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  • Heidelberg Retina Tomograph 3 machine learning classifiers
    Heidelberg Retina Tomograph 3 machine learning classifiers

    Aims: To assess performance of classifiers trained on Heidelberg Retina Tomograph 3 (HRT3) parameters for discriminating between healthy and glaucomatous eyes. Methods: Classifiers were trained using HRT3 parameters from 60 healthy subjects and 140 glaucomatous subjects. The classifiers were trained on all 95 variables and smaller sets created

  • Impact of the methylation classifier and ancillary methods
    Impact of the methylation classifier and ancillary methods

    The classifier impacted the diagnosis in 46.5% of these high-confidence classifier score cases, including a substantially new diagnosis in 26.9% cases. Among the 289 cases received with only a descriptive diagnosis, methylation was able to resolve approximately half (144, 49.8%) with high-confidence scores

  • 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

  • DNA methylation-based classification and grading system
    DNA methylation-based classification and grading system

    14 Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany. 15 Genomics and Proteomics Core Facility, Micro-Array Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany. 16 Department of Neuropathology, Otto von Guericke University Magdeburg, Magdeburg, Germany

  • Sarcoma classification by DNA methylation profiling
    Sarcoma classification by DNA methylation profiling

    Jan 21, 2021 In total, 426 independent sarcoma samples were analysed. 75% matched to an established DNA methylation class with a classifier prediction cut-off

  • 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

  • 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

  • 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

  • Ensemble Methods in Machine Learning | SpringerLink
    Ensemble Methods in Machine Learning | SpringerLink

    Dec 01, 2000 Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting

  • DNA methylation-based profiling of bone and soft tissue
    DNA methylation-based profiling of bone and soft tissue

    On limiting the validation to the 820 cases with histological diagnoses for which the DKFZ Classifier was trained, 61% of cases received a prediction, and the histological diagnosis was concordant with the predicted methylation class in 88% of these cases, findings comparable to those reported in the DKFZ Classifier paper

  • Impact of the methylation classifier and ancillary
    Impact of the methylation classifier and ancillary

    DNA methylation profiling and classification using the DKFZ/Heidelberg CNS tumor classifier was performed, as well as unsupervised analyses of methylation data. Ancillary testing, where relevant, was performed

  • Heidelberg Retina Tomograph 3 machine
    Heidelberg Retina Tomograph 3 machine

    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, Department of Ophthalmology, University of Pittsburgh School

  • Classifier comparison — scikit-learn 1.0.2
    Classifier comparison — scikit-learn 1.0.2

    Classifier comparison. . A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets

  • Qlucore and Heidelberg University Hospital partnership
    Qlucore and Heidelberg University Hospital partnership

    Nov 28, 2021 To generate data required for the development of the classifier for NSCLC and clinical decision support functionality, in WP2 Heidelberg University Hospital will generate RNA-seq data from biopsies from NSCLC patients