• Sarcoma classification by DNA methylation profiling

    Sarcoma classification by DNA methylation profiling

    9 Heidelberg Center for Personalized Oncology (HIPO), German Cancer Research Center (DKFZ), Heidelberg, Germany. ... Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077

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  • DNA methylation-based classification of central nervous

    DNA methylation-based classification of central nervous

    1 Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany. ... For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other

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  • Methylation array profiling of adult brain tumours

    Methylation array profiling of adult brain tumours

    Feb 20, 2019 A brain tumour methylation classifier has been developed at the German Cancer Research Center (DKFZ) and Heidelberg University in Heidelberg, Germany (henceforth in short “Classifier”), to identify distinct DNA methylation classes of CNS tumours

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  • MNP - Classifier details

    MNP - Classifier details

    Reference set (classifier version: 11b4) The reference set is highly reminiscent of the WHO classification of brain tumors but not identical. For the constriction of the reference set at least 8 typical cases of each histological subtype described in the WHO classification of brain tumors were collected and underwent critical histological review

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  • German Cancer Research Center - DKFZ

    German Cancer Research Center - DKFZ

    The German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) with its more than 2,500 employees is the largest biomedical research institute in Germany. At DKFZ, more than 1,000 scientists investigate how cancer develops, identify cancer risk factors and endeavor to find new strategies to prevent people from getting cancer. They develop novel approaches to make tumor

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  • Heidelberg Retina Tomograph 3 machine learning

    Heidelberg Retina Tomograph 3 machine learning

    Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection K A Townsend, 1G Wollstein, D Danks,2,3 K R Sung, H Ishikawa, 1L Kagemann, M L Gabriele, 1J S Schuman 1 UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center

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  • Fuzzy classifiers - Scholarpedia

    Fuzzy classifiers - Scholarpedia

    Jun 11, 2013 A classifier is an algorithm that assigns a class label to an object, based on the object description. ... Heidelberg, May 2000. Mamdani E. H., Application of fuzzy logic to approximate reasoning using linguistic synthesis, IEEE Trans. Computers 26(12), 1977, pp. 1182-1191

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  • Machine learning workflows to estimate class probabilities

    Machine learning workflows to estimate class probabilities

    Mar 14, 2018 Because the respective R package for each investigated ML-classifier algorithm has different built-in functionalities, ... the Heidelberg experience. Acta Neuropathol. 136, 181–210 (2018)

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  • noab227.pdf - Impact of the methylation classifier and

    noab227.pdf - Impact of the methylation classifier and

    The DKFZ/Heidelberg CNS tumor methylation classifier contributes to these goals, but more experience is required to understand its impact. We utilized the classifier and integrated diagnosis on a large cohort of 1,000 cases, most of which were received from outside consultation for the purpose of methylation

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  • GitHub - Huitzilo/neuromorphic_classifier: A

    GitHub - Huitzilo/neuromorphic_classifier: A

    This project implements the neuromorphic classifier network as described in [1]. In its current version, it requires the Spikey neuromorphic hardware system [2], that is developed at Kirchhoff-Institute for Physics, Heidelberg University [3]. How to run an example:

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  • MNP - Classifier list

    MNP - Classifier list

    Name Version Description Reference group; 11b2: 11b2: Brain tumor classifier: show: 11b4: 11b4: Brain tumor classifier: show: sarcoma classifier: 8.0: Sarcoma classifier

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  • DNA methylation‐based profiling of bone and soft tissue

    DNA methylation‐based profiling of bone and soft tissue

    May 05, 2021 This may partly be explained by the Heidelberg Sarcoma Classifier reference set being composed of ‘classical’ cases with confirmed pathognomonic alterations for all entities characterised by such a feature. The classifier was therefore trained on a relatively narrow spectrum of cases for each sarcoma subtype compared to those in our data

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  • Comparison of Different Machine Learning Classifiers for

    Comparison of Different Machine Learning Classifiers for

    Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating

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  • Heidelberg Retina Tomograph Measurements of the

    Heidelberg Retina Tomograph Measurements of the

    Heidelberg Retina Tomograph Measurements of the Optic Disc and Parapapillary Retina for Detecting Glaucoma Analyzed by Machine Learning Classifiers Linda M. Zangwill,1 Kwokleung Chan, 2,3Christopher Bowd,1 Jicuang Hao, Te-Won Lee, 2,3Robert N. Weinreb,1 Terrence J. Sejnowski, and Michael H. Goldbaum1 PURPOSE. To determine whether topographical

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  • Comparison of machine-learning methodologies for accurate

    Comparison of machine-learning methodologies for accurate

    May 17, 2021 For classifier testing, 15% of the entire meta expression samples are assembled accordingly. (2) The classifiers are constructed in a training process based on information extracted from the combination of supplied samples, and their respective class labels

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