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  1. Article: Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra.

    Halloran, John T / Rocke, David M

    Advances in neural information processing systems

    2019  Volume 30, Page(s) 5724–5733

    Abstract: Tandem mass spectrometry ( ...

    Abstract Tandem mass spectrometry (
    Language English
    Publishing date 2019-11-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1012320-9
    ISSN 1049-5258
    ISSN 1049-5258
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra.

    Halloran, John T / Rocke, David M

    Advances in neural information processing systems

    2019  Volume 31, Page(s) 5420–5430

    Abstract: The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra representative of ... ...

    Abstract The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra representative of the
    Language English
    Publishing date 2019-11-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1012320-9
    ISSN 1049-5258
    ISSN 1049-5258
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Transnasal Esophagoscopy-Our Experience.

    Rocke, John / Ahmed, Shadaba

    International archives of otorhinolaryngology

    2018  Volume 23, Issue 1, Page(s) 7–11

    Abstract: ... ...

    Abstract Introduction
    Language English
    Publishing date 2018-10-11
    Publishing country Brazil
    Document type Journal Article
    ZDB-ID 2578584-9
    ISSN 1809-4864 ; 1809-9777
    ISSN (online) 1809-4864
    ISSN 1809-9777
    DOI 10.1055/s-0038-1661359
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

    Halloran, John T / Rocke, David M

    Journal of proteome research

    2018  Volume 17, Issue 5, Page(s) 1978–1982

    Abstract: Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary ... ...

    Abstract Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l
    MeSH term(s) Algorithms ; Databases, Protein ; Machine Learning ; Proteomics/methods ; Software ; Support Vector Machine ; Time Factors
    Language English
    Publishing date 2018-04-06
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2078618-9
    ISSN 1535-3907 ; 1535-3893
    ISSN (online) 1535-3907
    ISSN 1535-3893
    DOI 10.1021/acs.jproteome.7b00767
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    Zs.A 6189: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Recurrent tonsillitis and parental perceptions of tonsillectomy during the COVID-19 pandemic.

    Heward, Elliot / Rocke, John / Kumar, Nirmal / Izzat, Steve

    International journal of pediatric otorhinolaryngology

    2020  Volume 139, Page(s) 110463

    Abstract: Objectives: The coronavirus outbreak has triggered the implementation of nationwide social distancing measures. We aimed to investigate the impact on patients with recurrent tonsillitis and parental perceptions towards tonsillectomy during the COVID-19 ... ...

    Abstract Objectives: The coronavirus outbreak has triggered the implementation of nationwide social distancing measures. We aimed to investigate the impact on patients with recurrent tonsillitis and parental perceptions towards tonsillectomy during the COVID-19 pandemic.
    Methods: A telephone questionnaire was conducted for all children awaiting tonsillectomy for recurrent tonsillitis after social distancing for 2 months at our centre. The COVID-19 lockdown period was compared with the 2 months prior to lockdown.
    Results: Forty-four children had been social distancing at home during lockdown. There was a significant reduction in tonsillitis episodes during the 2-month lockdown period in comparison with 2 months prior to lockdown (p = 0.0001). In 70% (n = 31) of cases parents wanted their child's tonsillectomy during the coronavirus outbreak.
    Conclusion: These findings demonstrate that viral exposure is a key factor in the pathophysiology of recurrent tonsillitis and that social distancing measures can reduce the frequency of recurrent tonsillitis. Despite the overall reduction in tonsillitis frequency during the lockdown period, the majority of parents wanted their child's tonsillectomy during the coronavirus outbreak. This demonstrates the impact tonsillitis has on the patient and their family's quality of life.
    MeSH term(s) Adolescent ; Attitude to Health ; COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/psychology ; Child ; Child, Preschool ; Cross-Sectional Studies ; Female ; Health Care Surveys ; Humans ; Infant ; Male ; Pandemics ; Parents/psychology ; Perception ; Physical Distancing ; Protective Factors ; Quality of Life ; Recurrence ; Retrospective Studies ; Risk Factors ; Tonsillectomy/psychology ; Tonsillitis/etiology ; Tonsillitis/prevention & control ; Tonsillitis/surgery ; United Kingdom/epidemiology
    Keywords covid19
    Language English
    Publishing date 2020-10-23
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 754501-0
    ISSN 1872-8464 ; 0165-5876
    ISSN (online) 1872-8464
    ISSN 0165-5876
    DOI 10.1016/j.ijporl.2020.110463
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1507: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 134: Show issues

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  6. Article ; Online: Is loss of sense of smell a diagnostic marker in COVID-19: A systematic review and meta-analysis.

    Rocke, John / Hopkins, Claire / Philpott, Carl / Kumar, Nirmal

    Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery

    2020  Volume 45, Issue 6, Page(s) 914–922

    Abstract: Aims: To systematically review the currently available evidence investigating the association between olfactory dysfunction (OD) and the novel coronavirus (COVID-19). To analyse the prevalence of OD in patients who have tested positive on polymerase ... ...

    Abstract Aims: To systematically review the currently available evidence investigating the association between olfactory dysfunction (OD) and the novel coronavirus (COVID-19). To analyse the prevalence of OD in patients who have tested positive on polymerase chain reaction (PCR) for COVID-19. To perform a meta-analysis of patients presenting with olfactory dysfunction, during the pandemic, and to investigate the positive predictive value for a COVID-19-positive result in this population. To assess whether olfactory dysfunction could be used as a diagnostic marker for COVID-19 positivity and aid public health approaches in tackling the current outbreak.
    Methods: We systematically searched MedLine (PubMed), Embase, Health Management Information Consortium (HMIC), Medrxiv, the Cochrane Library, the Cochrane COVID-19 Study Register, NIHR Dissemination centre, Clinical Evidence, National Health Service Evidence and the National Institute of Clinical Excellence to identify the current published evidence which associates coronaviridae or similar RNA viruses with anosmia. The initial search identified 157 articles. A total of 145 papers were excluded following application of our exclusion criteria. The 12 remaining articles that presented evidence on the association between COVID-19 and olfactory dysfunction were critically analysed.
    Results: Olfactory dysfunction has been shown to be the strongest predictor of COVID-19 positivity when compared to other symptoms in logistic regression analysis. In patients who had tested positive for COVID-19, there was a prevalence of 62% of OD. In populations of patients who are currently reporting OD, there is a positive predictive value of 61% for a positive COVID-19 result.
    Conclusion: Our review has shown that there is already significant evidence which demonstrates an association between OD and the novel coronavirus-COVID-19. It is unclear if this finding is unique to this coronavirus as individual viral phenotypes rarely present in such concentrated large numbers. We have demonstrated that OD is comparatively more predictive for COVID-19 positivity compared to other associated symptoms. We recommend that people who develop OD during the pandemic should be self-isolate and this guidance should be adopted internationally to prevent transmission.
    MeSH term(s) COVID-19/complications ; COVID-19/diagnosis ; COVID-19 Nucleic Acid Testing ; Humans ; Olfaction Disorders/diagnosis ; Olfaction Disorders/epidemiology ; Olfaction Disorders/virology ; Predictive Value of Tests ; Prevalence ; Reverse Transcriptase Polymerase Chain Reaction
    Keywords covid19
    Language English
    Publishing date 2020-09-16
    Publishing country England
    Document type Journal Article ; Meta-Analysis ; Systematic Review
    ZDB-ID 2205891-6
    ISSN 1749-4486 ; 1749-4478 ; 0307-7772 ; 1365-2273
    ISSN (online) 1749-4486
    ISSN 1749-4478 ; 0307-7772 ; 1365-2273
    DOI 10.1111/coa.13620
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1309: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Book ; Online: GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification

    Halloran, John T. / Rocke, David M.

    2020  

    Abstract: One of the most efficient methods to solve L2-regularized primal problems, such as logistic regression and linear support vector machine (SVM) classification, is the widely used trust region Newton algorithm, TRON. While TRON has recently been shown to ... ...

    Abstract One of the most efficient methods to solve L2-regularized primal problems, such as logistic regression and linear support vector machine (SVM) classification, is the widely used trust region Newton algorithm, TRON. While TRON has recently been shown to enjoy substantial speedups on shared-memory multi-core systems, exploiting graphical processing units (GPUs) to speed up the method is significantly more difficult, owing to the highly complex and heavily sequential nature of the algorithm. In this work, we show that using judicious GPU-optimization principles, TRON training time for different losses and feature representations may be drastically reduced. For sparse feature sets, we show that using GPUs to train logistic regression classifiers in LIBLINEAR is up to an order-of-magnitude faster than solely using multithreading. For dense feature sets--which impose far more stringent memory constraints--we show that GPUs substantially reduce the lengthy SVM learning times required for state-of-the-art proteomics analysis, leading to dramatic improvements over recently proposed speedups. Furthermore, we show how GPU speedups may be mixed with multithreading to enable such speedups when the dataset is too large for GPU memory requirements; on a massive dense proteomics dataset of nearly a quarter-billion data instances, these mixed-architecture speedups reduce SVM analysis time from over half a week to less than a single day while using limited GPU memory.

    Comment: 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada
    Keywords Computer Science - Machine Learning ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Quantitative Biology - Quantitative Methods ; Statistics - Machine Learning
    Subject code 004
    Publishing date 2020-08-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Transnasal Esophagoscopy—Our Experience

    John Rocke / Shadaba Ahmed

    International Archives of Otorhinolaryngology, Vol 23, Iss 01, Pp 007-

    2019  Volume 011

    Abstract: Abstract Introduction Transnasal esophagoscopy (TNE) is a widely used tool both in the diagnosis and treatment of patients presenting complaints within the head and the neck. This is because this investigative adjunct examination provides the advantage ... ...

    Abstract Abstract Introduction Transnasal esophagoscopy (TNE) is a widely used tool both in the diagnosis and treatment of patients presenting complaints within the head and the neck. This is because this investigative adjunct examination provides the advantage of visualizing above the level of the cricopharyngeus muscle when compared to the more widely used esophagogastroduodenoscopy (EGD). Objectives We have assessed if the implementation of TNE within a district general hospital (DGH) was feasible, and investigated if the resources of our patients could be better directed away from other investigations such as barium swallow and EGD in favor of this novel technique. The TNE technique has been largely applied in central teaching hospitals within the United Kingdom, but there are still no published reports of a DGH investigating its applicability in this smaller-sized clinical environment. Method We have analyzed our theater database to find all the patients who had undergone TNE, and recorded their reason for presenting, the preceding investigations, and the procedural findings. Results In most cases, the TNE was conducted without technical issues, and we were able to identify positive findings in 43% of the patients who underwent Esophagogastroduodenoscopy (EGD). We were able to treat patients successfully during the investigation when a cricopharyngeal stricture or narrowing was found. A normal EGD did not preclude further investigations with TNE. All but one of our patients were treated as day-case procedures. Conclusion Transnasal esophagoscopy can be successfully delivered within a DGH. A previous EGD does not mean that the TNE will not reveal positive findings due to its superior visualization of the pharynx and the upper esophagus.
    Keywords endoscopy ; esophagoscopy ; deglutition disorders ; Medicine ; R ; Otorhinolaryngology ; RF1-547
    Subject code 610 ; 616
    Language English
    Publishing date 2019-01-01T00:00:00Z
    Publisher Thieme Revinter Publicações Ltda.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra

    Halloran, John T. / Rocke, David M.

    2019  

    Abstract: Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A collection of spectra is generated at the output of the process, each spectrum of which is ... ...

    Abstract Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A collection of spectra is generated at the output of the process, each spectrum of which is representative of a peptide (protein subsequence) present in the original complex sample. In this work, we leverage the log-likelihood gradients of generative models to improve the identification of such spectra. In particular, we show that the gradient of a recently proposed dynamic Bayesian network (DBN) may be naturally employed by a kernel-based discriminative classifier. The resulting Fisher kernel substantially improves upon recent attempts to combine generative and discriminative models for post-processing analysis, outperforming all other methods on the evaluated datasets. We extend the improved accuracy offered by the Fisher kernel framework to other search algorithms by introducing Theseus, a DBN representing a large number of widely used MS/MS scoring functions. Furthermore, with gradient ascent and max-product inference at hand, we use Theseus to learn model parameters without any supervision.

    Comment: 13 pages. A partitioned version of this appeared in NIPS 2017
    Keywords Quantitative Biology - Quantitative Methods ; Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2019-09-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra

    Halloran, John T. / Rocke, David M.

    2019  

    Abstract: The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra representative of the peptides (i.e., protein subsequences) present in the ... ...

    Abstract The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra representative of the peptides (i.e., protein subsequences) present in the original sample. In this work, we greatly expand the parameter learning capabilities of a dynamic Bayesian network (DBN) peptide-scoring algorithm, Didea, by deriving emission distributions for which its conditional log-likelihood scoring function remains concave. We show that this class of emission distributions, called Convex Virtual Emissions (CVEs), naturally generalizes the log-sum-exp function while rendering both maximum likelihood estimation and conditional maximum likelihood estimation concave for a wide range of Bayesian networks. Utilizing CVEs in Didea allows efficient learning of a large number of parameters while ensuring global convergence, in stark contrast to Didea's previous parameter learning framework (which could only learn a single parameter using a costly grid search) and other trainable models (which only ensure convergence to local optima). The newly trained scoring function substantially outperforms the state-of-the-art in both scoring function accuracy and downstream Fisher kernel analysis. Furthermore, we significantly improve Didea's runtime performance through successive optimizations to its message passing schedule and derive explicit connections between Didea's new concave score and related MS/MS scoring functions.

    Comment: 16 pages. A partitioned version of this appeared in NeurIPS 2018
    Keywords Quantitative Biology - Quantitative Methods ; Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2019-09-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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