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  1. Article ; Online: An evaluation of von Willebrand factor (recombinant) therapy for adult patients living with severe type 3 von Willebrand disease.

    Hancock, John M / Escobar, Miguel A

    Expert review of hematology

    2023  Volume 16, Issue 3, Page(s) 157–161

    Abstract: Introduction: Von Willebrand Factor (VWF) containing concentrates have been used for the treatment of von Willebrand Disease (VWD) for many years. Recently, however, a novel recombinant VWF (rVWF or vonicog alpha, VONVENDI [US], VEYVONDI [Europe]) has ... ...

    Abstract Introduction: Von Willebrand Factor (VWF) containing concentrates have been used for the treatment of von Willebrand Disease (VWD) for many years. Recently, however, a novel recombinant VWF (rVWF or vonicog alpha, VONVENDI [US], VEYVONDI [Europe]) has arrived to the market for the treatment of VWD. Initially, rVWF was approved by the U.S. Food and Drug Administration (FDA) for the on-demand treatment and control of bleeding episodes and for the perioperative management of bleeding for patients with VWD. More recently, however, the FDA has approved rVWF for routine prophylaxis to prevent bleeding episodes for those patients with severe type 3 VWD receiving on-demand therapy.
    Areas covered: This review will focus on recent phase III trial results from NCT02973087 regarding the use of long-term routine twice weekly prophylaxis with rVWF for the prevention of bleed events in patients with severe type 3 VWD.
    Expert opinion: A novel rVWF concentrate may have greater hemostatic potential over prior plasma-derived VWF concentrates and is now FDA approved for use in routine prophylaxis for patients with severe type 3 VWD in the United States. This greater hemostatic potential may be due to the presence of ultra-large VWF multimers and a more favorable high-molecular-weight multimer pattern compared to prior pdVWF concentrates.
    MeSH term(s) Humans ; Adult ; von Willebrand Factor/therapeutic use ; von Willebrand Diseases/drug therapy ; von Willebrand Disease, Type 3/drug therapy ; Recombinant Proteins ; Hemorrhage/etiology ; Hemorrhage/prevention & control ; Hemostatics/therapeutic use
    Chemical Substances von Willebrand Factor ; Recombinant Proteins ; Hemostatics
    Language English
    Publishing date 2023-03-02
    Publishing country England
    Document type Review ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2516804-6
    ISSN 1747-4094 ; 1747-4086
    ISSN (online) 1747-4094
    ISSN 1747-4086
    DOI 10.1080/17474086.2023.2184339
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6943: Show issues Location:
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  2. Book: Dictionary of bioinformatics and computational biology

    Hancock, John M.

    2004  

    Title variant Bioinformatics and computational biology
    Author's details ed. by John M. Hancock
    Keywords Computational Biology ; Bioinformatik
    Language English
    Size XXIII, 636 S. : graph. Darst.
    Publisher Wiley-Liss
    Publishing place Hoboken, NJ
    Publishing country United States
    Document type Book
    HBZ-ID HT013754261
    ISBN 0-471-43622-4 ; 978-0-471-43622-5
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2005 A 7613 order for loan Magazin

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  3. Article ; Online: Synergistic interplay between melatonin and hydrogen sulfide enhances cadmium-induced oxidative stress resistance in stock (

    Zulfiqar, Faisal / Moosa, Anam / Ali, Hayssam M / Hancock, John T / Yong, Jean Wan Hong

    Plant signaling & behavior

    2024  Volume 19, Issue 1, Page(s) 2331357

    Abstract: Ornamental crops particularly cut flowers are considered sensitive to heavy metals (HMs) induced oxidative stress condition. Melatonin (MLT) is a versatile phytohormone with the ability to mitigate abiotic stresses induced oxidative stress in plants. ... ...

    Abstract Ornamental crops particularly cut flowers are considered sensitive to heavy metals (HMs) induced oxidative stress condition. Melatonin (MLT) is a versatile phytohormone with the ability to mitigate abiotic stresses induced oxidative stress in plants. Similarly, signaling molecules such as hydrogen sulfide (H
    MeSH term(s) Hydrogen Sulfide/pharmacology ; Cadmium/toxicity ; Melatonin/pharmacology ; Oxidative Stress ; Antioxidants/metabolism ; Brassicaceae/metabolism ; Hydrogen Peroxide ; Sulfides
    Chemical Substances Hydrogen Sulfide (YY9FVM7NSN) ; Cadmium (00BH33GNGH) ; Melatonin (JL5DK93RCL) ; sodium bisulfide (FWU2KQ177W) ; Antioxidants ; Hydrogen Peroxide (BBX060AN9V) ; Sulfides
    Language English
    Publishing date 2024-04-02
    Publishing country United States
    Document type Journal Article
    ISSN 1559-2324
    ISSN (online) 1559-2324
    DOI 10.1080/15592324.2024.2331357
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Data reduction techniques for highly imbalanced medicare Big Data

    John T. Hancock / Huanjing Wang / Taghi M. Khoshgoftaar / Qianxin Liang

    Journal of Big Data, Vol 11, Iss 1, Pp 1-

    2024  Volume 41

    Abstract: Abstract In the domain of Medicare insurance fraud detection, handling imbalanced Big Data and high dimensionality remains a significant challenge. This study assesses the combined efficacy of two data reduction techniques: Random Undersampling (RUS), ... ...

    Abstract Abstract In the domain of Medicare insurance fraud detection, handling imbalanced Big Data and high dimensionality remains a significant challenge. This study assesses the combined efficacy of two data reduction techniques: Random Undersampling (RUS), and a novel ensemble supervised feature selection method. The techniques are applied to optimize Machine Learning models for fraud identification in the classification of highly imbalanced Big Medicare Data. Utilizing two datasets from The Centers for Medicare & Medicaid Services (CMS) labeled by the List of Excluded Individuals/Entities (LEIE), our principal contribution lies in empirically demonstrating that data reduction techniques applied to these datasets significantly improves classification performance. The study employs a systematic experimental design to investigate various scenarios, ranging from using each technique in isolation to employing them in combination. The results indicate that a synergistic application of both techniques outperforms models that utilize all available features and data. Moreover, reduction in the number of features leads to more explainable models. Given the enormous financial implications of Medicare fraud, our findings not only offer computational advantages but also significantly enhance the effectiveness of fraud detection systems, thereby having the potential to improve healthcare services.
    Keywords Random undersampling ; Ensemble supervised feature selection ; Big Data ; Medicare fraud detection ; Computer engineering. Computer hardware ; TK7885-7895 ; Information technology ; T58.5-58.64 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: CatBoost for big data: an interdisciplinary review.

    Hancock, John T / Khoshgoftaar, Taghi M

    Journal of big data

    2020  Volume 7, Issue 1, Page(s) 94

    Abstract: Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and ... ...

    Abstract Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost's effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.
    Keywords covid19
    Language English
    Publishing date 2020-11-04
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2780218-8
    ISSN 2196-1115
    ISSN 2196-1115
    DOI 10.1186/s40537-020-00369-8
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  6. Article ; Online: Evaluating classifier performance with highly imbalanced Big Data

    John T. Hancock / Taghi M. Khoshgoftaar / Justin M. Johnson

    Journal of Big Data, Vol 10, Iss 1, Pp 1-

    2023  Volume 31

    Abstract: Abstract Using the wrong metrics to gauge classification of highly imbalanced Big Data may hide important information in experimental results. However, we find that analysis of metrics for performance evaluation and what they can hide or reveal is rarely ...

    Abstract Abstract Using the wrong metrics to gauge classification of highly imbalanced Big Data may hide important information in experimental results. However, we find that analysis of metrics for performance evaluation and what they can hide or reveal is rarely covered in related works. Therefore, we address that gap by analyzing multiple popular performance metrics on three Big Data classification tasks. To the best of our knowledge, we are the first to utilize three new Medicare insurance claims datasets which became publicly available in 2021. These datasets are all highly imbalanced. Furthermore, the datasets are comprised of completely different data. We evaluate the performance of five ensemble learners in the Machine Learning task of Medicare fraud detection. Random Undersampling (RUS) is applied to induce five class ratios. The classifiers are evaluated with both the Area Under the Receiver Operating Characteristic Curve (AUC), and Area Under the Precision Recall Curve (AUPRC) metrics. We show that AUPRC provides a better insight into classification performance. Our findings reveal that the AUC metric hides the performance impact of RUS. However, classification results in terms of AUPRC show RUS has a detrimental effect. We show that, for highly imbalanced Big Data, the AUC metric fails to capture information about precision scores and false positive counts that the AUPRC metric reveals. Our contribution is to show AUPRC is a more effective metric for evaluating the performance of classifiers when working with highly imbalanced Big Data.
    Keywords Extremely randomized trees ; XGBoost ; Class imbalance ; Big Data ; Undersampling ; AUC ; Computer engineering. Computer hardware ; TK7885-7895 ; Information technology ; T58.5-58.64 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2023-04-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  7. Article: Circles within circles: commentary on Ghosal et al. (2013) "Circ2Traits: a comprehensive database for circular RNA potentially associated with disease and traits".

    Hancock, John M

    Frontiers in genetics

    2015  Volume 5, Page(s) 459

    Language English
    Publishing date 2015-01-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2014.00459
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  8. Article: Editorial: biological ontologies and semantic biology.

    Hancock, John M

    Frontiers in genetics

    2014  Volume 5, Page(s) 18

    Language English
    Publishing date 2014-02-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2014.00018
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Commentary on Shimoyama et al. (2012): three ontologies to define phenotype measurement data.

    Hancock, John M

    Frontiers in genetics

    2014  Volume 5, Page(s) 93

    Language English
    Publishing date 2014-04-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2014.00093
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Sleepwalking towards more harm from asthma.

    Jenkins, Christine R / Bardin, Philip G / Blakey, John / Hancock, Kerry L / Gibson, Peter / McDonald, Vanessa M

    The Medical journal of Australia

    2023  Volume 219, Issue 2, Page(s) 49–52

    MeSH term(s) Humans ; Somnambulism ; Polysomnography ; Asthma
    Language English
    Publishing date 2023-06-12
    Publishing country Australia
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 186082-3
    ISSN 1326-5377 ; 0025-729X
    ISSN (online) 1326-5377
    ISSN 0025-729X
    DOI 10.5694/mja2.52000
    Database MEDical Literature Analysis and Retrieval System OnLINE

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