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dlomix-0.0.4


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توضیحات

Deep Learning for Proteomics
ویژگی مقدار
سیستم عامل -
نام فایل dlomix-0.0.4
نام dlomix
نسخه کتابخانه 0.0.4
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Omar Shouman
ایمیل نویسنده o.shouman@tum.de
آدرس صفحه اصلی https://github.com/wilhelm-lab/dlomix
آدرس اینترنتی https://pypi.org/project/dlomix/
مجوز -
# DLOmix [![Docs](https://readthedocs.org/projects/docs/badge/?version=latest)](https://dlomix.readthedocs.io/en/latest/?badge=latest) [![Build](https://github.com/wilhelm-lab/dlomix/actions/workflows/build.yaml/badge.svg)](https://github.com/wilhelm-lab/dlomix/actions/workflows/build.yaml) [![PyPI](https://github.com/wilhelm-lab/dlomix/actions/workflows/pypi.yaml/badge.svg)](https://github.com/wilhelm-lab/dlomix/actions/workflows/pypi.yaml) **DLOmix** is a python framework for Deep Learning in Proteomics. Initially built ontop of TensorFlow/Keras, support for PyTorch can however be integrated once the main API is established. ## Usage Experiment a simple retention time prediction use-case using Google Colab    [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/wilhelm-lab/dlomix/blob/develop/notebooks/Example_RTModel_Walkthrough_colab.ipynb) A version that includes experiment tracking with [Weights and Biases](https://www.wandb.ai) is available here    [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/wilhelm-lab/dlomix/blob/develop/notebooks/Example_RTModel_Walkthrough_colab-weights-and-biases.ipynb) **Resources Repository** More learning resources can be found in the [dlomix-resources](https://github.com/wilhelm-lab/dlomix-resources) repository. ## Installation Run the following to install: ```bash $ pip install dlomix ``` **General Overview** - `data`: structures for modelling the input data, currently based on `tf.Dataset` - `eval`: classes for evaluating models and reporting results - `layers`: custom layers used for building models, based on `tf.keras.layers.Layer` - `losses`: custom losses to be use for training with `model.fit()` - `models`: common model architectures for the relevant use-cases based on `tf.keras.Model` to allow for using the Keras training API - `pipelines`: an exemplary high-level pipeline implementation - `reports`: classes for generating reports related to the different tasks - `constants.py`: constants and configuration values - `utils.py`: utility functions **Use-cases** - Retention Time Prediction: - a regression problem where the the retention time of a peptide sequence is to be predicted. **To-Do** Functionality: - [X] integrate prosit - [ ] extend pipeline for different types of models and backbones - [ ] extend pipeline to allow for fine-tuning with custom datasets - [X] add residual plots to reporting, possibly other regression analysis tools - [X] output reporting results as PDF - [ ] extend data representation to include modifications Package structure: - [X] integrate `deeplc.py` into `models.py`, preferably introduce a package structure (e.g. `models.retention_time`) - [X] add references for implemented models in the ReadMe - [ ] introduce a style guide and checking (e.g. PEP) - [X] plan documentation (sphinx and readthedocs) ## Developing DLOmix To install dlomix, along with the the tools needed to develop and run tests, run the following command in your virtualenv: ```bash $ pip install -e .[dev] ``` **References:** [**Prosit**] [1] Gessulat, S., Schmidt, T., Zolg, D. P., Samaras, P., Schnatbaum, K., Zerweck, J., ... & Wilhelm, M. (2019). Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nature methods, 16(6), 509-518. [**DeepLC**] [2] DeepLC can predict retention times for peptides that carry as-yet unseen modifications Robbin Bouwmeester, Ralf Gabriels, Niels Hulstaert, Lennart Martens, Sven Degroeve bioRxiv 2020.03.28.013003; doi: 10.1101/2020.03.28.013003 [3] Bouwmeester, R., Gabriels, R., Hulstaert, N. et al. DeepLC can predict retention times for peptides that carry as-yet unseen modifications. Nat Methods 18, 1363–1369 (2021). https://doi.org/10.1038/s41592-021-01301-5


نیازمندی

مقدار نام
- fpdf
- pandas
- numpy
- matplotlib
- scikit-learn
- tensorflow
- pyarrow
- seaborn
>=3.7 pytest
- pytest-cov
- black
- twine
- setuptools
- wheel
- pylint


نحوه نصب


نصب پکیج whl dlomix-0.0.4:

    pip install dlomix-0.0.4.whl


نصب پکیج tar.gz dlomix-0.0.4:

    pip install dlomix-0.0.4.tar.gz