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Generating Labeled Low-heterogeneity Transcriptomes Using Crispra And Crispri Can Improve Phenotype Prediction By Deep Learning.

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Generating Labeled Low-heterogeneity Transcriptomes Using Crispra And Crispri Can Improve Phenotype Prediction By Deep Learning.

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Generating Labeled Low-Heterogeneity Transcriptomes Using CRISPRa and CRISPRi Can Improve Phenotype Prediction by

Ilya Velegzhaninov^1,2, Dmitry Kazakov³, Elena Rasova¹

¹Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences, 28b Kommunisticheskaya st., Syktyvkar 167000, Russia.

ACS Synthetic Biology

|May 25, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study addresses challenges in using omics data for deep learning (DL) models to predict complex traits like cancer treatment resistance. It proposes a framework for creating better datasets to improve DL-based phenotype prediction.

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Area of Science:

Biology
Bioinformatics
Genomics

Background:

Deep learning (DL) shows promise in biology, biomedicine, and agriculture.
Predicting complex phenotypic traits, such as cancer treatment resistance, from omics data is a major challenge.
Existing omics datasets are often insufficient in volume and too heterogeneous for effective DL-based phenotype prediction without dimensionality reduction.

Purpose of the Study:

To discuss deficiencies in current omics datasets for phenotype prediction.
To propose a framework for generating phenotypically labeled, low-heterogeneity datasets.
To supplement existing data for improved DL model training.

Main Methods:

Formulating experimental considerations for obtaining phenotypically labeled cell lines.

Utilizing CRISPR activation (CRISPRa) and CRISPR interference (CRISPRi) technologies.

Creating cell lines with small incremental differences in transcriptomes.

Main Results:

A framework for creating supplementary, phenotypically labeled, low-heterogeneity omics datasets is proposed.
Specific experimental guidelines for generating uniform genetic information sources are outlined.
The developed cell lines and datasets are intended for enhancing DL-based phenotype prediction models.

Conclusions:

Addressing data limitations is crucial for advancing DL in biological and biomedical applications.
The proposed framework and methods offer a pathway to more robust DL models for phenotype prediction.
This work facilitates the development of DL models for predicting complex traits like cancer resistance.