B-G.C.22: Content-Based Retrieval for High-Dimensional Biological Data with Multi-Resolution Hierarchical Similarity
Authors
Hasan Oğul
Østfold University of Applied Sciences
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Advances in sequencing technologies provide valuable insights into biology and medicine, such as information about gene expression variation across conditions, between cell types, or over time. The increasing application of these technologies leads to the expansion of the associated databases containing sparse high-dimensional data, which creates a need for effective information retrieval strategies. Content-based search is particularly challenging in such repositories, especially when datasets lack a shared feature alignment.
In this work, we present a workflow for content-based retrieval of high-dimensional biological data. Our framework involves representing each gene expression profile with a hierarchical gene clustering. The similarity between pairs of profiles is computed with the multi-resolution adjusted Rand index - a novel similarity metric incorporating multiple levels of hierarchy into the similarity calculation. We demonstrate our database search workflow for single-cell RNA sequencing data and time-series microarrays. Our results indicate that the framework is flexible, interpretable, and applicable for content-based search across diverse types of high-dimensional data.
Co-authors: Hasan Oğul
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