A deep-learning based multi-omics bulk sequencing data integration suite with a focus on (pre-)clinical endpoint prediction. The package includes multiple types of deep learning architectures such as simple fully connected networks, supervised variational autoencoders, graph convolutional networks, multi-triplet networks different options of data layer fusion, and automates feature selection and hyperparameter optimisation. The tools are continuosly benchmarked on publicly available datasets mostly related to the study of cancer. Some of the applications of the methods we develop are drug response modeling in cancer patients or preclinical models (such as cell lines and patient-derived xenografts), cancer subtype prediction, or any other clinically relevant outcome prediction that can be formulated as a regression, classification, survival, or cross-modality prediction problem.
In order to refer to our work, please cite our manuscript currently available at BioRxiv.
- Modeling Breast Cancer Subtypes
- Survival Markers of Lower Grade Gliomas
- Unsupervised Analysis of Bone Marrow Cells
For the latest benchmark results see: https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html
The code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks
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pip install mkdocstrings[python]
mkdocs build --clean
