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  1. Article: All models are wrong and yours are useless: making clinical prediction models impactful for patients.

    Markowetz, Florian

    NPJ precision oncology

    2024  Volume 8, Issue 1, Page(s) 54

    Language English
    Publishing date 2024-02-28
    Publishing country England
    Document type Journal Article
    ISSN 2397-768X
    ISSN 2397-768X
    DOI 10.1038/s41698-024-00553-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: All biology is computational biology.

    Markowetz, Florian

    PLoS biology

    2017  Volume 15, Issue 3, Page(s) e2002050

    Abstract: Here, I argue that computational thinking and techniques are so central to the quest of understanding life that today all biology is computational biology. Computational biology brings order into our understanding of life, it makes biological concepts ... ...

    Abstract Here, I argue that computational thinking and techniques are so central to the quest of understanding life that today all biology is computational biology. Computational biology brings order into our understanding of life, it makes biological concepts rigorous and testable, and it provides a reference map that holds together individual insights. The next modern synthesis in biology will be driven by mathematical, statistical, and computational methods being absorbed into mainstream biological training, turning biology into a quantitative science.
    MeSH term(s) Computational Biology ; Humans ; Models, Biological
    Language English
    Publishing date 2017-03-09
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2126776-5
    ISSN 1545-7885 ; 1544-9173
    ISSN (online) 1545-7885
    ISSN 1544-9173
    DOI 10.1371/journal.pbio.2002050
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A saltationist theory of cancer evolution.

    Markowetz, Florian

    Nature genetics

    2016  Volume 48, Issue 10, Page(s) 1102–1103

    Abstract: A new study based on single-nucleus sequencing reports that triple-negative breast cancers acquire copy number aberrations in short punctuated bursts in the earliest stages of tumor evolution, rather than continuously and gradually, challenging ... ...

    Abstract A new study based on single-nucleus sequencing reports that triple-negative breast cancers acquire copy number aberrations in short punctuated bursts in the earliest stages of tumor evolution, rather than continuously and gradually, challenging prevailing models of tumor evolution.
    MeSH term(s) DNA Copy Number Variations/genetics ; Female ; Genetic Heterogeneity ; Humans ; Selection, Genetic/genetics ; Triple Negative Breast Neoplasms/genetics ; Triple Negative Breast Neoplasms/pathology
    Language English
    Publishing date 2016-09-27
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 1108734-1
    ISSN 1546-1718 ; 1061-4036
    ISSN (online) 1546-1718
    ISSN 1061-4036
    DOI 10.1038/ng.3687
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: SliDL: A toolbox for processing whole-slide images in deep learning.

    Berman, Adam G / Orchard, William R / Gehrung, Marcel / Markowetz, Florian

    PloS one

    2023  Volume 18, Issue 8, Page(s) e0289499

    Abstract: The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these ... ...

    Abstract The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. Here we introduce SliDL, a Python library for performing pre- and post-processing of WSIs. SliDL makes WSI data handling easy, allowing users to perform essential processing tasks in a few simple lines of code, bridging the gap between standard image analysis and WSI analysis. We introduce each of the main functionalities within SliDL: from annotation and tile extraction to tissue detection and model evaluation. We also provide 'code snippets' to guide the user in running SliDL. SliDL has been designed to interact with PyTorch, one of the most widely used deep learning libraries, allowing seamless integration into deep learning workflows. By providing a framework in which deep learning methods for WSI analysis can be developed and applied, SliDL aims to increase the accessibility of an important application of deep learning.
    MeSH term(s) Deep Learning ; Image Interpretation, Computer-Assisted/methods ; Coloring Agents ; Image Processing, Computer-Assisted/methods
    Chemical Substances Coloring Agents
    Language English
    Publishing date 2023-08-07
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0289499
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: You Are Not Working for Me; I Am Working with You.

    Markowetz, Florian

    PLoS computational biology

    2015  Volume 11, Issue 9, Page(s) e1004387

    MeSH term(s) Communication ; Group Processes ; Humans ; Laboratories ; Leadership ; Research Design ; Workplace
    Language English
    Publishing date 2015-09-24
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1004387
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  6. Article ; Online: Five selfish reasons to work reproducibly.

    Markowetz, Florian

    Genome biology

    2015  Volume 16, Page(s) 274

    Abstract: And so, my fellow scientists: ask not what you can do for reproducibility; ask what reproducibility can do for you! Here, I present five reasons why working reproducibly pays off in the long run and is in the self-interest of every ambitious, career- ... ...

    Abstract And so, my fellow scientists: ask not what you can do for reproducibility; ask what reproducibility can do for you! Here, I present five reasons why working reproducibly pays off in the long run and is in the self-interest of every ambitious, career-oriented scientist.
    MeSH term(s) Career Choice ; Humans ; Research ; Workforce
    Language English
    Publishing date 2015-12-08
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1474-760X
    ISSN (online) 1474-760X
    ISSN 1474-760X
    DOI 10.1186/s13059-015-0850-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: "All Biology is Computational Biology" - Computer-basierte Ansätze werden eine neue Synthese der Biologie auslösen

    Markowetz, Florian

    Laborjournal

    2017  Volume 24, Issue 7/8, Page(s) 35

    Language German
    Document type Article
    ZDB-ID 1237282-1
    ISSN 1612-8354
    Database Current Contents Medicine

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4842: Show issues Location:
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  8. Article: Five selfish reasons to work reproducibly

    Markowetz, Florian

    Genome biology. 2015 Dec., v. 16, no. 1

    2015  

    Abstract: And so, my fellow scientists: ask not what you can do for reproducibility; ask what reproducibility can do for you! Here, I present five reasons why working reproducibly pays off in the long run and is in the self-interest of every ambitious, career- ... ...

    Abstract And so, my fellow scientists: ask not what you can do for reproducibility; ask what reproducibility can do for you! Here, I present five reasons why working reproducibly pays off in the long run and is in the self-interest of every ambitious, career-oriented scientist.
    Keywords career development ; motivation ; reproducibility ; research ; scientists
    Language English
    Dates of publication 2015-12
    Size p. 274.
    Publishing place BioMed Central
    Document type Article
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6906
    ISSN (online) 1474-760X
    ISSN 1465-6906
    DOI 10.1186/s13059-015-0850-7
    Database NAL-Catalogue (AGRICOLA)

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  9. Article ; Online: Quantification of TFF3 expression from a non-endoscopic device predicts clinically relevant Barrett's oesophagus by machine learning.

    Berman, Adam G / Tan, W Keith / O'Donovan, Maria / Markowetz, Florian / Fitzgerald, Rebecca C

    EBioMedicine

    2022  Volume 82, Page(s) 104160

    Abstract: Background: Intestinal metaplasia (IM) is pre-neoplastic with variable cancer risk. Cytosponge-TFF3 test can detect IM. We aimed to 1) assess whether quantitative TFF3 scores can distinguish clinically relevant Barrett's oesophagus (BO) (C≥1 or M≥3) ... ...

    Abstract Background: Intestinal metaplasia (IM) is pre-neoplastic with variable cancer risk. Cytosponge-TFF3 test can detect IM. We aimed to 1) assess whether quantitative TFF3 scores can distinguish clinically relevant Barrett's oesophagus (BO) (C≥1 or M≥3) from focal IM pathologies (C<1, M<3 or IM of gastro-oesophageal junction); 2) whether TFF3 counts can be automated to inform clinical practice.
    Methods: Patients from the Barett's oEsophagus Screening Trial 2 (BEST2) case-control and BEST3 randomised trials were used. For aim 1, TFF3-positive glands were scored manually and correlated with clinical diagnosis. For aim 2, machine learning approach was used to obtain TFF3 count and logistic regression with cross-validation was trained on the BEST2 dataset (n = 529) and tested in the BEST3 dataset (n = 158).
    Findings: Patients with clinically relevant BO had higher mean TFF3 gland count compared to focal IM pathologies (mean difference 4.14; 95% confidence interval, CI 2.76-5.52, p < 0.001). The mean class-balanced validation accuracy was 0.84 (95% CI 0.77-0.90), and precision of 0.95 (95% CI 0.87-1.00) for detecting clinically relevant BO. Applying this model on BEST3 showed precision of 0.91 (95% CI 0.85-0.97) for focal IM pathologies with a class-balanced accuracy of 0.77 (95% CI 0.69-0.84). Using this model, 55% of patients (87/158) in BEST3 would fall below the threshold for clinically relevant BO and could avoid gastroscopy, while only missing 5.1% of patients (8/158).
    Interpretation: Automated Cytosponge-TFF3 gland quantification may enable thresholds to be set to trigger confirmatory gastroscopy to minimize overdiagnosis of focal IM pathologies with very low cancer-associated risk.
    Funding: Cancer Research UK (12088/16893 and C14478/A21047).
    MeSH term(s) Barrett Esophagus/diagnosis ; Barrett Esophagus/pathology ; Esophageal Neoplasms/pathology ; Gastroscopy ; Humans ; Machine Learning ; Metaplasia ; Trefoil Factor-3
    Chemical Substances TFF3 protein, human ; Trefoil Factor-3
    Language English
    Publishing date 2022-07-15
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2851331-9
    ISSN 2352-3964
    ISSN (online) 2352-3964
    DOI 10.1016/j.ebiom.2022.104160
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 7090: Show issues Location:
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  10. Article ; Online: How to understand the cell by breaking it: network analysis of gene perturbation screens.

    Markowetz, Florian

    PLoS computational biology

    2010  Volume 6, Issue 2, Page(s) e1000655

    MeSH term(s) Animals ; Cell Physiological Phenomena ; Cluster Analysis ; Gene Regulatory Networks ; Genomics ; Humans ; Models, Genetic ; Models, Statistical ; Phenotype ; Signal Transduction ; Systems Biology/methods
    Language English
    Publishing date 2010-02-26
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1000655
    Database MEDical Literature Analysis and Retrieval System OnLINE

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