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

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.