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Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

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AIGen: an artificial intelligence software for complex genetic data analysis.

Tingting Hou¹, Xiaoxi Shen², Shan Zhang³

¹Department of Experimental Statistics, Louisiana State University, 45 Martin D. Woodin Hall, Baton Rouge, LA 70802, United States.

Briefings in Bioinformatics

|November 16, 2024

Summary

This summary is machine-generated.

We developed AIGen, a C++ package using novel neural networks, to overcome challenges in analyzing large-scale genetic data with artificial intelligence (AI). AIGen efficiently models complex genotype-phenotype relationships for improved accuracy in genetic studies.

Keywords:

MINQUE batch training complex relationships human genome neural networks

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Area of Science:

Genetics
Bioinformatics
Artificial Intelligence

Background:

Deep neural networks (DNNs) have transformed many fields but are underutilized in genetic data analysis due to high dimensionality and computational demands.
Existing methods struggle with the complexity and scale of modern genetic datasets, limiting the application of advanced AI techniques.

Purpose of the Study:

To introduce AIGen, a C++ package designed to integrate artificial intelligence (AI) into genetic data analysis.
To enable the modeling of complex genotype-phenotype relationships, including interactions, using novel neural network architectures.

Main Methods:

Development of AIGen package in C++ utilizing kernel neural networks and functional neural networks.
Implementation of computationally efficient algorithms, including minimum norm quadratic unbiased estimation and batch training, for accelerated analysis.
Application of AIGen to the large-scale UK Biobank dataset.

Main Results:

AIGen demonstrates efficient analysis of large-scale genetic datasets.
The package achieves improved accuracy in modeling genotype-phenotype relationships.
AIGen maintains robust performance even with high-dimensional genetic data and millions of samples.

Conclusions:

AIGen effectively addresses the analytical and computational challenges of using AI in genetic data analysis.
The package facilitates the study of complex genetic architectures and interactions in large populations.
AIGen provides a powerful and efficient tool for large-scale genetic research, accessible via GitHub.