Package Design¶

This package is capable of handling a variety of input data types:

• Scalar input data

• Functional input data (or densely measured data)

When the input data is scalar data, we adopt the ScalarGE model. This model is designed to characterize G-E interaction effects between high-dimensional (scalar) genetic variables and environmental variables. When the input data is functional data or densely measured observations, we adopt the FuncGE model. This model is utilized to capture G-E interaction effects between functional genetic variables and scalar environmental variables.

To adapt to the multiple output types in clinical analysis, this package supports diverse output requirements:

• Continuous output data

• Binary output data

• Survival output data

By integrating minimax concave penalty (MCP) and L ₂-norm regularization within a neural network estimation framework, GENetLib offers an innovative solution for high-dimensional genetic data analysis.

Framework¶

The framework of GENetLib is shown in the figure below.

Features¶

GENetLib has the following features:

• Comprehensiveness: Supports a variety of input and output formats, enabling the construction of comprehensive neural network models for G-E interaction analysis.

• Flexibility: Offers a multitude of parameters allowing users to build models flexibly according to their specific needs.

• Functional data compatibility: Implements methods for functional data analysis (FDA) in Python, facilitating the processing of functional data with Python.

• Scalability: New methods for G-E interaction analysis via deep learning can be easily integrated into the system.