  
Vol 23 (2) ◽  
pp. 219-226 ◽  
Author(s):  
Andrew T. Hale ◽  
David P. Stonko ◽  
Jaims Lim ◽  
Oscar D. Guillamondegui ◽  
Chevis N. Shannon ◽  
...  
Keyword(s):  
Neural Network ◽  
Traumatic Brain Injury ◽  
Artificial Neural Network ◽  
Brain Injury ◽  
False Negative ◽  
False Negative Rate ◽  
Research Network ◽  
Direct Result ◽  
Data Set ◽  
Artificial Neural

OBJECTIVEPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling in patients who will have a clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based way to safely discharge children who are at low risk for a CRTBI. The authors hypothesized that an artificial neural network (ANN) trained on clinical and radiologist-interpreted imaging metrics could provide a tool for identifying patients likely to suffer from a CRTBI.METHODSThe authors used the prospectively collected, publicly available, multicenter Pediatric Emergency Care Applied Research Network (PECARN) TBI data set. All patients under the age of 18 years with TBI and admission head CT imaging data were included. The authors constructed an ANN using clinical and radiologist-interpreted imaging metrics in order to predict a CRTBI, as previously defined by PECARN: 1) neurosurgical procedure, 2) intubation > 24 hours as direct result of the head trauma, 3) hospitalization ≥ 48 hours and evidence of TBI on a CT scan, or 4) death due to TBI.RESULTSAmong 12,902 patients included in this study, 480 were diagnosed with CRTBI. The authors’ ANN had a sensitivity of 99.73% with precision of 98.19%, accuracy of 97.98%, negative predictive value of 91.23%, false-negative rate of 0.0027%, and specificity for CRTBI of 60.47%. The area under the receiver operating characteristic curve was 0.9907.CONCLUSIONSThe authors are the first to utilize artificial intelligence to predict a CRTBI in a clinically meaningful manner, using radiologist-interpreted CT information, in order to identify pediatric patients likely to suffer from a CRTBI. This proof-of-concept study lays the groundwork for future studies incorporating iterations of this algorithm directly into the electronic medical record for real-time, data-driven predictive assistance to physicians.

scholarly journals GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel L. Cameron ◽  
Jonathan Baber ◽  
Charles Shale ◽  
Jose Espejo Valle-Inclan ◽  
Nicolle Besselink ◽  
...  
Keyword(s):  
Copy Number ◽  
Structural Variation ◽  
False Negative ◽  
False Negative Rate ◽  
Structural Variants ◽  
Structural Variant ◽  
Complex Rearrangement ◽  
False Discovery ◽  
Copy Number Changes ◽  
Paired End Sequencing

AbstractGRIDSS2 is the first structural variant caller to explicitly report single breakends—breakpoints in which only one side can be unambiguously determined. By treating single breakends as a fundamental genomic rearrangement signal on par with breakpoints, GRIDSS2 can explain 47% of somatic centromere copy number changes using single breakends to non-centromere sequence. On a cohort of 3782 deeply sequenced metastatic cancers, GRIDSS2 achieves an unprecedented 3.1% false negative rate and 3.3% false discovery rate and identifies a novel 32–100 bp duplication signature. GRIDSS2 simplifies complex rearrangement interpretation through phasing of structural variants with 16% of somatic calls phasable using paired-end sequencing.

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