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Optimizing Clinico-Genomic Disease Prediction across Ancestries: A Machine Learning Strategy with Pareto Improvement

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TLGP: Transfer Learning for Genomic Prediction

Software Description

Here are the software scripts implementing our machine learning methods for multi-ancestral clinico-genomic prediction of diseases.


Entity Path/location Note


Data ./data/instructions.txt The path of the real and synthetic datasets.

Model ./*.py/build_model The interface used to build generator a deep learning model with Keras.

Mixture ./scripts/*.py/mixture_learning The mixture learning scheme for each study.

Independent ./scripts/*.py/independent_learning The independent learning scheme for each study

Naïve ./scripts/*.py/naive_transfer The Naïve transfer learning Transfer scheme for each study

Transfer ./scripts/*.py/super_transfer The transfer learning scheme for each study

TL_PRS ./script/LR/TL_PRS.py The implementation of the linear transfer method.


System Requirements

Software dependency

The system relies on the following software, reagent, or resources.

Software version

Our software has been tested on the following software version.


Software and SOURCE IDENTIFIER Hardware


REAGENT or RESOURCE SOURCE IDENTIFIER

Python 3.7 Python Software https://www.python.org/download/releases/2.7/ Foundation

Numpy 1.15.4 Tidelift, Inc https://libraries.io/pypi/numpy/1.15.4

Numpydoc 0.9.1 Tidelift, Inc https://libraries.io/pypi/numpydoc

Scipy 1.2.1 The SciPy https://docs.scipy.org/doc/scipy-1.2.1/reference/ community

Sklearn 0.0 The Python https://pypi.org/project/sklearn/ community

Keras 2.2.4 GitHub, Inc. https://github.com/keras-team/keras/releases/tag/2.2.4

Keras-Applications GitHub, Inc. https://github.com/keras-team/keras-applications 1.0.8

Keras-Preprocessing GitHub, Inc. https://github.com/keras-team/keras-preprocessing/releases/tag/1.1.0 1.1.0

Tensorboard 1.13.1 GitHub, Inc. https://github.com/tensorflow/tensorboard/releases/tag/1.13.1

Tensorflow 1.13.1 tensorflow.org https://www.tensorflow.org/install/pip

Tensorflow-estimator The Python https://pypi.org/project/tensorflow-estimator/ 1.13.1 community

Statsmodels 0.9.0 Statsmodels.org https://www.statsmodels.org/stable/release/version0.9.html

Xlrd 1.2.0 The Python https://pypi.org/project/xlrd/ community

XlsxWriter 1.1.8 The Python https://pypi.org/project/XlsxWriter/ community

Xlwings 0.15.8 The Python https://pypi.org/project/xlwings/ community
Xlwt 1.3.0 The Python https://pypi.org/project/xlwt/ community


Hardware requirements

We recommend using a GPU (V100) for optimal software performance.

Installation Guide

This package contains the source code and instructions to reproduce the results represented in our paper. Our software would run on Windows and Ubuntu, but we suggest using Linux system which is easier for environment configuration.

Conda --install requirements.txt

Requirements.txt

numpy==1.15.4

numpydoc==0.9.1

scipy==1.2.1

seaborn==0.9.0

sklearn==0.0

skrebate==0.6

torch==1.7.1

Keras==2.2.4

Keras-Applications==1.0.8

Keras-Preprocessing==1.1.0

tensorboard==1.13.1

tensorflow==1.13.1

tensorflow-estimator==1.13.0

statsmodels==0.9.0

lifelines==0.16.3

Optunity==1.1.1

xlrd==1.2.0-

XlsxWriter==1.1.8

xlwings==0.15.8

xlwt==1.3.0

Authors

Yan Gao and Yan Cui, ({ygao45, ycui2}@uthsc.edu).

License

This project is covered under the GNU General Public License (GPL).

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Optimizing Clinico-Genomic Disease Prediction across Ancestries: A Machine Learning Strategy with Pareto Improvement

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