scholarly journals Look4TRs: A de-novo tool for detecting simple tandem repeats using self-supervised hidden Markov models

2018 ◽  
Author(s):  
Alfredo Velasco ◽  
Benjamin T. James ◽  
Vincent D. Wells ◽  
Hani Z. Girgis
Keyword(s):  
Hidden Markov Models ◽  
False Positive ◽  
Tandem Repeats ◽  
Markov Models ◽  
De Novo ◽  
Hidden Markov ◽  
False Positive Rate ◽  
High Sensitivity ◽  
Positive Rate ◽  
Simple Tandem Repeats

ABSTRACTSimple tandem repeats, microsatellites in particular, have regulatory functions, links to several diseases, and applications in biotechnology. Sequences of thousands of species will be available soon. There is immediate need for an accurate tool for detecting microsatellites in the new genomes. The current available tools have limitations. As a remedy, we proposed Look4TRs, which is the first application of self-supervised hidden Markov models to discovering microsatellites. It adapts itself to the input genomes, balancing high sensitivity and low false positive rate. It auto-calibrates itself, freeing the user from adjusting the parameters manually, leading to consistent results across different studies. We evaluated Look4TRs on eight genomes. Based on F-measure, which combines sensitivity and false positive rate, Look4TRs outperformed TRF and MISA — the most widely-used tools — by 106% and 82%. Look4TRs outperformed the second best tool, MsDetector or Tantan, by 11%. Look4TRs represents technical advances in the annotation of microsatellites.

scholarly journals Look4TRs: A de-novo tool for detecting simple tandem repeats using self-supervised hidden Markov models

2019 ◽  
Author(s):  
Alfredo Velasco ◽  
Benjamin T James ◽  
Vincent D Wells ◽  
Hani Z Girgis
Keyword(s):  
Hidden Markov Models ◽  
False Positive ◽  
Tandem Repeats ◽  
Markov Models ◽  
Hidden Markov ◽  
False Positive Rate ◽  
Supplementary Information ◽  
The Third ◽  
Positive Rate ◽  
Simple Tandem Repeats

Abstract Motivation Simple tandem repeats, microsatellites in particular, have regulatory functions, links to several diseases, and applications in biotechnology. There is an immediate need for an accurate tool for detecting microsatellites in newly sequenced genomes. The current available tools are either sensitive or specific but not both; some tools require adjusting parameters manually. Results We propose Look4TRs, the first application of self-supervised hidden Markov models to discovering microsatellites. Look4TRs adapts itself to the input genomes, balancing high sensitivity and low false positive rate. It auto-calibrates itself. We evaluated Look4TRs on 26 eukaryotic genomes. Based on F measure, which combines sensitivity and false positive rate, Look4TRs outperformed TRF and MISA —the most widely-used tools—by 78% and 84%. Look4TRs outperformed the second and the third best tools, MsDetector and Tantan by 17% and 34%. On eight bacterial genomes, Look4TRs outperformed the second and the third best tools by 27% and 137%. Availability https://github.com/TulsaBioinformaticsToolsmith/Look4TRs Supplementary information Supplementary data are available at Bioinformatics online and on https://drive.google.com/open?id=1cIcS7Gvj0wj1B81-rnTU_OAG3IiNH54Y.

The False Positive Rate of Transcutaneous Tissue Oximetry Alarms in Microvascular Breast Reconstruction Rises after 24 Hours

2020 ◽  
Author(s):  
Phu C. Tran ◽  
Will DeBrock ◽  
Mary E. Lester ◽  
Brett C. Hartman ◽  
Juan Socas ◽  
...  
Keyword(s):  
Breast Reconstruction ◽  
Postoperative Period ◽  
False Positive ◽  
False Positive Rate ◽  
High Sensitivity ◽  
Flap Loss ◽  
Vascular Compromise ◽  
Positive Rate ◽  
Tissue Oximetry ◽  
Salvage Rate

Abstract Background Transcutaneous tissue oximetry is widely used as an adjunct for postoperative monitoring after microvascular breast reconstruction. Despite a high sensitivity at detecting vascular issues, alarms from probe malfunctions/errors can generate unnecessary nursing calls, concerns, and evaluations. The purpose of this study is to analyze the false positive rate of transcutaneous tissue oximetry monitoring over the postoperative period and assess changes in its utility over time. Patients and Methods Consecutive patients undergoing microvascular breast reconstruction at our institution with monitoring using transcutaneous tissue oximetry were assessed between 2017 and 2019. Variables of interest were transcutaneous tissue oximetry alarms, flap loss, re-exploration, and salvage rates. Results The study included 175 patients (286 flaps). The flap loss rate was 1.0% (3/286). Twelve patients (6.8%) required re-exploration, with 9 patients found to have actual flap compromise (all within 24 hours). The salvage rate was 67.0%. The 3 takebacks after 24 hours were for bleeding concerns rather than anastomotic problems. Within the initial 24-hour postoperative period, 43 tissue oximetry alarms triggered nursing calls; 7 alarms (16.2%) were confirmed to be for flap issues secondary to vascular compromise. After 24 hours, none of the 44 alarms were associated with flap compromise. The false positive rate within 24 hours was 83.7% (36/43) compared with 100% (44/44) after 24 hours (p = 0.01). Conclusion The transcutaneous tissue oximetry false positive rate significantly rises after 24 hours. The benefit may not outweigh the concerns, labor, and effort that results from alarms after postoperative day 1. We recommend considering discontinuing this monitoring after 24 hours.

scholarly journals HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models

2009 ◽  
Vol 10 (S1) ◽  
Author(s):  
Sabah Kadri ◽  
Veronica Hinman ◽  
Panayiotis V Benos
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
De Novo ◽  
Hidden Markov ◽  
Hierarchical Hidden Markov Models

scholarly journals PCR evaluation of false-positive signals from two automated blood-culture systems

2006 ◽  
Vol 55 (1) ◽  
pp. 53-57 ◽  
Author(s):  
Z. Ceren Karahan ◽  
Ipek Mumcuoglu ◽  
Haluk Guriz ◽  
Deniz Tamer ◽  
Neriman Balaban ◽  
...  
Keyword(s):  
Blood Culture ◽  
False Positive ◽  
Pasteurella Multocida ◽  
False Positive Rate ◽  
High Sensitivity ◽  
Becton Dickinson ◽  
Specimen Handling ◽  
Culture Systems ◽  
Positive Rate ◽  
Diagnostic Microbiology

Rapid detection of micro-organisms from blood is one of the most critical functions of a diagnostic microbiology laboratory. Automated blood-culture systems reduce the time needed to detect positive cultures, and reduce specimen handling. The false-positive rate of such systems is 1–10 %. In this study, the presence of pathogens in ‘false-positive’ bottles obtained from BACTEC 9050 (Becton Dickinson) and BacT/Alert (Biomérieux) systems was investigated by eubacterial and fungal PCR. A total of 169 subculture-negative aerobic blood-culture bottles (104 BacT/Alert and 65 BACTEC) were evaluated. Both fungal and eubacterial PCRs were negative for all BACTEC bottles. Fungal PCR was also negative for the BacT/Alert system, but 10 bottles (9·6 %) gave positive results by eubacterial PCR. Sequence analysis of the positive PCR amplicons indicated the presence of the following bacteria (number of isolates in parentheses): Pasteurella multocida (1), Staphylococcus epidermidis (2), Staphylococcus hominis (1), Micrococcus sp. (1), Streptococcus pneumoniae (1), Corynebacterium spp. (2), Brachibacterium sp. (1) and Arthrobacter/Rothia sp. (1). Antibiotic usage by the patients may be responsible for the inability of the laboratory to grow these bacteria on subcultures. For patients with more than one false-positive bottle, molecular methods can be used to evaluate the microbial DNA in these bottles. False positives from the BACTEC system may be due to elevated patient leukocyte counts or the high sensitivity of the system to background increases in CO2 concentration.

DDoS Detection and Prevention Based on Joint Entropy and Conditional Entropy

2011 ◽  
Vol 474-476 ◽  
pp. 2129-2133
Author(s):  
Yong Hao Gu ◽  
Wei Ming Wu
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Conditional Entropy ◽  
High Sensitivity ◽  
Joint Entropy ◽  
Positive Rate ◽  
Single Attribute ◽  
Ddos Detection ◽  
The Stability

Distributed Denial of Service (DDoS) imposes a very serious threat to the stability of the Internet. Compared with many detection approaches, detecting DDoS attacks based on entropy has advantages such as simplicity, high sensitivity and low false positive rate. But the method with single attribute entropy has high false positive rate when detecting attribute forged attacks. This paper presents a detecting method based on joint entropy and a filtering way based on conditional entropy. The efficiency of this scheme is validated with simulation on the research lab network.

scholarly journals Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers

2020 ◽  
Vol 9 (12) ◽  
pp. 3908
Author(s):  
Jungheum Cho ◽  
Jihang Kim ◽  
Kyong Joon Lee ◽  
Chang Mo Nam ◽  
Sung Hyun Yoon ◽  
...  
Keyword(s):  
Lung Cancer ◽  
False Positive ◽  
False Positive Rate ◽  
High Sensitivity ◽  
Lung Cancers ◽  
Ct Screening ◽  
National Lung Screening Trial ◽  
Positive Rate ◽  
Lung Screening ◽  
Lung Ct

We aimed to analyse the CT examinations of the previous screening round (CTprev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CTprev in participants with incidence lung cancer, and a DL-CAD analysed CTprev according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers. As per the NLST criteria, 88% (100/113) of CTprev were positive and 74 of them had missed lung cancers. The DL-CAD reported 98% (98/100) of the positive screens as positive and detected 95% (70/74) of the missed lung cancers. As per the Lung-RADS classification, 82% (93/113) of CTprev were positive and 60 of them had missed lung cancers. The DL-CAD reported 97% (90/93) of the positive screens as positive and detected 98% (59/60) of the missed lung cancers. The DL-CAD made false positive calls in 10.3% (27/263) of controls, with 0.16 false positive nodules per scan (41/263). In conclusion, the majority of CTprev in participants with incidence lung cancers had missed lung cancers, and the DL-CAD detected them with high sensitivity and a limited false positive rate.

scholarly journals Filtering de novo indels in parent-offspring trios

2020 ◽  
Vol 21 (S16) ◽  
Author(s):  
Yongzhuang Liu ◽  
Jian Liu ◽  
Yadong Wang
Keyword(s):  
False Positive ◽  
De Novo ◽  
False Positive Rate ◽  
Gradient Boosting ◽  
Whole Genome ◽  
Sequencing Data ◽  
Clinical Practices ◽  
Computational Approaches ◽  
Exome Sequencing Data ◽  
Positive Rate

Abstract Background Identification of de novo indels from whole genome or exome sequencing data of parent-offspring trios is a challenging task in human disease studies and clinical practices. Existing computational approaches usually yield high false positive rate. Results In this study, we developed a gradient boosting approach for filtering de novo indels obtained by any computational approaches. Through application on the real genome sequencing data, our approach showed it could significantly reduce the false positive rate of de novo indels without a significant compromise on sensitivity. Conclusions The software DNMFilter_Indel was written in a combination of Java and R and freely available from the website at https://github.com/yongzhuang/DNMFilter_Indel.

scholarly journals A new algorithm to train hidden Markov models for biological sequences with partial labels

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jiefu Li ◽  
Jung-Youn Lee ◽  
Li Liao
Keyword(s):  
Active Learning ◽  
Hidden Markov Models ◽  
Markov Models ◽  
De Novo ◽  
Sequence Data ◽  
Hidden Markov ◽  
Biological Sequences ◽  
Training Method ◽  
Functional Protein ◽  
Ongoing Research

Abstract Background Hidden Markov models (HMM) are a powerful tool for analyzing biological sequences in a wide variety of applications, from profiling functional protein families to identifying functional domains. The standard method used for HMM training is either by maximum likelihood using counting when sequences are labelled or by expectation maximization, such as the Baum–Welch algorithm, when sequences are unlabelled. However, increasingly there are situations where sequences are just partially labelled. In this paper, we designed a new training method based on the Baum–Welch algorithm to train HMMs for situations in which only partial labeling is available for certain biological problems. Results Compared with a similar method previously reported that is designed for the purpose of active learning in text mining, our method achieves significant improvements in model training, as demonstrated by higher accuracy when the trained models are tested for decoding with both synthetic data and real data. Conclusions A novel training method is developed to improve the training of hidden Markov models by utilizing partial labelled data. The method will impact on detecting de novo motifs and signals in biological sequence data. In particular, the method will be deployed in active learning mode to the ongoing research in detecting plasmodesmata targeting signals and assess the performance with validations from wet-lab experiments.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

2002 ◽  
Vol 41 (01) ◽  
pp. 37-41 ◽  
Author(s):  
S. Shung-Shung ◽  
S. Yu-Chien ◽  
Y. Mei-Due ◽  
W. Hwei-Chung ◽  
A. Kao
Keyword(s):  
Acute Appendicitis ◽  
False Positive ◽  
False Positive Rate ◽  
Accurate Method ◽  
Clinical Findings ◽  
Pathological Findings ◽  
Lower Quadrant ◽  
Predictive Values ◽  
Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

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