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Frederick Klauschen

Researcher at Humboldt University of Berlin

Publications -  238
Citations -  17312

Frederick Klauschen is an academic researcher from Humboldt University of Berlin. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 45, co-authored 170 publications receiving 12451 citations. Previous affiliations of Frederick Klauschen include Charité & National Institutes of Health.

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DNA methylation-based machine learning classification distinguishes pleural mesothelioma from chronic pleuritis, pleural carcinosis, and pleomorphic lung carcinomas.

TL;DR: In this paper , the diagnostic value of DNA methylation analysis in combination with machine learning to differentiate pleural mesothelioma (PM) from important histopathological mimics was evaluated.

Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning

TL;DR: In this paper, different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data.
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New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning

TL;DR: In this paper , the authors performed a dynamic analysis of human reactive lymphoid tissue using confocal fluorescent laser microscopy in combination with machine learning and identified correlations of follicular dendritic cell movement and the behavior of lymphocytes in the microenvironment.
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[Structure and content of the EU-IVDR : Current status and implications for pathology].

TL;DR: The European Parliament and the Council of the European Union have proposed a new amending regulation (EU 2017/746) on in vitro diagnostic medical devices (IVDR) which introduces a phased implementation of the IVDR with new transitional provisions for certain in vitro diagnosis medical devices and a later date of application of some requirements for in-house devices for healthcare facilities as mentioned in this paper .
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Systems proteogenomics for precision oncology.

TL;DR: Findings suggest that ways beyond genomics have to be found to identify functionally and clinically relevant molecular alterations within the complex mutational landscape of cancer.