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Article ; Online: The NCI Imaging Data Commons as a platform for reproducible research in computational pathology.

Schacherer, Daniela P / Herrmann, Markus D / Clunie, David A / Höfener, Henning / Clifford, William / Longabaugh, William J R / Pieper, Steve / Kikinis, Ron / Fedorov, Andrey / Homeyer, André

Computer methods and programs in biomedicine

2023 Volume 242, Page(s) 107839

Abstract: Background and objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR ... ...

Abstract	Background and objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. Methods: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. Results: The results of different runs of the same experiment were reproducible to a large extent. However, we observed occasional, small variations in AUC values, indicating a practical limit to reproducibility. Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.
MeSH term(s)	Humans ; Software ; Reproducibility of Results ; Cloud Computing ; Diagnostic Imaging ; Lung Neoplasms/diagnostic imaging
Language	English
Publishing date	2023-10-02
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2023.107839
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Article ; Online: Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology.

Gorman, Chris / Punzo, Davide / Octaviano, Igor / Pieper, Steven / Longabaugh, William J R / Clunie, David A / Kikinis, Ron / Fedorov, Andrey Y / Herrmann, Markus D

Nature communications

2023 Volume 14, Issue 1, Page(s) 1572

Abstract: The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and ... ...

Abstract	The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.
MeSH term(s)	Humans ; Microscopy/methods ; Data Science ; Reproducibility of Results
Language	English
Publishing date	2023-03-22
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-37224-2
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Article ; Online: BioTapestry: a tool to visualize the dynamic properties of gene regulatory networks.

Longabaugh, William J R

Methods in molecular biology (Clifton, N.J.)

2012 Volume 786, Page(s) 359–394

Abstract: BioTapestry is an open source, freely available software tool that has been developed to handle the -challenges of modeling genetic regulatory networks (GRNs). Using BioTapestry, a researcher can -construct a network model and use it to visualize and ... ...

Abstract	BioTapestry is an open source, freely available software tool that has been developed to handle the -challenges of modeling genetic regulatory networks (GRNs). Using BioTapestry, a researcher can -construct a network model and use it to visualize and understand the dynamic behavior of a complex, spatially and temporally distributed GRN. Here we provide a step-by-step example of a way to use BioTapestry to build a GRN model and discuss some common issues that can arise during this process.
MeSH term(s)	Computational Biology/methods ; Gene Regulatory Networks/genetics ; Models, Genetic ; Software ; Systems Biology
Language	English
Publishing date	2012
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-61779-292-2_21
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Article ; Online: Combing the hairball with BioFabric: a new approach for visualization of large networks.

Longabaugh, William J R

BMC bioinformatics

2012 Volume 13, Page(s) 275

Abstract: Background: The analysis of large, complex networks is an important aspect of ongoing biological research. Yet there is a need for entirely new, scalable approaches for network visualization that can provide more insight into the structure and function ... ...

Abstract	Background: The analysis of large, complex networks is an important aspect of ongoing biological research. Yet there is a need for entirely new, scalable approaches for network visualization that can provide more insight into the structure and function of these complex networks. Results: To address this need, we have developed a software tool named BioFabric, which uses a novel network visualization technique that depicts nodes as one-dimensional horizontal lines arranged in unique rows. This is in distinct contrast to the traditional approach that represents nodes as discrete symbols that behave essentially as zero-dimensional points. BioFabric then depicts each edge in the network using a vertical line assigned to its own unique column, which spans between the source and target rows, i.e. nodes. This method of displaying the network allows a full-scale view to be organized in a rational fashion; interesting network structures, such as sets of nodes with similar connectivity, can be quickly scanned and visually identified in the full network view, even in networks with well over 100,000 edges. This approach means that the network is being represented as a fundamentally linear, sequential entity, where the horizontal scroll bar provides the basic navigation tool for browsing the entire network. Conclusions: BioFabric provides a novel and powerful way of looking at any size of network, including very large networks, using horizontal lines to represent nodes and vertical lines to represent edges. It is freely available as an open-source Java application.
MeSH term(s)	Computer Graphics ; Gene Regulatory Networks ; Metabolic Networks and Pathways ; Software
Language	English
Publishing date	2012-10-27
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/1471-2105-13-275
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Book ; Online: The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

Schacherer, Daniela P. / Herrmann, Markus D. / Clunie, David A. / Höfener, Henning / Clifford, William / Longabaugh, William J. R. / Pieper, Steve / Kikinis, Ron / Fedorov, Andrey / Homeyer, André

2023

Abstract: Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR ... ...

Abstract	Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. Methods: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent. However, we observed small variations in AUC values of up to 0.045, indicating a practical limit to reproducibility. Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments. Comment: 13 pages, 5 figures; improved manuscript, new experiments with P100 GPU
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Publishing date	2023-03-16
Publishing country	us
Document type	Book ; Online
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Article: BioTapestry now provides a web application and improved drawing and layout tools.

Paquette, Suzanne M / Leinonen, Kalle / Longabaugh, William J R

F1000Research

2016 Volume 5, Page(s) 39

Abstract: Gene regulatory networks (GRNs) control embryonic development, and to understand this process in depth, researchers need to have a detailed understanding of both the network architecture and its dynamic evolution over time and space. Interactive ... ...

Abstract	Gene regulatory networks (GRNs) control embryonic development, and to understand this process in depth, researchers need to have a detailed understanding of both the network architecture and its dynamic evolution over time and space. Interactive visualization tools better enable researchers to conceptualize, understand, and share GRN models. BioTapestry is an established application designed to fill this role, and recent enhancements released in Versions 6 and 7 have targeted two major facets of the program. First, we introduced significant improvements for network drawing and automatic layout that have now made it much easier for the user to create larger, more organized network drawings. Second, we revised the program architecture so it could continue to support the current Java desktop Editor program, while introducing a new BioTapestry GRN Viewer that runs as a JavaScript web application in a browser. We have deployed a number of GRN models using this new web application. These improvements will ensure that BioTapestry remains viable as a research tool in the face of the continuing evolution of web technologies, and as our understanding of GRN models grows.
Language	English
Publishing date	2016-01-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2699932-8
ISSN	2046-1402
ISSN	2046-1402
DOI	10.12688/f1000research.7620.1
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Article ; Online: The TP53 Database: transition from the International Agency for Research on Cancer to the US National Cancer Institute.

de Andrade, Kelvin César / Lee, Elaine E / Tookmanian, Elise M / Kesserwan, Chimene A / Manfredi, James J / Hatton, Jessica N / Loukissas, Jennifer K / Zavadil, Jiri / Zhou, Lei / Olivier, Magali / Frone, Megan N / Shahzada, Owais / Longabaugh, William J R / Kratz, Christian P / Malkin, David / Hainaut, Pierre / Savage, Sharon A

Cell death and differentiation

2022 Volume 29, Issue 5, Page(s) 1071–1073

MeSH term(s)	Germ-Line Mutation ; International Agencies ; Mutation ; National Cancer Institute (U.S.) ; Neoplasms/genetics ; Tumor Suppressor Protein p53/genetics ; United States
Chemical Substances	Tumor Suppressor Protein p53
Language	English
Publishing date	2022-03-29
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Intramural ; Research Support, N.I.H., Extramural
ZDB-ID	1225672-9
ISSN	1476-5403 ; 1350-9047
ISSN (online)	1476-5403
ISSN	1350-9047
DOI	10.1038/s41418-022-00976-3
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Article ; Online: National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence.

Radiographics : a review publication of the Radiological Society of North America, Inc

2023 Volume 43, Issue 12, Page(s) e230180

Abstract: The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools ... ...

Abstract	The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires easy access to large high-quality annotated datasets, which are both representative and diverse. The National Cancer Institute (NCI) Imaging Data Commons (IDC) hosts large and diverse publicly available cancer image data collections. By harmonizing all data based on industry standards and colocalizing it with analysis and exploration resources, the IDC aims to facilitate the development, validation, and clinical translation of AI tools and address the well-documented challenges of establishing reproducible and transparent AI processing pipelines. Balanced use of established commercial products with open-source solutions, interconnected by standard interfaces, provides value and performance, while preserving sufficient agility to address the evolving needs of the research community. Emphasis on the development of tools, use cases to demonstrate the utility of uniform data representation, and cloud-based analysis aim to ease adoption and help define best practices. Integration with other data in the broader NCI Cancer Research Data Commons infrastructure opens opportunities for multiomics studies incorporating imaging data to further empower the research community to accelerate breakthroughs in cancer detection, diagnosis, and treatment. Published under a CC BY 4.0 license.
MeSH term(s)	United States ; Humans ; Artificial Intelligence ; National Cancer Institute (U.S.) ; Reproducibility of Results ; Diagnostic Imaging ; Multiomics ; Neoplasms/diagnostic imaging
Language	English
Publishing date	2023-11-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	603172-9
ISSN	1527-1323 ; 0271-5333
ISSN (online)	1527-1323
ISSN	0271-5333
DOI	10.1148/rg.230180
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Book ; Online: Slim

Gorman, Chris / Punzo, Davide / Octaviano, Igor / Pieper, Steve / Longabaugh, William J. R. / Clunie, David A. / Kikinis, Ron / Fedorov, Andrey Y. / Herrmann, Markus D.

interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology

2022

Abstract	The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. Slim is an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.
Keywords	Quantitative Biology - Quantitative Methods
Subject code	020
Publishing date	2022-05-18
Publishing country	us
Document type	Book ; Online
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Article ; Online: Absolute Quantification of Transcription Factors Reveals Principles of Gene Regulation in Erythropoiesis.

Gillespie, Mark A / Palii, Carmen G / Sanchez-Taltavull, Daniel / Shannon, Paul / Longabaugh, William J R / Downes, Damien J / Sivaraman, Karthi / Espinoza, Herbert M / Hughes, Jim R / Price, Nathan D / Perkins, Theodore J / Ranish, Jeffrey A / Brand, Marjorie

Molecular cell

2020 Volume 78, Issue 5, Page(s) 960–974.e11

Abstract: Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). However, despite years of studies, our knowledge about the protein copy number of TFs in the nucleus is limited. Here, by ... ...

Abstract	Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). However, despite years of studies, our knowledge about the protein copy number of TFs in the nucleus is limited. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first gene regulatory network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs' cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that, in the nucleus, co-repressors are dramatically more abundant than co-activators at the protein level, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.
MeSH term(s)	Databases, Factual ; Erythropoiesis/genetics ; Gene Expression Regulation/genetics ; Gene Regulatory Networks/genetics ; Hematopoiesis/genetics ; Humans ; Proteomics/methods ; Transcription Factors/analysis ; Transcription Factors/genetics ; Transcription Factors/metabolism
Chemical Substances	Transcription Factors
Language	English
Publishing date	2020-04-23
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1415236-8
ISSN	1097-4164 ; 1097-2765
ISSN (online)	1097-4164
ISSN	1097-2765
DOI	10.1016/j.molcel.2020.03.031
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