Work

Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis

Year: 2025

Type: conference-paper

Abstract: We analyze large, multidimensional, 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. Our conditional hierarchical Bayesian Tucker decomposition models are more coherent than the baseline models.

Source: IEEE Conference on Artificial Intelligence (CAI)

DOI: 10.1109/cai64502.2025.00079

Authors: Adam Sandler, Diego Klabjan, Yuan Luo

Institution: Northwestern University

Language: English

Topic: Tensor decomposition and applications

Subfield: Computational Mathematics

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, U.S. National Library of Medicine