Discovering distinctive elements of biomedical datasets for high-performance exploration

TL;DR

This paper introduces DEA, an unsupervised deep learning method for extracting distinctive elements from high-dimensional biomedical datasets, significantly improving accuracy and interpretability in various biomedical applications.

Contribution

The paper presents DEA, a novel kernel-driven triple-optimization network that reliably identifies distinctive data elements in high-dimensional biomedical datasets, enhancing accuracy and interpretability.

Findings

Improves disease detection accuracy by up to 45%.

Effective in gene ranking and cell recognition tasks.

Offers better interpretability through user-guided manipulation.

Abstract

The human brain represents an object by small elements and distinguishes two objects based on the difference in elements. Discovering the distinctive elements of high-dimensional datasets is therefore critical in numerous perception-driven biomedical and clinical studies. However, currently there is no available method for reliable extraction of distinctive elements of high-dimensional biomedical and clinical datasets. Here we present an unsupervised deep learning technique namely distinctive element analysis (DEA), which extracts the distinctive data elements using high-dimensional correlative information of the datasets. DEA at first computes a large number of distinctive parts of the data, then filters and condenses the parts into DEA elements by employing a unique kernel-driven triple-optimization network. DEA has been found to improve the accuracy by up to 45% in comparison to the traditional techniques in applications such as disease detection from medical images, gene ranking and cell recognition from single cell RNA sequence (scRNA-seq) datasets. Moreover, DEA allows user-guided manipulation of the intermediate calculation process and thus offers intermediate results with better interpretability.