Spatial quantification of clinical biomarker pharmacokinetics through deep learning-based segmentation and signal-oriented analysis of MSOT data

Hoffmann, Birgit; Gerst, Ruman; Cseresnyés, Zoltán; Foo, Wanling; Sommerfeld, Oliver; Press, Adrian T.; Bauer, Michael; Figge, Marc Thilo

doi:10.1016/j.pacs.2022.100361

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…In the interest of the reader, we also provide links to the various methods’ publicly available source code or webservers. The use of ML (more recently DL) methods is not limited solely to SBDD, and they have also been applied to all other areas of the drug discovery pipeline such as LBDD (Bahi and Batouche, 2018 ), lead optimization (de Souza Neto et al, 2020 ), and assessment of absorption (Shin et al, 2018 ), metabolism (Wang et al, 2020 ; Litsa et al, 2021 ), binding kinetics (De Benedetti and Fanelli, 2018 ; Mardt et al, 2018 ; Gao et al, 2019 ; Nunes-Alves et al, 2020 ; Feizpour et al, 2021 ; Obeid et al, 2021 ; Hoffmann et al, 2022 ), efficacy (Lin et al, 2018 ; Benning et al, 2020 ; Li et al, 2021 a ; Zhu et al, 2021 ) and toxicity properties (Ferreira and Andricopulo, 2019 ; Liu et al, 2019 ; Shi et al, 2019 ; Cáceres et al, 2020 ; Feinberg et al, 2020 ). While these studies are not covered in this review, interested readers are encouraged to review these recent works as well.…”

Section: Discussionmentioning

confidence: 99%

Applications of machine learning in computer-aided drug discovery

2022

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Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

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Section: Discussionmentioning

confidence: 99%

Applications of machine learning in computer-aided drug discovery

2022

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“…Furthermore, JIPipe comes with nodes for Python 17 , Jython 18 , R 19 , and ImageJ macro scripts, thus extending the flexibility of workflow development for experienced users with programming skills. JIPipe has already been successfully applied to solve numerous image analysis tasks, including recently published works on nematode activity analysis 20 and the quantification of multispectral optoacoustic tomography (MSOT) images 21 , thus underlining its flexibility and wide applicability.…”

Section: Introductionmentioning

confidence: 99%

JIPipe: visual batch processing for ImageJ

2023

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The continuous development of new microscopy techniques requires the parallel evolution of image analysis workflows. ImageJ provides a high level of accessibility to bioimage processing, which is still impeded by the necessity of developing scripts to achieve reproducibility, and to comply to the FAIR principles. We provide a visual language termed JIPipe that allows the construction of an ImageJ workflow purely by designing a flowchart. We already included over 1000 functions from ImageJ and its plugins. In return, ImageJ is extended with custom-designed algorithms, thus forming a symbiotic relationship with JIPipe. Our software includes a fully reproduceable and standardized project format, zero-cost scalability of pipelines, as well as automated data saving into an open format. JIPipe was already utilized to solve numerous demanding image analysis tasks, showcasing its wide applicability and adaptability. JIPipe contributes towards making bioimage analysis more accessible, thereby fostering collaborations between experimentalists and computer scientists.

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“…In particular, the field of bioimage informatics has drastically benefited from the advance of deep learning. Several deep-learning-based analysis approaches have been developed in recent years, ranging from the classification of protein subcellular localisation in immunohistochemistry images [ 30 ] to the spatial quantification of clinical biomarker pharmacokinetics [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Applications of Neural Networks in Biomedical Data Analysis

et al. 2022

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Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields.

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Spatial quantification of clinical biomarker pharmacokinetics through deep learning-based segmentation and signal-oriented analysis of MSOT data

Cited by 3 publications

References 30 publications

Applications of machine learning in computer-aided drug discovery

Applications of machine learning in computer-aided drug discovery

JIPipe: visual batch processing for ImageJ

Applications of Neural Networks in Biomedical Data Analysis

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