Package: fuseMLR 0.0.1
fuseMLR: Fusing Machine Learning in R
Recent technological advances have enable the simultaneous collection of multi-omics data i.e., different types or modalities of molecular data, presenting challenges for integrative prediction modeling due to the heterogeneous, high-dimensional nature and possible missing modalities of some individuals. We introduce this package for late integrative prediction modeling, enabling modality-specific variable selection and prediction modeling, followed by the aggregation of the modality-specific predictions to train a final meta-model. This package facilitates conducting late integration predictive modeling in a systematic, structured, and reproducible way.
Authors:
fuseMLR_0.0.1.tar.gz
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fuseMLR_0.0.1.tgz(r-4.4-any)fuseMLR_0.0.1.tgz(r-4.3-any)
fuseMLR_0.0.1.tar.gz(r-4.5-noble)fuseMLR_0.0.1.tar.gz(r-4.4-noble)
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fuseMLR.pdf |fuseMLR.html✨
fuseMLR/json (API)
# Install 'fuseMLR' in R: |
install.packages('fuseMLR', repos = c('https://imbs-hl.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/imbs-hl/fusemlr/issues
- multi_omics - Simulated multiomics data for 70 training participants and 23 testing participants, each with an effect size of 20 on each layer. Each layer includes 50 participants for training and 20 for testing. Participants do not perfectly overlap across layers. The simulation is based on the R package 'interSIM'.
Last updated 12 days agofrom:9869b24595. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 18 2024 |
R-4.5-win | OK | Dec 18 2024 |
R-4.5-linux | OK | Dec 18 2024 |
R-4.4-win | OK | Dec 18 2024 |
R-4.4-mac | OK | Dec 18 2024 |
R-4.3-win | OK | Dec 18 2024 |
R-4.3-mac | OK | Dec 18 2024 |
Exports:bestLayerLearnercobracreateTestingcreateTestLayercreateTrainingcreateTrainLayercreateTrainMetaLayerDataextractDataextractModelfusemlrHashTableLrnerModelPredictDataPredictingPredictLayerPredictMetaLayerTargetTestDataTestingTestLayerTestMetaLayerTrainDataTrainingTrainLayerTrainMetaLayerupsetplotVarSelvarSelectionweightedMeanLearner