@NKI-CCB

CCB, NKI

Computation Cancer Biology Group, the Netherlands Cancer Institute

Pinned repositories

  1. DISCOVER

    DISCOVER co-occurrence and mutual exclusivity analysis for cancer genomics data

    Fortran 6 4

  2. MixedIC50

    A non-linear mixed effects model for estimating compound sensitivity

    HTML 1 1

  3. RUBIC

    RUBIC detects recurrent copy number aberrations.

    R 1 5

  4. TANDEM

    A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the …

    R 1

  5. flexgsea-r

    Flexible gene set enrichment analysis

    R 4

  • Notebooks to reproduce the figures from CNR manuscript (Bosdriesz et al., 2018 https://doi.org/10.1101/243709)

    Jupyter Notebook Updated Nov 7, 2018
  • Flexible gene set enrichment analysis

    R 4 Apache-2.0 Updated Oct 22, 2018
  • A method for network reconstruction and quantification from perturbation experiments

    Python Updated Sep 17, 2018
  • Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples.

    R Updated Jun 13, 2018
  • Multivariate probability density approximation R package

    R Updated Jun 7, 2018
  • DISCOVER co-occurrence and mutual exclusivity analysis for cancer genomics data

    Fortran 6 4 Apache-2.0 Updated May 2, 2018
  • A non-linear mixed effects model for estimating compound sensitivity

    HTML 1 1 Updated Apr 10, 2018
  • An R Markdown file containing code to reproduce all figures from our iTOP publication

    Updated Apr 4, 2018
  • A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream featu…

    R 1 Updated Mar 8, 2018
  • The Multitask Variable Importance (Multitask VI) is a modified version of the permuted variable importance score for Random Forests. Essentially, for a Random Forest trained simultaneously for multiple response vectors, it allows the inference of variable importance scores per variable and per task.

    Python Updated Jan 9, 2018
  • Tool for identifying transposon insertions and their effects from RNA-seq data.

    Python 2 MIT Updated Oct 17, 2017
  • Functional Sparse-Factor Analysis

    Python Apache-2.0 Updated Sep 14, 2017
  • RUBIC detects recurrent copy number aberrations.

    R 1 5 Apache-2.0 Updated Apr 24, 2017
  • Common Insertion Site Mapping Platform implemented as an R package for statistical analysis of retroviral insertional mutagenesis screens.

    HTML 1 1 Updated Mar 22, 2017
  • R package for the Detection of Imbalanced Differential Signal (DIDS) algorithm.

    R Updated Feb 3, 2017
  • R code for Kernel Convolved Rule Based Mapping

    R Updated Feb 1, 2017