Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866908508803301376 |
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| author | Sandler, Adam Klabjan, Diego Luo, Yuan |
| author_facet | Sandler, Adam Klabjan, Diego Luo, Yuan |
| contents | We analyze large, multi-dimensional, sparse counting data sets, finding unsupervised groups to provide unique insights into genetic data. We create gene and biological pathway groups based on patients' variants to find common risk factors for four common types of cancer (breast, lung, prostate, and colorectal) and autism spectrum disorder. To accomplish this, we extend latent Dirichlet allocation to multiple dimensions and design distinct methods for hierarchical topic modeling. We find that our conditional hierarchical Bayesian Tucker decomposition models are more coherent than baseline models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1911_12426 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis Sandler, Adam Klabjan, Diego Luo, Yuan Machine Learning Methodology We analyze large, multi-dimensional, sparse counting data sets, finding unsupervised groups to provide unique insights into genetic data. We create gene and biological pathway groups based on patients' variants to find common risk factors for four common types of cancer (breast, lung, prostate, and colorectal) and autism spectrum disorder. To accomplish this, we extend latent Dirichlet allocation to multiple dimensions and design distinct methods for hierarchical topic modeling. We find that our conditional hierarchical Bayesian Tucker decomposition models are more coherent than baseline models. |
| title | Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/1911.12426 |