| Name | flexynesis JSON |
| Version |
1.0.7
JSON |
| download |
| home_page | None |
| Summary | A deep-learning based multi-omics bulk sequencing data integration suite with a focus on (pre-)clinical endpoint prediction. |
| upload_time | 2025-09-02 18:25:08 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | Modified MIT License for Academic and Non-Commercial Use Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software for academic, research, and educational purposes without restriction, including without limitation the rights to use, copy, modify, merge, publish, and distribute copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: Commercial use of this software or any derivative works is prohibited without explicit permission and a separate commercial license from the copyright holders. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
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<p align="center">
<img alt="logo" src="https://github.com/BIMSBbioinfo/flexynesis/raw/main/img/logo.png" width="40%">
</p>
[](https://pepy.tech/project/flexynesis)
)
# flexynesis
Flexynesis: a flexible deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction.
Flexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures — from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models — with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.
Built with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.
The framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.
<p align="center">
<img alt="workflow" src="https://github.com/BIMSBbioinfo/flexynesis/raw/main/img/graphical_abstract.jpg">
</p>
# Citing our work
In order to refer to our work, please cite our manuscript currently available at [BioRxiv](https://biorxiv.org/cgi/content/short/2024.07.16.603606v1).
# Getting started with Flexynesis
## Command-line tutorial
- [Getting Started with Flexynesis](https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis/site/getting_started/)
## Jupyter notebooks for interactive usage
- [Modeling Breast Cancer Subtypes](https://github.com/BIMSBbioinfo/flexynesis/blob/main/examples/tutorials/brca_subtypes.ipynb)
- [Survival Markers of Lower Grade Gliomas](https://github.com/BIMSBbioinfo/flexynesis/blob/main/examples/tutorials/survival_subtypes_LGG_GBM.ipynb)
- [Unsupervised Analysis of Bone Marrow Cells](https://github.com/BIMSBbioinfo/flexynesis/blob/main/examples/tutorials/unsupervised_analysis_single_cell.ipynb)
# Benchmarks
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
# Documentation
Documentation generated using [mkdocs](https://mkdocstrings.github.io/)
```
pip install mkdocstrings[python]
mkdocs build --clean
```
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"author_email": "Bora Uyar <bora.uyar@mdc-berlin.de>, Taras Savchyn <Taras.Savchyn@mdc-berlin.de>, Ricardo Wurmus <Ricardo.Wurmus@mdc-berlin.de>, Ahmet Sarigun <Ahmet.Sariguen@mdc-berlin.de>",
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"description": "\n<p align=\"center\">\n <img alt=\"logo\" src=\"https://github.com/BIMSBbioinfo/flexynesis/raw/main/img/logo.png\" width=\"40%\">\n</p>\n\n[](https://pepy.tech/project/flexynesis)\n\n\n\n)\n\n# flexynesis\n\nFlexynesis: a flexible deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction.\n\nFlexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures \u2014 from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models \u2014 with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.\n\nBuilt with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.\n\nThe framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.\n\n<p align=\"center\">\n <img alt=\"workflow\" src=\"https://github.com/BIMSBbioinfo/flexynesis/raw/main/img/graphical_abstract.jpg\">\n</p>\n\n# Citing our work\n\nIn order to refer to our work, please cite our manuscript currently available at [BioRxiv](https://biorxiv.org/cgi/content/short/2024.07.16.603606v1). \n\n# Getting started with Flexynesis\n\n## Command-line tutorial\n\n- [Getting Started with Flexynesis](https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis/site/getting_started/)\n\n## Jupyter notebooks for interactive usage\n\n- [Modeling Breast Cancer Subtypes](https://github.com/BIMSBbioinfo/flexynesis/blob/main/examples/tutorials/brca_subtypes.ipynb)\n- [Survival Markers of Lower Grade Gliomas](https://github.com/BIMSBbioinfo/flexynesis/blob/main/examples/tutorials/survival_subtypes_LGG_GBM.ipynb)\n- [Unsupervised Analysis of Bone Marrow Cells](https://github.com/BIMSBbioinfo/flexynesis/blob/main/examples/tutorials/unsupervised_analysis_single_cell.ipynb)\n\n\n# Benchmarks\n\nFor the latest benchmark results see: \nhttps://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html\n\nThe code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks\n\n\n# Documentation\n\nDocumentation generated using [mkdocs](https://mkdocstrings.github.io/) \n\n```\npip install mkdocstrings[python]\nmkdocs build --clean\n```\n\n\n\n",
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