| Title: | AIGen: an artificial intelligence software for complex genetic data analysis |
| Journal: | Briefings in Bioinformatics |
| Published: | 23 Sep 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39550221/ |
| DOI: | https://doi.org/10.1093/bib/bbae566 |
Publication 13653
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Abstract
The recent development of artificial intelligence (AI) technology, especially the advance of deep neural network (DNN) technology, has revolutionized many fields. While DNN plays a central role in modern AI technology, it has rarely been used in genetic data analysis due to analytical and computational challenges brought by high-dimensional genetic data and an increasing number of samples. To facilitate the use of AI in genetic data analysis, we developed a C++ package, AIGen, based on two newly developed neural networks (i.e. kernel neural networks and functional neural networks) that are capable of modeling complex genotype-phenotype relationships (e.g. interactions) while providing robust performance against high-dimensional genetic data. Moreover, computationally efficient algorithms (e.g. a minimum norm quadratic unbiased estimation approach and batch training) are implemented in the package to accelerate the computation, making them computationally efficient for analyzing large-scale datasets with thousands or even millions of samples. By applying AIGen to the UK Biobank dataset, we demonstrate that it can efficiently analyze large-scale genetic data, attain improved accuracy, and maintain robust performance. Availability: AIGen is developed in C++ and its source code, along with reference libraries, is publicly accessible on GitHub at https://github.com/TingtHou/AIGen.</p>
6 Keywords
- Algorithms
- Artificial Intelligence
- Computational Biology
- Humans
- Neural Networks, Computer
- Software
6 Authors
- Tingting Hou
- Xiaoxi Shen
- Shan Zhang
- Muxuan Liang
- Li Chen
- Qing Lu
Enabling scientific discoveries that improve human health