Approximate kNN Classification for Biomedical Data

TL;DR

This paper introduces an approximate kNN classification method tailored for high-dimensional biomedical data like scRNA-seq, aiming to reduce computational costs while maintaining prediction accuracy.

Contribution

It proposes a novel approach using approximate nearest neighbor search algorithms specifically designed for high-dimensional biomedical data classification.

Findings

Approximate kNN maintains accuracy with reduced computation.

Method effectively handles ultra-high dimensional scRNA-seq data.

Experimental results confirm the approach's broad applicability.

Abstract

We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution grows. An indicative example is the single-cell RNA-seq (scRNA-seq), an emerging DNA sequencing technology with promising capabilities but significant computational challenges due to the large-scaled generated data. Regarding the classification process for scRNA-seq data, an appropriate method is the k Nearest Neighbor (kNN) classifier since it is usually utilized for large-scale prediction tasks due to its simplicity, minimal parameterization, and model-free nature. However, the ultra-high dimensionality that characterizes scRNA-seq impose a computational bottleneck, while prediction power can be affected by the "Curse of Dimensionality". In this work, we proposed the utilization of approximate nearest neighbor search algorithms for the task of kNN classification in scRNA-seq data focusing on a particular methodology tailored for high dimensional data. We argue that even relaxed approximate solutions will not affect the prediction performance significantly. The experimental results confirm the original assumption by offering the potential for broader applicability.