Advances in next-generation sequencing technology are producing large amounts of biological data. In this dissertation, I focus on the integration of AI and network science to analyze genomics and transcriptomics data. Specifically, I answer the following questions: (1) How can we compare a large collection of long genome sequences? (2) How can we use hypergraphs to improve clustering of single-cell RNA sequencing data? (3) Can representation learning on single-cell RNA sequencing data create co-expression networks with a higher signal-to-noise ratio? For the first question, I find that misclassifications from AI models provide insights for comparative genome analysis. In particular, misclassification likelihoods reveal (spatial) associations between genome ensembles. For the second question, I identify inflated signals in co-expression networks due to data sparsity, and show that using hypergraphs and co-expression networks together with a memory mechanism outperforms established methods, especially for weakly modular data. For the third question, I show that different representation learning approaches yield co-expression networks with varying structural properties and gene clustering behaviors, with cross-association analysis revealing method-specific signal-to-noise characteristics. I also investigate a related research question: How can we use hypergraphs to solve constrained optimization problems? I find that for resource allocation problems, optimizing for the algebraic connectivity of the hypergraph leads to robust and resilient solutions.