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Updated: Jun 8, 2026

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Adaptive resampling for improved machine learning in imbalanced single-cell datasets.

Zeinab Navidi¹, Akshaya Thoutam², Madeline Hughes³

¹Department of Computer Science, University of Toronto, Toronto, ON, Canada.

Biorxiv : the Preprint Server for Biology

|November 24, 2025

Summary

This summary is machine-generated.

We developed Adaptive Resampling (AR), a novel method to improve machine learning for single-cell transcriptomics. AR enhances model performance by adaptively resampling data during training, leading to better insights from complex biological data.

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Area of Science:

Computational Biology
Genomics
Machine Learning

Background:

Machine learning models for single-cell transcriptomics offer biological insights.
Current tools struggle with underrepresented or out-of-distribution cellular data.
Effective representation learning is crucial for analyzing complex single-cell data.

Purpose of the Study:

To introduce a generalizable Adaptive Resampling (AR) approach for single-cell transcriptomics.
To enhance single-cell representation learning by addressing limitations in current models.
To improve the performance of machine learning models on diverse single-cell data.

Main Methods:

Developed an online, adaptive resampling strategy based on learned latent data structure.
Integrated Adaptive Resampling (AR) concurrently with model training.
Evaluated AR on gene expression reconstruction, cell type classification, and perturbation response prediction tasks.

Main Results:

Adaptive Resampling (AR) significantly improved downstream performance across various tasks and datasets.
The AR training approach enhanced the quality of learned cellular embeddings.
Demonstrated superior performance compared to standard training methods in single-cell transcriptomic analysis.

Conclusions:

Adaptive Resampling (AR) is a valuable technique for improving machine learning models in single-cell transcriptomics.
AR effectively addresses challenges with underrepresented and out-of-distribution cellular data.
The proposed method enhances representation learning and predictive accuracy for biological insights.