UMINT-FS: UMINT-guided Feature Selection for multi-omics datasets

Document Type

Conference Article

Publication Title

Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Abstract

Feature selection is a crucial step in single-cell biological data analysis. It involves identifying and selecting a subset of features (genes, proteins, peaks among others) that are most informative and relevant for downstream analysis. A prior investigation has introduced an unsupervised neural network model, known as UMINT, tailored for the integration of single-cell multi-omics data. This novel deep learning model excels at single-cell multi-omics integration and feature extraction, yet lacks the ability to perform feature selection. The present study extends UMINT and introduces UMINT-FS that enables selection of top features from multi-omics datasets by analysing the weights learned by the UMINT network during integration of the omics modalities. UMINT-FS can operate in both supervised and unsupervised learning environments. A supervised learning environment empowers it to find cell-type-specific markers. The performance of UMINT-FS has been evaluated on two different types of single-cell multi-omics datasets and results demonstrated better performance than current state-of-the-art methods.

First Page

594

Last Page

601

DOI

10.1109/BIBM58861.2023.10385731

Publication Date

1-1-2023

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